A company plans to provision a log delivery stream within a VPC. The company configured the VPC flow logs to publish to Amazon CloudWatch Logs. The company needs to send the flow logs to Splunk at a near-real-time rate for further analysis.
Which solution will meet these requirements with the LEAST operational overhead?
A. Configure an Amazon Kinesis data stream with Splunk as a destination. Create a CloudWatch Logs subscription filter to send log events to the data stream.
B. Create an Amazon Kinesis Data Firehose delivery stream with Splunk as a destination. Create a CloudWatch Logs subscription filter to send log events to the delivery stream.
C. Create an Amazon Kinesis Data Firehose delivery stream with Splunk as a destination. Create an AWS Lambda function to send the flow logs from CloudWatch Logs to the delivery stream.
D. Configure an Amazon Kinesis data stream with Splunk as a destination. Create an AWS Lambda function to send the flow logs from CloudWatch Logs to the data stream.
A company is storing millions of sales transaction records in Amazon Redshift. A data analyst must perform an analysis on sales data. The analysis depends on a subset of customer record data that resides in a Salesforce application. The company wants to transfer the data from Salesforce with the least possible infrastructure setup, coding, and operational effort.
Which solution meets these requirements?
A. Use AWS Glue and the SpringML library to connect Apache Spark with Salesforce and extract the data as a table to Amazon S3 in Apache Parquet format. Query the data by using Amazon Redshift Spectrum.
B. Use Amazon AppFlow to create a flow. Establish a connection and a flow trigger to transfer customer record data from Salesforce to an Amazon Redshift table.
C. Use Amazon API Gateway to configure a Salesforce customer data flow subscription to AWS Lambda events and create tables in Amazon S3 in Apache Parquet format. Query the data by using Amazon Redshift Spectrum.
D. Use Salesforce Data Loader to export the Salesforce customer data as a .csv file and load it into Amazon S3. Query the data by using Amazon Redshift Spectrum.
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.
A retail company that is based in the United States has launched a global website. The website's historic transaction data is stored in an Amazon Redshift cluster in a VPC in the us-east-1 Region. The company's business intelligence (BI) team wants to enhance user experience by providing a dashboard to visualize trends.
The BI team decides to use Amazon QuickSight to render the dashboards. During development, a team in Japan provisioned QuickSight in the ap-northeast-1 Region. However, the team cannot connect from QuickSight in ap-northeast-1 to the Amazon Redshift cluster in us-east-1.
Which solution will resolve this issue MOST cost-effectively?
A. In the Amazon Redshift console, configure Cross-Region snapshots. Set the destination Region as ap-northeast-1. Restore the Amazon Redshift cluster from the snapshot. Connect to QuickSight in ap-northeast-1.
B. Create a VPC endpoint from the QuickSight VPC to the Amazon Redshift VPC.
C. Create an Amazon Redshift endpoint connection string with Region information in the string. Use this connection string in QuickSight to connect to Amazon Redshift.
D. Create a new security group for the Amazon Redshift cluster in us-east-1. Add an inbound rule that allows access from the appropriate IP address range for the QuickSight servers in ap-northeast-1.
A data analytics specialist has a 50 GB data file in .csv format and wants to perform a data transformation task. The data analytics specialist is using the Amazon Athena CREATE TABLE AS SELECT (CTAS) statement to perform the transformation. The resulting output will be used to query the data from Amazon Redshift Spectrum.
Which CTAS statement should the data analytics specialist use to provide the MOST efficient performance?
A. CREATE TABLE new_Table
WITH (
format = 'TEXTFILE',
orc_compression = 'SNAPPY')
AS SELECT *
FROM old_table;
B. CREATE TABLE new_Table
WITH (
format = 'TEXTFILE',
)
AS SELECT *
FROM old_table;
C. CREATE TABLE new_Table
WITH (
format = 'PARQUET',
parquet_compression = 'SNAPPY')
AS SELECT *
FROM old_table;
D. CREATE TABLE new_Table
WITH (
format = JSON,
)
AS SELECT *
FROM old_table;
A company uses Amazon Redshift for data analysis. The data is not encrypted at rest. A data analytics specialist must implement a solution to encrypt the data at rest.
Which solution will meet this requirement with the LEAST operational overhead?
A. Use the ALTER TABLE command with the ENCODE option to update existing private information columns in the Amazon Redshift tables to use LZO encoding.
B. Export data from the existing Amazon Redshift cluster to Amazon S3 by using the UNLOAD command with the ENCRYPTED option. Create a new Amazon Redshift cluster with encryption enabled. Load data into the new cluster by using the COPY command.
C. Create a manual snapshot of the existing Amazon Redshift cluster. Restore the snapshot into a new Amazon Redshift cluster with encryption enabled.
D. Modify the existing Amazon Redshift cluster to use AWS Key Management Service (AWS KMS) encryption. Wait for the cluster to finish resizing.
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. Bar charts
B. Word clouds
C. Circle packing with words
D. Heat maps
A data analytics specialist is creating a solution that uses AWS Glue ETL jobs to process .csv and .json files as they arrive in Amazon S3. The data analytics specialist has created separate AWS Glue ETL jobs for processing each file type. The data analytics specialist also has set up an event notification on the S3 bucket for all new object create events. The event invokes an AWS Lambda function to call the appropriate AWS Glue ETL job to run.
The daily number of files is consistent. The files arrive continuously and take 5-10 minutes to process. The data analytics specialist has set up the appropriate permission for the Lambda function and the AWS Glue ETL job to run, but the solution fails in quality testing with the following error:
ConcurrentRunsExceededException
All the files are valid and are in the expected format for processing.
Which set of actions will resolve the error?
A. Create two separate S3 buckets for each file type. Create two separate Lambda functions for the file types and for calls to the corresponding AWS Glue ETL job.
B. Use job bookmarks and turn on continuous logging in each of the AWS Glue ETL job properties.
C. Ensure that the worker type of the AWS Glue ETL job is G.1X or G.2X and that the number of workers is equivalent to the daily number of files to be processed.
D. Increase the maximum number of concurrent runs in the job properties.
A company has a data warehouse in Amazon Redshift that is approximately 500 TB in size. New data is imported every few hours and read-only queries are run throughout the day and evening. There is a particularly heavy load with no writes for several hours each morning on business days. During those hours, some queries are queued and take a long time to run. The company needs to optimize query performance and avoid any downtime.
What is the MOST cost-effective solution?
A. Turn on concurrency scaling in the workload management (WLM) queue.
B. Add more nodes using the AWS Management Console during peak hours. Set the distribution style to ALL.
C. Use elastic resize to quickly add nodes during peak times. Remove the nodes when they are not needed.
D. Use a snapshot, restore, and resize operation. Switch to the new target cluster.
A marketing company wants to improve its reporting and business intelligence capabilities. During the planning phase, the company interviewed the relevant stakeholders and discovered that:
1.
The operations team reports are run hourly for the current month's data.
2.
The sales team wants to use multiple Amazon QuickSight dashboards to show a rolling view of the last 30 days based on several categories. The sales team also wants to view the data as soon as it reaches the reporting backend.
3.
The finance team's reports are run daily for last month's data and once a month for the last 24 months of data.
Currently, there is 400 TB of data in the system with an expected additional 100 TB added every month. The company is looking for a solution that is as cost-effective as possible.
Which solution meets the company's requirements?
A. Store the last 24 months of data in Amazon Redshift. Configure Amazon QuickSight with Amazon Redshift as the data source.
B. Store the last 2 months of data in Amazon Redshift and the rest of the months in Amazon S3. Set up an external schema and table for Amazon Redshift Spectrum. Configure Amazon QuickSight with Amazon Redshift as the data source.
C. Store the last 24 months of data in Amazon S3 and query it using Amazon Redshift Spectrum. Configure Amazon QuickSight with Amazon Redshift Spectrum as the data source.
D. Store the last 2 months of data in Amazon Redshift and the rest of the months in Amazon S3. Use a long-running Amazon EMR with Apache Spark cluster to query the data as needed. Configure Amazon QuickSight with Amazon EMR as the data source.
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