A company has a producer application that collects device log data. The producer application writes to an Amazon Kinesis Data Firehose delivery stream that delivers data to an Amazon S3 bucket. The company needs to build a series of dashboards to display real-time trends of the metrics in the log data. Which solution will meet these requirements?
-
A
Update the Kinesis Data Firehose delivery stream to add an Amazon OpenSearch Service (Amazon Elasticsearch Service) cluster as another destination. Use OpenSearch Dashboards (Kibana) for log data visualization.
-
B
Update the Kinesis Data Firehose delivery stream to add an Amazon Kinesis Data Analytics application as an additional destination. Use Amazon QuickSight to display the output of the Kinesis Data Analytics application.
-
C
Create another Kinesis Data Firehose delivery stream. Update the producer application to write a copy of the log data into the new delivery stream. Set the new delivery stream to deliver data into an Amazon QuickSight dashboard.
-
D
Update the producer application to write the log data to an Amazon Kinesis data stream. Deliver this data stream to the original Kinesis Data Firehose delivery stream and a new Kinesis Data Firehose delivery stream. Set the new delivery stream to deliver data into an Amazon OpenSearch Service (Amazon Elasticsearch Service) cluster. Use OpenSearch Dashboards (Kibana) for log data visualization.
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Correct answerB
ExplanationReferences:
https://docs.aws.amazon.com/firehose/latest/dev/what-is-this-service.html
An ecommerce company stores customer purchase data in Amazon RDS. The company wants a solution to store and analyze historical data. The most recent 6 months of data will be queried frequently for analytics workloads. This data is several terabytes large. Once a month, historical data for the last 5 years must be accessible and will be joined with the more recent data. The company wants to optimize performance and cost. Which storage solution will meet these requirements?
-
A
Create a read replica of the RDS database to store the most recent 6 months of data. Copy the historical data into Amazon S3. Create an AWS Glue Data Catalog of the data in Amazon S3 and Amazon RDS. Run historical queries using Amazon Athena.
-
B
Use an ETL tool to incrementally load the most recent 6 months of data into an Amazon Redshift cluster. Run more frequent queries against this cluster. Create a read replica of the RDS database to run queries on the historical data.
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C
Incrementally copy data from Amazon RDS to Amazon S3. Create an AWS Glue Data Catalog of the data in Amazon S3. Use Amazon Athena to query the data.
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D
Incrementally copy data from Amazon RDS to Amazon S3. Load and store the most recent 6 months of data in Amazon Redshift. Configure an Amazon Redshift Spectrum table to connect to all historical data.
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Correct answerD
ExplanationThe cost-effective way to query across S3 and Amazon RDS is using Amazon redshift spectrum. References:
https://docs.aws.amazon.com/redshift/latest/dg/c-using-spectrum.html
https://www.upsolver.com/blog/aws-athena-pricing-redshift-comparison
A global company has different sub-organizations, and each sub-organization sells its products and services in various countries. The company's senior leadership wants to quickly identify which sub-organization is the strongest performer in each country. All sales data is stored in Amazon S3 in Parquet format. Which approach can provide the visuals that senior leadership requested with the least amount of effort?
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A
Use Amazon QuickSight with Amazon Athena as the data source. Use heat maps as the visual type.
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B
Use Amazon QuickSight with Amazon S3 as the data source. Use heat maps as the visual type.
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C
Use Amazon QuickSight with Amazon Athena as the data source. Use pivot tables as the visual type.
-
D
Use Amazon QuickSight with Amazon S3 as the data source. Use pivot tables as the visual type.
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Correct answerA
ExplanationQuickSight does not support S3 files with parquet format, Athena does it. For visualization is better a graph than a pivot table.
https://docs.aws.amazon.com/athena/latest/ug/when-should-i-use-ate.html
https://docs.aws.amazon.com/quicksight/latest/user/supported-data-sources.html
A transport company wants to track vehicular movements by capturing geolocation records. The records are 10 B in size and up to 10,000 records are captured each second. Data transmission delays of a few minutes are acceptable, considering unreliable network conditions. The transport company decided to use Amazon Kinesis Data Streams to ingest the data. The company is looking for a reliable mechanism to send data to Kinesis Data Streams while maximizing the throughput efficiency of the Kinesis shards. Which solution will meet the company's requirements?
-
A
-
B
Kinesis Producer Library (KPL)
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C
-
D
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Correct answerB
ExplanationCorrect answer is B as Kinesis Producer Library (KPL) is a wrapper over Kinesis Agent provides the ability to buffer, batch and retry functionality and helps achieve high write throughput. The Amazon Kinesis Producer Library (KPL) simplifies the producer application development by enabling developers to achieve high write throughput to one or more Kinesis data streams. The KPL is an easy to use, highly configurable library that you install on your hosts. It acts as an intermediary between your producer application code and the Kinesis Streams API actions. The KPL can help build high-performance producers. Consider a situation where your Amazon EC2 instances serve as a proxy for collecting 100-byte events from hundreds or thousands of low power devices and writing records into a Kinesis data stream. These EC2 instances must each write thousands of events per second to your data stream. To achieve the throughput needed, producers must implement complicated logic, such as batching or multithreading, in addition to retry logic and record de-aggregation at the consumer side. The KPL performs all of these tasks for you.
A banking company wants to collect large volumes of transactional data using Amazon Kinesis Data Streams for real-time analytics. The company uses PutRecord to send data to Amazon Kinesis, and has observed network outages during certain times of the day. The company wants to obtain exactly once semantics for the entire processing pipeline. What should the company do to obtain these characteristics?
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A
Design the application so it can remove duplicates during processing be embedding a unique ID in each record.
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B
Rely on the processing semantics of Amazon Kinesis Data Analytics to avoid duplicate processing of events.
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C
Design the data producer so events are not ingested into Kinesis Data Streams multiple times.
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D
Rely on the exactly one processing semantics of Apache Flink and Apache Spark Streaming included in Amazon EMR.
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Correct answerA
ExplanationCorrect answer is A as producer retries can result in duplicates in Kinesis Data Streams and must be handled by the producer by using a unique key for each message. There are two primary reasons why records may be delivered more than one time to your Amazon Kinesis Data Streams application: producer retries and consumer retries. Your application must anticipate and appropriately handle processing individual records multiple times. Options B & C are wrong as they would not handle the exactly-once processing semantics. Option D is wrong as although Apache Flink and Spark Streaming would work, it would need a complete change in the current application.
A company wants to run analytics on its Elastic Load Balancing logs stored in Amazon S3. A data analyst needs to be able to query all data from a desired year, month, or day. The data analyst should also be able to query a subset of the columns. The company requires minimal operational overhead and the most cost-effective solution. Which approach meets these requirements for optimizing and querying the log data?
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A
Use an AWS Glue job nightly to transform new log files into .csv format and partition by year, month, and day. Use AWS Glue crawlers to detect new partitions. Use Amazon Athena to query data.
-
B
Launch a long-running Amazon EMR cluster that continuously transforms new log files from Amazon
S3 into its Hadoop Distributed File System (HDFS) storage and partitions by year, month, and day. Use Apache Presto to query the optimized format.
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C
Launch a transient Amazon EMR cluster nightly to transform new log files into Apache ORC format and partition by year, month, and day. Use Amazon Redshift Spectrum to query the data.
-
D
Use an AWS Glue job nightly to transform new log files into Apache Parquet format and partition by year, month, and day. Use AWS Glue crawlers to detect new partitions. Use Amazon Athena to query data.
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Correct answerD
ExplanationA - .csv format is not optimal
B - long running EMR is not cost-effective and has operational over-head of cluster management C - Again EMR with running Redshift Cluster is not cost-effective
So, Answer is Option D - low-cost and no operational over-head. Data Scanning cost by Athena can be minimized by partition pruning and subset of columns from parquet file.
A human resources company maintains a 10-node Amazon Redshift cluster to run analytics queries on the company's data. The Amazon Redshift cluster contains a product table and a transactions table, and both tables have a product_sku column. The tables are over 100 GB in size. The majority of queries run on both tables. Which distribution style should the company use for the two tables to achieve optimal query performance?
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A
An EVEN distribution style for both tables
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B
A KEY distribution style for both tables
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C
An ALL distribution style for the product table and an EVEN distribution style for the transactions table
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D
An EVEN distribution style for the product table and an KEY distribution style for the transactions table
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Correct answerB
ExplanationB - KEY - both tables are huge and have common key . ALL-distribution style for the product table not correct because of size EVEN distribution style for the product table - may not necessarily help.
Question 8
Multiple choice
An airline has been collecting metrics on flight activities for analytics. A recently completed proof of concept demonstrates how the company provides insights to data analysts to improve on-time departures.
The proof of concept used objects in Amazon S3, which contained the metrics in .csv format, and used Amazon Athena for querying the data. As the amount of data increases, the data analyst wants to optimize the storage solution to improve query performance. Which options should the data analyst use to improve performance as the data lake grows? (Choose three.)
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A
Add a randomized string to the beginning of the keys in S3 to get more throughput across partitions.
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B
Use an S3 bucket in the same account as Athena.
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C
Compress the objects to reduce the data transfer I/O.
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D
Use an S3 bucket in the same Region as Athena.
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E
Preprocess the .csv data to JSON to reduce I/O by fetching only the document keys needed by the query.
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F
Preprocess the .csv data to Apache Parquet to reduce I/O by fetching only the data blocks needed for predicates.
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Correct answersC, D, F
ExplanationCorrect answers are C, D & F Options C & F as using compression and columnar data format helps improve query performance and optimize storage Option D as using Athena and S3 within the same region would help with query performance and cost. Option A is wrong as S3 scales automatically now and is not bounded by the restriction. Option B is wrong as using the same account does not help in optimizing the cost of query performance. Option E is wrong as using JSON is the same as using CSV files and does help in n optimizing the cost or query performance.
A large company has several independent business units. Each business unit is responsible for its own data, but needs to share data with other units for collaboration. Each unit stores data in an Amazon S3 data lake created with AWS Lake Formation. To create dashboard reports, the marketing team wants to join its data stored in an Amazon Redshift cluster with the sales team customer table stored in the data lake. The sales team has a large number of tables and schemas, but the marketing team should only have access to the customer table. The solution must be secure and scalable. Which set of actions meets these requirements?
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A
The sales team shares the AWS Glue Data Catalog customer table with the marketing team in read- only mode using the named resource method. The marketing team accepts the datashare using AWS Resource Access Manager (AWS RAM) and creates a resource link to the shared customer table. The marketing team joins its data with the customer table using Amazon Redshift Spectrum.
-
B
The marketing team creates an S3 cross-account replication between the sales team's S3 bucket as the source and the marketing team's S3 bucket as the destination. The marketing team runs an AWS Glue crawler on the replicated data in its AWS account to create an AWS Glue Data Catalog customer table. The marketing team joins its data with the customer table using Amazon Redshift Spectrum.
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C
The marketing team creates an AWS Lambda function in the sales team's account to replicate data between the sale team's S3 bucket as the source and the marketing team's S3 bucket as the destination. The marketing team runs an AWS Glue crawler on the replicated data in its AWS account to create an AWS Glue Data Catalog customer table. The marketing team joins its data with the
customer table using Amazon Redshift Spectrum.
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D
The sales team shares the AWS Glue Data Catalog customer table with the marketing team in read-only mode using the Lake Formation tag-based access control (LF-TBAC) method. The sales team updates the AWS Glue Data Catalog resource policy to add relevant permissions for the marketing team. The marketing team creates a resource link to the shared customer table. The marketing team joins its data with the customer table using Amazon Redshift Spectrum.
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Question 10
Single choice
A telecommunications company is looking for an anomaly-detection solution to identify fraudulent calls.
The company currently uses Amazon Kinesis to stream voice call records in a JSON format from its on-premises database to Amazon S3. The existing dataset contains voice call records with 200 columns. To detect fraudulent calls, the solution would need to look at 5 of these columns only. The company is interested in a cost-effective solution using AWS that requires minimal effort and experience in anomaly-detection algorithms. Which solution meets these requirements?
-
A
Use an AWS Glue job to transform the data from JSON to Apache Parquet. Use AWS Glue crawlers to discover the schema and build the AWS Glue Data Catalog. Use Amazon Athena to create a table with a subset of columns. Use Amazon QuickSight to visualize the data and then use Amazon QuickSight
machine learning-powered anomaly detection.
-
B
Use Kinesis Data Firehose to detect anomalies on a data stream from Kinesis by running SQL queries, which compute an anomaly score for all calls and store the output in Amazon RDS. Use Amazon Athena to build a dataset and Amazon QuickSight to visualize the results.
-
C
Use an AWS Glue job to transform the data from JSON to Apache Parquet. Use AWS Glue crawlers to discover the schema and build the AWS Glue Data Catalog. Use Amazon SageMaker to build an anomaly detection model that can detect fraudulent calls by ingesting data from Amazon S3.
-
D
Use Kinesis Data Analytics to detect anomalies on a data stream from Kinesis by running SQL queries, which compute an anomaly score for all calls. Connect Amazon QuickSight to Kinesis Data Analytics to visualize the anomaly scores.
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Correct answerA
ExplanationCorrect answer is A as only limited columns are required it would be best to convert the data in columnar format and expose it through Athena to QuickSight for anomaly detection. Using parquet with Athena helps provide a cost-effective solution using QuickSight requires minimal effort and experience in anomaly-detection algorithms. Link :
https://docs.aws.amazon.com/quicksight/latest/user/anomaly-detection-function.html Option B is wrong as Kinesis Data Firehose does not provide analytic functions and cannot be used for anamoly detection Option C is wrong as using SageMaker would require more effort. Option D is wrong as Kinesis Data Analytics does not integrate with QuickSight directly.
Question 11
Single choice
An online advertising company wants to perform sentiment analysis of social media data to measure the success of online advertisements. The company wants to implement an end-to-end streaming solution to continuously ingest data from various social networks, clean and transform the streaming data in near-real time, and make the data available for analytics and visualization with Amazon QuickSight. The company wants a solution that is easy to implement and manage so it can design better analytics solutions instead of provisioning and maintaining infrastructure. Which solution meets these requirements with the LEAST amount of operational effort?
-
A
Use Amazon Kinesis Data Firehose to ingest the data. Author an AWS Glue streaming ETL job to transform the ingested data. Load the transformed data into an Amazon Redshift table.
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B
Use Apache Kafka running on Amazon EC2 instances to ingest the data. Create an Amazon EMR Spark job to transform the ingested data. Use the COPY command to load the transformed data into an Amazon Redshift table.
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C
Use Amazon Managed Streaming for Apache Kafka (Amazon MSK) to ingest the data. Create an Amazon EMR Spark job to transform the ingested data. Use the COPY command to load the transformed data into an Amazon Redshift table.
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D
Use Amazon Kinesis Data Streams to ingest the data. Author an AWS Glue streaming ETL job to transform the ingested data. Load the transformed data into an Amazon Redshift table.
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Question 12
Single choice
A company stores revenue data in Amazon Redshift. A data analyst needs to create a dashboard so that the company's sales team can visualize historical revenue and accurately forecast revenue for the upcoming months. Which solution will MOST cost-effectively meet these requirements?
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A
Create an Amazon QuickSight analysis by using the data in Amazon Redshift. Add a custom field in QuickSight that applies a linear regression function to the data. Publish the analysis as a dashboard.
-
B
Create a JavaScript dashboard by using D3.js charts and the data in Amazon Redshift. Export the data to Amazon SageMaker. Run a Python script to run a regression model to forecast revenue. Import the data back into Amazon Redshift. Add the new forecast information to the dashboard.
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C
Create an Amazon QuickSight analysis by using the data in Amazon Redshift. Add a forecasting widget Publish the analysis as a dashboard.
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D
Create an Amazon SageMaker model for forecasting. Integrate the model with an Amazon QuickSight dataset. Create a widget for the dataset. Publish the analysis as a dashboard.
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Correct answerC
ExplanationYou can add a forecasting widget to your existing analysis, and publish it as a dashboard. References:
https://docs.aws.amazon.com/quicksight/latest/user/forecasts-and-whatifs.html
Question 13
Single choice
A financial company hosts a data lake in Amazon S3 and a data warehouse on an Amazon Redshift cluster. The company uses Amazon QuickSight to build dashboards and wants to secure access from its on-premises Active Directory to Amazon QuickSight. How should the data be secured?
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A
Use an Active Directory connector and single sign-on (SSO) in a corporate network environment.
-
B
Use a VPC endpoint to connect to Amazon S3 from Amazon QuickSight and an IAM role to authenticate Amazon Redshift.
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C
Establish a secure connection by creating an S3 endpoint to connect Amazon QuickSight and a VPC endpoint to connect to Amazon Redshift.
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D
Place Amazon QuickSight and Amazon Redshift in the security group and use an Amazon S3 endpoint to connect Amazon QuickSight to Amazon S3.
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Question 14
Single choice
A company needs a solution to control data access for the company's Amazon S3 data lake. The company expects the number of data sources in the data lake and the number of users that access the data to increase rapidly. All the data in the data lake is cataloged in an AWS Glue Data Catalog. Users access the data by using Amazon Athena and Amazon QuickSight. A data analytics specialist must implement a solution that controls which users can ingest new data into the data lake. The solution also must restrict access to data at the column level and must provide audit capabilities. Which solution will meet these requirements?
-
A
Use IAM resource-based policies to allow access to required S3 prefixes only. Use AWS CloudTrail for audit logs.
-
B
Use AWS Lake Formation access controls for the data in the data lake. Use AWS CloudTrail for audit logs.
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C
Use IAM identity-based policies to allow access to authorized users only. Use Amazon CloudWatch for audit logs.
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D
Use Athena federated queries to access the data in the data lake. Use S3 server access logs for audit logs.
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Question 15
Single choice
A company is designing a support ticketing system for its employees. The company has a flattened LDAP dataset that contains employee data. The data includes ticket categories that the employees can access, relevant ticket metadata stored in Amazon S3, and the business unit of each employee. The company uses Amazon QuickSight to visualize the data. The company needs an automated solution to apply row-level data restriction within the QuickSight group for each business unit. The solution must grant access to an employee when an employee is added to a business unit and must deny access to an employee when an employee is removed from a business unit. Which solution will meet these requirements?
-
A
Load the dataset into SPICE from Amazon S3. Create a SPICE query that contains the dataset rules for row-level security. Upload separate .csv files to Amazon S3 for adding and removing users from a group. Apply the permissions dataset on the existing QuickSight users. Create an AWS Lambda function that will run periodically to refresh the direct query cache based on the changes to the .csv file.
-
B
Load the dataset into SPICE from Amazon S3. Create an AWS Lambda function that will run each time the direct query cache is refreshed. Configure the Lambda function to apply a permissions file to the dataset that is loaded into SPICE. Configure the addition and removal of groups and users by creating a QuickSight IAM policy.
-
C
Load the dataset into SPICE from Amazon S3. Apply a permissions file to the dataset to dictate which group has access to the dataset. Upload separate .csv files to Amazon S3 for adding and removing groups and users under the path that QuickSight is reading from. Create an AWS Lambda function that will run when a particular object is uploaded to Amazon S3. Configure the Lambda function to make API calls to QuickSight to add or remove users or a group.
-
D
Move the data from Amazon S3 into Amazon Redshift. Load the dataset into SPICE from Amazon Redshift. Create an AWS Lambda function that will run each time the direct query cache is refreshed.
Configure the Lambda function to apply a permissions file to the dataset that is loaded into SPICE.
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Question 16
Single choice
A company is using an AWS Lambda function to run Amazon Athena queries against a cross-account AWS Glue Data Catalog. A query returns the following error: HIVE_METASTORE_ERROR The error message states that the response payload size exceeds the maximum allowed size. The queried table is already partitioned, and the data is stored in an Amazon S3 bucket in the Apache Hive partition format. Which solution will resolve this error?
-
A
Modify the Lambda function to upload the query response payload as an object into the S3 bucket.
Include an S3 object presigned URL as the payload in the Lambda function response.
-
B
Run the MSCK REPAIR TABLE command on the queried table.
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C
Create a separate folder in the S3 bucket. Move the data files that need to be queried into that folder.
Create an AWS Glue crawler that points to the folder instead of the S3 bucket.
-
D
Check the schema of the queried table for any characters that Athena does not support. Replace any unsupported characters with characters that Athena supports.
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Correct answerA
Explanationhttps://aws.amazon.com/premiumsupport/knowledge-center/athena-hive-metastore-error/
Question 17
Single choice
A company's data analytics specialist must build a solution to implement quality checks on a dataset before the company uses the data in a sales report. The dataset is stored in an Amazon S3 bucket and is in CSV format. The data quality checks must include identification of duplicate rows, removal of duplicate rows, and validation of date formats. The solution must run daily and must produce output data in Apache Parquet format in Amazon S3. Which solution will meet these requirements with the LEAST development effort?
-
A
Create an AWS Glue ETL job that includes transformation steps to implement data quality checks.
Configure the job to write to Amazon S3. Create a schedule-based job within an AWS Glue workflow to run the job daily.
-
B
Create an AWS Glue DataBrew job that includes data quality recipe steps to implement data quality checks. Configure the job to write to Amazon S3. Create a schedule within the DataBrew job to run the job daily.
-
C
Create an Amazon EMR cluster. Use an Apache Spark ETL job that includes data processing steps to implement data quality checks. Configure the job to write to Amazon S3. Create an Apache Oozie workflow to run the job daily.
-
D
Create an AWS Lambda function. Use custom code to implement data quality checks and to write to Amazon S3. Create an Amazon EventBridge rule to run the Lambda function daily.
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Question 18
Single choice
A company uses Amazon Redshift for its data warehouse. The company is running an ETL process that receives data in data parts from five third-party providers. The data parts contain independent records that are related to one specific job.
The company receives the data parts at various times throughout each day. A data analytics specialist must implement a solution that loads the data into Amazon Redshift only after the company receives all five data parts. Which solution will meet these requirements?
-
A
Create an Amazon S3 bucket to receive the data. Use S3 multipart upload to collect the data from the different sources and to form a single object before loading the data into Amazon Redshift.
-
B
Use an AWS Lambda function that is scheduled by cron to load the data into a temporary table in Amazon Redshift. Use Amazon Redshift database triggers to consolidate the final data when all five data parts are ready.
-
C
Create an Amazon S3 bucket to receive the data. Create an AWS Lambda function that is invoked by S3 upload events. Configure the function to validate that all five data parts are gathered before the function loads the data into Amazon Redshift.
-
D
Create an Amazon Kinesis Data Firehose delivery stream. Program a Python condition that will invoke a buffer flush when all five data parts are received.
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Question 19
Single choice
An advertising company has a data lake that is built on Amazon S3. The company uses AWS Glue Data Catalog to maintain the metadata. The data lake is several years old and its overall size has increased exponentially as additional data sources and metadata are stored in the data lake. The data lake administrator wants to implement a mechanism to simplify permissions management between Amazon S3 and the Data Catalog to keep them in sync. Which solution will simplify permissions management with minimal development effort?
-
A
Set AWS Identity and Access Management (IAM) permissions for AWS Glue
-
B
Use AWS Lake Formation permissions
-
C
Manage AWS Glue and S3 permissions by using bucket policies
-
D
Use Amazon Cognito user pools
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Correct answerB
ExplanationReferences:
https://docs.aws.amazon.com/lake-formation/latest/dg/how-it-works.html
Question 20
Single choice
A company plans to store quarterly financial statements in a dedicated Amazon S3 bucket. The financial statements must not be modified or deleted after they are saved to the S3 bucket. Which solution will meet these requirements?
-
A
Create the S3 bucket with S3 Object Lock in governance mode.
-
B
Create the S3 bucket with MFA delete enabled.
-
C
Create the S3 bucket with S3 Object Lock in compliance mode.
-
D
Create S3 buckets in two AWS Regions. Use S3 Cross-Region Replication (CRR) between the buckets.
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Question 21
Single choice
Once a month, a company receives a 100 MB .csv file compressed with gzip. The file contains 50,000 property listing records and is stored in Amazon S3 Glacier. The company needs its data analyst to query a subset of the data for a specific vendor. What is the most cost-effective solution?
-
A
Load the data into Amazon S3 and query it with Amazon S3 Select.
-
B
Query the data from Amazon S3 Glacier directly with Amazon Glacier Select.
-
C
Load the data to Amazon S3 and query it with Amazon Athena.
-
D
Load the data to Amazon S3 and query it with Amazon Redshift Spectrum.
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Correct answerA
ExplanationCorrect answer is A as AWS S3 Select enables querying S3 data on selected fields. As S3 Glacier Select does not support uncompressed data, it needs to be restored to S3. With Amazon S3 Select, you can use simple structured query language (SQL) statements to filter the contents of an Amazon S3 object and retrieve just the subset of data that you need. By using Amazon S3
Select to filter this data, you can reduce the amount of data that Amazon S3 transfers, which reduces the cost and latency to retrieve this data. Amazon S3 Select works on objects stored in CSV, JSON, or Apache Parquet format. It also works with objects that are compressed with GZIP or BZIP2 (for CSV and JSON objects only), and server-side encrypted objects. You can specify the format of the results as either CSV or JSON, and you can determine how the records in the result are delimited. Option B is wrong as Archive objects that are queried by S3 Glacier Select must be formatted as uncompressed comma-separated values (CSV). Options C & D are wrong as Athena and Redshift would add additional cost.
Question 22
Multiple choice
An online retailer needs to deploy a product sales reporting solution. The source data is exported from an external online transaction processing (OLTP) system for reporting. Roll-up data is calculated each day for the previous day's activities.
The reporting system has the following requirements: 1. Have the daily roll-up data readily available for 1 year.
2. After 1 year, archive the daily roll-up data for occasional but immediate access.
3. The source data exports stored in the reporting system must be retained for 5 years. Query access will be needed only for re-evaluation, which may occur within the first 90 days. Which combination of actions will meet these requirements while keeping storage costs to a minimum? (Choose two.)
-
A
Store the source data initially in the Amazon S3 Standard-Infrequent Access (S3 Standard-IA) storage class. Apply a lifecycle configuration that changes the storage class to Amazon S3 Glacier Deep Archive 90 days after creation, and then deletes the data 5 years after creation.
-
B
Store the source data initially in the Amazon S3 Glacier storage class. Apply a lifecycle configuration that changes the storage class from Amazon S3 Glacier to Amazon S3 Glacier Deep Archive 90 days after creation, and then deletes the data 5 years after creation.
-
C
Store the daily roll-up data initially in the Amazon S3 Standard storage class. Apply a lifecycle configuration that changes the storage class to Amazon S3 Glacier Deep Archive 1 year after data creation.
-
D
Store the daily roll-up data initially in the Amazon S3 Standard storage class. Apply a lifecycle configuration that changes the storage class to Amazon S3 Standard-Infrequent Access (S3 Standard-
IA) 1 year after data creation.
-
E
Store the daily roll-up data initially in the Amazon S3 Standard-Infrequent Access (S3 Standard-IA) storage class. Apply a lifecycle configuration that changes the storage class to Amazon S3 Glacier 1
year after data creation.
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Correct answersA, D
ExplanationCorrect answers are A & D Option A as the source data exports can be stored in (S3 Standard-IA) storage class for 90 days if needed, and then moved to S3 Glacier Deep Archive for 5 years before it is expired. Option D as the roll-up data can be stored in Standard class for a year and then moved to S3 Standard-IA to save cost but still provide immediate access. Option B is wrong as although you can transition the objects directly to the S3 Glacier storage class, the data would not be readily available for roll-up calculation and would cost more to perform the roll-up. Options C & E are wrong as S3 Glacier and Glacier Deep Archive would not be able to provide immediate access to the roll-up data.
Question 23
Single choice
A company uses Amazon Redshift to store historical sales transactions. The company must encrypt data at rest in the Redshift cluster. The company also must store encryption keys by using an on-premises
hardware security module (HSM). Which solution will meet these requirements with the LEAST operational overhead?
-
A
Create and store encryption keys by using AWS CloudHSM Classic. Launch a new Redshift cluster with the option to use CloudHSM Classic to store keys.
-
B
Establish an AWS Site-to-Site VPN connection between the existing VPC and the on-premises network. Create an HSM connection and a client certificate for the on-premises HSM. Launch a new Redshift cluster in the VPC with the option to use the on-premises HSM to store keys.
-
C
Establish an AWS Site-to-Site VPN connection between the existing VPC and the on-premises network. Create an HSM connection and a client certificate for the on-premises HSM. Configure the existing Redshift cluster in the VPC with the option to use the on-premises HSM to store keys. Reboot the Redshift cluster.
-
D
Create a replica of the on-premises HSM in AWS CloudHSM. Launch a new Redshift cluster with the option to use CloudHSM to store keys.
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Question 24
Single choice
A banking company is currently using Amazon Redshift for sensitive data. An audit found that the current cluster is unencrypted. Compliance requires that a database with sensitive data must be encrypted using a hardware security module (HSM) with customer managed keys. Which modifications are required in the cluster to ensure compliance?
-
A
Create a new HSM-encrypted Amazon Redshift cluster and migrate the data to the new cluster.
-
B
Modify the DB parameter group with the appropriate encryption settings and then restart the cluster.
-
C
Enable HSM encryption in Amazon Redshift using the command line.
-
D
Modify the Amazon Redshift cluster from the console and enable encryption using the HSM option.
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Correct answerA
ExplanationWhen you modify your cluster to enable AWS KMS encryption, Amazon Redshift automatically migrates your data to a new encrypted cluster. References:
https://docs.aws.amazon.com/redshift/latest/mgmt/working-with-db-encryption.html
Question 25
Single choice
A manufacturing company uses Amazon S3 to store its data. The company wants to use AWS Lake Formation to provide granular-level security on those data assets. The data is in Apache Parquet format.
The company has set a deadline for a consultant to build a data lake. How should the consultant create the MOST cost-effective solution that meets these requirements?
-
A
Run Lake Formation blueprints to move the data to Lake Formation. Once Lake Formation has the data, apply permissions on Lake Formation.
-
B
To create the data catalog, run an AWS Glue crawler on the existing Parquet data. Register the Amazon S3 path and then apply permissions through Lake Formation to provide granular-level security.
-
C
Install Apache Ranger on an Amazon EC2 instance and integrate with Amazon EMR. Using Ranger policies, create role-based access control for the existing data assets in Amazon S3.
-
D
Create multiple IAM roles for different users and groups. Assign IAM roles to different data assets in Amazon S3 to create table-based and column-based access controls.
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Correct answerB
ExplanationB is the right answer as per doc: You can use one of the blueprints available in Lake Formation to ingest data into your data lake. Lake Formation creates Glue workflows that crawl source tables, extract the data, and load it to S3. In S3, Lake Formation organizes the data for you, setting up partitions and data formats for optimized performance and cost. For data already in Amazon S3, you can register those buckets with Lake Formation to manage them.
https://aws.amazon.com/lake-formation/faqs/
Option A is wrong as duplicating the data in AWS Lake Formation would not be the most cost-effective approach. Option C is wrong as using EMR would not be the most cost-effective approach also it would take time to set up. Option D is wrong as S3 does not provide table-based or column-based access control.
Question 26
Single choice
A company operates toll services for highways across the country and collects data that is used to understand usage patterns. Analysts have requested the ability to run traffic reports in near-real time. The company is interested in building an ingestion pipeline that loads all the data into an Amazon Redshift cluster and alerts operations personnel when toll traffic for a particular toll station does not meet a specified threshold. Station data and the corresponding threshold values are stored in Amazon S3. Which approach is the MOST efficient way to meet these requirements?
-
A
Use Amazon Kinesis Data Firehose to collect data and deliver it to Amazon Redshift and Amazon Kinesis Data Analytics simultaneously. Create a reference data source in Kinesis Data Analytics to temporarily store the threshold values from Amazon S3 and compare the count of vehicles for a particular toll station against its corresponding threshold value. Use AWS Lambda to publish an Amazon Simple Notification Service (Amazon SNS) notification if the threshold is not met.
-
B
Use Amazon Kinesis Data Streams to collect all the data from toll stations. Create a stream in Kinesis Data Streams to temporarily store the threshold values from Amazon S3. Send both streams to Amazon Kinesis Data Analytics to compare the count of vehicles for a particular toll station against its corresponding threshold value. Use AWS Lambda to publish an Amazon Simple Notification Service (Amazon SNS) notification if the threshold is not met. Connect Amazon Kinesis Data Firehose to Kinesis Data Streams to deliver the data to Amazon Redshift.
-
C
Use Amazon Kinesis Data Firehose to collect data and deliver it to Amazon Redshift. Then, automatically trigger an AWS Lambda function that queries the data in Amazon Redshift, compares the count of vehicles for a particular toll station against its corresponding threshold values read from Amazon S3, and publishes an Amazon Simple Notification Service (Amazon SNS) notification if the threshold is not met.
-
D
Use Amazon Kinesis Data Firehose to collect data and deliver it to Amazon Redshift and Amazon Kinesis Data Analytics simultaneously. Use Kinesis Data Analytics to compare the count of vehicles against the threshold value for the station stored in a table as an in-application stream based on information stored in Amazon S3. Configure an AWS Lambda function as an output for the application that will publish an Amazon Simple Queue Service (Amazon SQS) notification to alert operations personnel if the threshold is not met.
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Correct answerA
Explanationhttps://docs.aws.amazon.com/kinesisanalytics/latest/dev/app-add-reference-data.html
Question 27
Single choice
A company's data science team is designing a shared dataset repository on a Windows server. The data repository will store a large amount of training data that the data science team commonly uses in its machine learning models. The data scientists create a random number of new datasets each day. The company needs a solution that provides persistent, scalable file storage and high levels of throughput and IOPS. The solution also must be highly available and must integrate with Active Directory for access control. Which solution will meet these requirements with the LEAST development effort?
-
A
Store datasets as files in an Amazon EMR cluster. Set the Active Directory domain for authentication.
-
B
Store datasets as files in Amazon FSx for Windows File Server. Set the Active Directory domain for authentication.
-
C
Store datasets as tables in a multi-node Amazon Redshift cluster. Set the Active Directory domain for authentication.
-
D
Store datasets as global tables in Amazon DynamoDB. Build an application to integrate authentication with the Active Directory domain.
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Question 28
Single choice
A company stores financial performance records of its various portfolios in CSV format in Amazon S3. A data analytics specialist needs to make this data accessible in the AWS Glue Data Catalog for the company's data analysts. The data analytics specialist creates an AWS Glue crawler in the AWS Glue console. What must the data analytics specialist do next to make the data accessible for the data analysts?
-
A
Create an IAM role that includes the AWSGlueExecutionRole policy. Associate the role with the crawler. Specify the S3 path of the source data as the crawler's data store. Create a schedule to run the crawler. Point to the S3 path for the output.
-
B
Create an IAM role that includes the AWSGlueServiceRole policy. Associate the role with the crawler.
Specify the S3 path of the source data as the crawler's data store. Create a schedule to run the crawler. Specify a database name for the output.
-
C
Create an IAM role that includes the AWSGlueExecutionRole policy. Associate the role with the crawler. Specify the S3 path of the source data as the crawler's data store. Allocate data processing units (DPUs) to run the crawler. Specify a database name for the output.
-
D
Create an IAM role that includes the AWSGlueServiceRole policy. Associate the role with the crawler.
Specify the S3 path of the source data as the crawler's data store. Allocate data processing units (DPUs) to run the crawler. Point to the S3 path for the output.
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Question 29
Single choice
An ecommerce company is migrating its business intelligence environment from on premises to the AWS Cloud. The company will use Amazon Redshift in a public subnet and Amazon QuickSight. The tables already are loaded into Amazon Redshift and can be accessed by a SQL tool. The company starts QuickSight for the first time. During the creation of the data source, a data analytics specialist enters all the information and tries to validate the connection. An error with the following message occurs: "Creating a connection to your data source timed out." How should the data analytics specialist resolve this error?
-
A
Grant the SELECT permission on Amazon Redshift tables.
-
B
Add the QuickSight IP address range into the Amazon Redshift security group.
-
C
Create an IAM role for QuickSight to access Amazon Redshift.
-
D
Use a QuickSight admin user for creating the dataset.
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Correct answerB
ExplanationCorrect answer is B as the error is time out and not permissions denied, the most likely reason is the Redshift Security Group does not allow QuickSight IP address range.
For Amazon QuickSight to connect to an Amazon Redshift instance, you must create a new security group for that instance. This security group contains an inbound rule authorizing access from the appropriate IP address range for the Amazon QuickSight servers in that AWS Region. Options A, C & D are wrong as the error is a timeout and not access denied.
https://docs.aws.amazon.com/quicksight/latest/user/enabling-access-redshift.html
Question 30
Single choice
A company wants to provide its data analysts with uninterrupted access to the data in its Amazon Redshift cluster. All data is streamed to an Amazon S3 bucket with Amazon Kinesis Data Firehose. An AWS Glue job that is scheduled to run every 5 minutes issues a COPY command to move the data into Amazon Redshift. The amount of data delivered is uneven throughout the day, and cluster utilization is high during certain periods. The COPY command usually completes within a couple of seconds. However, when load spike occurs, locks can exist and data can be missed. Currently, the AWS Glue job is configured to run without retries, with timeout at 5 minutes and concurrency at 1. How should a data analytics specialist configure the AWS Glue job to optimize fault tolerance and improve data availability in the Amazon Redshift cluster?
-
A
Increase the number of retries. Decrease the timeout value. Increase the job concurrency.
-
B
Keep the number of retries at 0. Decrease the timeout value. Increase the job concurrency.
-
C
Keep the number of retries at 0. Decrease the timeout value. Keep the job concurrency at 1.
-
D
Keep the number of retries at 0. Increase the timeout value. Keep the job concurrency at 1.
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Correct answerA
ExplanationB is out - jobs will due to decreased timeout when there is no concurrency.
C is out - locks will still exist
D is out - Glue job that is scheduled to run every 5 minutes
Question 31
Single choice
A business intelligence (BI) engineer must create a dashboard to visualize how often certain keywords are used in relation to others in social media posts about a public figure. The BI engineer extracts the keywords from the posts and loads them into an Amazon Redshift table. The table displays the keywords and the count corresponding to each keyword. The BI engineer needs to display the top keywords with more emphasis on the most frequently used keywords. Which visual type in Amazon QuickSight meets these requirements?
-
A
-
B
-
C
Circle packing with words
-
D
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Question 32
Single choice
A company creates daily and monthly business metrics from data that partners provide. Each day, the partners deliver JSON data files to an Amazon S3 bucket that the company owns. The S3 object keys use Apache Hive style date partitions. The company uses an Amazon EventBridge rule to invoke an AWS Lambda function that reads all objects in the S3 bucket to aggregate the daily and monthly metrics. The company performs occasional analysis that requires access to historical data. As more data has accumulated, the Lambda function is timing out frequently. A data analytics specialist must prevent the Lambda function timeouts. Which solution will meet these requirements with the LEAST operational overhead?
-
A
Update the EventBridge rule to invoke AWS Step Functions to retry the Lambda function if the function fails.
-
B
Modify the Lambda function to delete older S3 objects during the daily processing.
-
C
Modify the Lambda function to query the S3 objects by using Amazon Athena with date filters.
-
D
Create an AWS Glue job to invoke the Lambda function. Update the EventBridge rule to invoke the
AWS Glue job.
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Question 33
Single choice
A real estate company maintains data about all properties listed in a market. The company receives data about new property listings from vendors who upload the data daily as compressed files into Amazon S3.
The company's leadership team wants to see the most up-to-date listings as soon as the data is uploaded to Amazon S3. The data analytics team must automate and orchestrate the data processing workflow of the listings to feed a dashboard. The team also must provide the ability to perform one-time queries and analytical reporting in a scalable manner. Which solution meets these requirements MOST cost-effectively?
-
A
Use Amazon EMR for processing incoming data. Use AWS Step Functions for workflow orchestration.
Use Apache Hive for one-time queries and analytical reporting. Bulk ingest the data in Amazon OpenSearch Service (Amazon Elasticsearch Service). Use OpenSearch Dashboards (Kibana) on Amazon OpenSearch Service (Amazon Elasticsearch Service) for the dashboard.
-
B
Use Amazon EMR for processing incoming data. Use AWS Step Functions for workflow orchestration.
Use Amazon Athena for one-time queries and analytical reporting. Use Amazon QuickSight for the dashboard.
-
C
Use AWS Glue for processing incoming data. Use AWS Step Functions for workflow orchestration. Use Amazon Redshift Spectrum for one-time queries and analytical reporting. Use OpenSearch Dashboards (Kibana) on Amazon OpenSearch Service (Amazon Elasticsearch Service) for the dashboard.
-
D
Use AWS Glue for processing incoming data. Use AWS Lambda and S3 Event Notifications for workflow orchestration. Use Amazon Athena for one-time queries and analytical reporting. Use Amazon QuickSight for the dashboard.
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Correct answerB
ExplanationReferences:
https://aws.amazon.com/blogs/compute/visualizing-aws-step-functions-workflows-from-the-amazon-athena-console/
Question 34
Single choice
A company receives datasets from partners at various frequencies. The datasets include baseline data and incremental data. The company needs to merge and store all the datasets without reprocessing the data. Which solution will meet these requirements with the LEAST development effort?
-
A
Use an AWS Glue job with a temporary table to process the datasets. Store the data in an Amazon
RDS table.
-
B
Use an Apache Spark job in an Amazon EMR cluster to process the datasets. Store the data in EMR File System (EMRFS).
-
C
Use an AWS Glue job with job bookmarks enabled to process the datasets. Store the data in Amazon
S3.
-
D
Use an AWS Lambda function to process the datasets. Store the data in Amazon S3.
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Question 35
Single choice
A large company has a central data lake to run analytics across different departments. Each department uses a separate AWS account and stores its data in an Amazon S3 bucket in that account. Each AWS account uses the AWS Glue Data Catalog as its data catalog. There are different data lake access requirements based on roles. Associate analysts should only have read access to their departmental data.
Senior data analysts can have access in multiple departments including theirs, but for a subset of columns only. Which solution achieves these required access patterns to minimize costs and administrative tasks?
-
A
Consolidate all AWS accounts into one account. Create different S3 buckets for each department and move all the data from every account to the central data lake account. Migrate the individual data catalogs into a central data catalog and apply fine-grained permissions to give to each user the required access to tables and databases in AWS Glue and Amazon S3.
-
B
Keep the account structure and the individual AWS Glue catalogs on each account. Add a central data lake account and use AWS Glue to catalog data from various accounts. Configure cross-account access for AWS Glue crawlers to scan the data in each departmental S3 bucket to identify the schema and populate the catalog. Add the senior data analysts into the central account and apply highly detailed access controls in the Data Catalog and Amazon S3.
-
C
Set up an individual AWS account for the central data lake. Use AWS Lake Formation to catalog the cross-account locations. On each individual S3 bucket, modify the bucket policy to grant S3 permissions to the Lake Formation service-linked role. Use Lake Formation permissions to add fine-grained access controls to allow senior analysts to view specific tables and columns.
-
D
Set up an individual AWS account for the central data lake and configure a central S3 bucket. Use an AWS Lake Formation blueprint to move the data from the various buckets into the central S3 bucket.
On each individual bucket, modify the bucket policy to grant S3 permissions to the Lake Formation service-linked role. Use Lake Formation permissions to add fine-grained access controls for both associate and senior analysts to view specific tables and columns.
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Correct answerC
ExplanationCorrect answer is C as AWS Data Lake Formation can help provide a centralized place for maintaining data catalog to various locations, without moving the data. Also, AWS Lake Formation permissions can help provide a central access control location. Option A is wrong as consolidating accounts would increase administrative tasks. Option B is wrong as although it might work, it is more simpler to use AWS Lake Formation for access control. Option D is wrong as moving all the data to central S3 would duplicate the storage cost and increase administrative tasks.
Question 36
Single choice
A company wants to optimize the cost of its data and analytics platform. The company is ingesting a number of .csv and JSON files in Amazon S3 from various data sources. Incoming data is expected to be 50 GB each day. The company is using Amazon Athena to query the raw data in Amazon S3 directly. Most queries aggregate data from the past 12 months, and data that is older than 5 years is infrequently queried. The typical query scans about 500 MB of data and is expected to return results in less than 1 minute. The raw data must be retained indefinitely for compliance requirements. Which solution meets the company's requirements?
-
A
Use an AWS Glue ETL job to compress, partition, and convert the data into a columnar data format.
Use Athena to query the processed dataset. Configure a lifecycle policy to move the processed data into the Amazon S3 Standard-Infrequent Access (S3 Standard-IA) storage class 5 years after object creation. Configure a second lifecycle policy to move the raw data into Amazon S3 Glacier for long-
term archival 7 days after object creation.
-
B
Use an AWS Glue ETL job to partition and convert the data into a row-based data format. Use Athena to query the processed dataset. Configure a lifecycle policy to move the data into the Amazon S3
Standard-Infrequent Access (S3 Standard-IA) storage class 5 years after object creation. Configure a second lifecycle policy to move the raw data into Amazon S3 Glacier for long-term archival 7 days after object creation.
-
C
Use an AWS Glue ETL job to compress, partition, and convert the data into a columnar data format.
Use Athena to query the processed dataset. Configure a lifecycle policy to move the processed data into the Amazon S3 Standard-Infrequent Access (S3 Standard-IA) storage class 5 years after the object was last accessed. Configure a second lifecycle policy to move the raw data into Amazon S3
Glacier for long-term archival 7 days after the last date the object was accessed.
-
D
Use an AWS Glue ETL job to partition and convert the data into a row-based data format. Use Athena to query the processed dataset. Configure a lifecycle policy to move the data into the Amazon S3
Standard-Infrequent Access (S3 Standard-IA) storage class 5 years after the object was last accessed.
Configure a second lifecycle policy to move the raw data into Amazon S3 Glacier for long-term archival 7 days after the last date the object was accessed.
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Correct answerA
ExplanationCorrect answer is A as columnar data format store data efficiently by employing column-wise compression and enables split and parallel processing. Storing processed data in S3 in SA-IA and moving raw data in Glacier would help reduce costs. Option B & D is wrong as it is recommended to use columnar data format for processing. Options C is wrong as lifecycle rules are based on Object creation data and not last date when the object was accessed.
Question 37
Single choice
A media company has a streaming playback application. The company needs to collect and analyze data to provide near-real-time feedback on playback issues within 30 seconds. The company requires a consumer application to identify playback issues, such as decreased quality during a specified time frame.
The data will be streamed in JSON format. The schema can change over time. Which solution will meet these requirements?
-
A
Send the data to Amazon Kinesis Data Firehose with delivery to Amazon S3. Configure an S3 event to invoke an AWS Lambda function to process and analyze the data.
-
B
Send the data to Amazon Managed Streaming for Apache Kafka. Configure Amazon Kinesis Data Analytics for SQL Application as the consumer application to process and analyze the data.
-
C
Send the data to Amazon Kinesis Data Firehose with delivery to Amazon S3. Configure Amazon S3 to initiate an event for AWS Lambda to process and analyze the data.
-
D
Send the data to Amazon Kinesis Data Streams. Configure an Amazon Kinesis Data Analytics for Apache Flink application as the consumer application to process and analyze the data.
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Correct answerD
Explanationhttps://aws.amazon.com/kinesis/data-analytics/features/?pg=ln&sec=hs
Question 38
Single choice
A media company has been performing analytics on log data generated by its applications. There has been a recent increase in the number of concurrent analytics jobs running, and the overall performance of existing jobs is decreasing as the number of new jobs is increasing. The partitioned data is stored in Amazon S3 One Zone-Infrequent Access (S3 One Zone-IA) and the analytic processing is performed on Amazon EMR clusters using the EMR File System (EMRFS) with consistent view enabled. A data analyst has determined that it is taking longer for the EMR task nodes to list objects in Amazon S3. Which action would MOST likely increase the performance of accessing log data in Amazon S3?
-
A
Use a hash function to create a random string and add that to the beginning of the object prefixes when storing the log data in Amazon S3.
-
B
Use a lifecycle policy to change the S3 storage class to S3 Standard for the log data.
-
C
Increase the read capacity units (RCUs) for the shared Amazon DynamoDB table.
-
D
Redeploy the EMR clusters that are running slowly to a different Availability Zone.
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Correct answerC
ExplanationEMRFS consistent view tracks consistency using a DynamoDB table to track objects in Amazon S3 that have been synced with or created by EMRF. So increasing RCU for the shared DynamoDB table will help here.
Question 39
Multiple choice
A company uses Amazon Redshift as its data warehouse. The Redshift cluster is not encrypted. A data analytics specialist needs to use hardware security module (HSM) managed encryption keys to encrypt the data that is stored in the Redshift cluster. Which combination of steps will meet these requirements? (Choose three.)
-
A
Stop all write operations on the source cluster. Unload data from the source cluster.
-
B
Copy the data to a new target cluster that is encrypted with AWS Key Management Service (AWS
KMS).
-
C
Modify the source cluster by activating AWS CloudHSM encryption. Configure Amazon Redshift to automatically migrate data to a new encrypted cluster.
-
D
Modify the source cluster by activating encryption from an external HSM. Configure Amazon Redshift to automatically migrate data to a new encrypted cluster.
-
E
Copy the data to a new target cluster that is encrypted with an HSM from AWS CloudHSM.
-
F
Rename the source cluster and the target cluster after the migration so that the target cluster is using the original endpoint.
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Question 40
Single choice
A company has a production AWS account that runs production workloads. The company created a new security AWS account to store and analyze security logs from the production AWS account. The security logs in the production AWS account are stored in Amazon CloudWatch Logs. The company needs to use Amazon Kinesis Data Streams to deliver the security logs to the security AWS account. Which solution will meet these requirements?
-
A
Create a destination data stream in the production AWS account. In the security AWS account, create an IAM role that has cross-account permissions to Kinesis Data Streams in the production AWS account.
-
B
Create a destination data stream in the security AWS account. Create an IAM role and a trust policy to grant CloudWatch Logs the permission to put data into the stream. Create a subscription filter in the security AWS account.
-
C
Create a destination data stream in the production AWS account. In the production AWS account, create an IAM role that has cross-account permissions to Kinesis Data Streams in the security AWS account.
-
D
Create a destination data stream in the security AWS account. Create an IAM role and a trust policy to grant CloudWatch Logs the permission to put data into the stream. Create a subscription filter in the production AWS account.
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Question 41
Single choice
An online gaming company is using an Amazon Kinesis Data Analytics SQL application with a Kinesis data stream as its source. The source sends three non-null fields to the application: player_id, score, and
us_5_digit_zip_code. A data analyst has a .csv mapping file that maps a small number of us_5_digit_zip_code values to a territory code. The data analyst needs to include the territory code, if one exists, as an additional output of the Kinesis Data Analytics application. How should the data analyst meet this requirement while minimizing costs?
-
A
Store the contents of the mapping file in an Amazon DynamoDB table. Preprocess the records as they arrive in the Kinesis Data Analytics application with an AWS Lambda function that fetches the mapping and supplements each record to include the territory code, if one exists. Change the SQL query in the application to include the new field in the SELECT statement.
-
B
Store the mapping file in an Amazon S3 bucket and configure the reference data column headers for the .csv file in the Kinesis Data Analytics application. Change the SQL query in the application to include a join to the file's S3 Amazon Resource Name (ARN), and add the territory code field to the SELECT columns.
-
C
Store the mapping file in an Amazon S3 bucket and configure it as a reference data source for the Kinesis Data Analytics application. Change the SQL query in the application to include a join to the reference table and add the territory code field to the SELECT columns.
-
D
Store the contents of the mapping file in an Amazon DynamoDB table. Change the Kinesis Data Analytics application to send its output to an AWS Lambda function that fetches the mapping and supplements each record to include the territory code, if one exists. Forward the record from the Lambda function to the original application destination.
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Correct answerC
ExplanationCorrect answer is C as Kinesis Data Analytics allows adding S3 source for reference data which can be referred by Kinesis Data Analytics for data enrichment. Kinesis Data Analytics stores it as an in-application reference table. Options A & D are wrong as they are not cost-effective. Option B is wrong as Kinesis Data Analytics stores the reference data as an in-application reference table.
Question 42
Single choice
A company wants find ways to expand its website business by analyzing customer orders and purchasing trends. To perform data analysis, a pipeline must support daily data ingestion from the production databases into a data lake that is built on Amazon S3. The website uses Amazon DynamoDB to store product details and Amazon Aurora PostgreSQL to store order details in production. Which solution can be used to accomplish these goals with LEAST operational overhead?
-
A
Leverage AWS Database Migration Service (AWS DMS) to run two continuous data replication jobs from both Aurora PostgreSQL and DynamoDB into Amazon S3. Leverage AWS Glue for data cataloging.
-
B
Set up an AWS Lake Formation workflow with blueprints for Aurora PostgreSQL and an AWS Glue ETLjob for DynamoDB to ingest data into Amazon S3. Leverage AWS Glue for data cataloging.
-
C
Create a custom Python script to ingest data from both Aurora PostgreSQL and Amazon DynamoDB into Amazon S3 using the AWS SDK for Python (Boto3) library. Deploy the script on an Amazon EC2 instance and schedule the job to run daily using a cron job. Leverage AWS Glue for data cataloging.
-
D
Use Amazon EMR to ingest data from both Aurora PostgreSQL and DynamoDB into Amazon S3.
Leverage Apache Hive on the same EMR cluster for data cataloging.
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Question 43
Single choice
A retail company's data analytics team recently created multiple product sales analysis dashboards for the average selling price per product using Amazon QuickSight. The dashboards were created from .csv files uploaded to Amazon S3. The team is now planning to share the dashboards with the respective external product owners by creating individual users in Amazon QuickSight. For compliance and governance reasons, restricting access is a key requirement. The product owners should view only their respective product analysis in the dashboard reports. Which approach should the data analytics team take to allow product owners to view only their products in the dashboard?
-
A
Separate the data by product and use S3 bucket policies for authorization.
-
B
Separate the data by product and use IAM policies for authorization.
-
C
Create a manifest file with row-level security.
-
D
Create dataset rules with row-level security.
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Correct answerD
ExplanationReferences:
https://docs.aws.amazon.com/quicksight/latest/user/restrict-access-to-a-data-set-using-row-level-security.html
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