A financial services company is building a data lake solution on Amazon S3. The company plans to use analytics offerings from AWS to meet user needs for one-time querying and business intelligence reports. A portion of the columns will contain personally identifiable information (PII) Only authorized users should be able to see plaintext PII data.
What is the MOST operationally efficient solution that meets these requirements?
A. Define a bucket policy for each S3 bucket of the data lake to allow access to users who have authorization to see PII data. Catalog the data by using AWS Glue. Create two IAM roles. Attach a permissions policy with access to PII columns to one role. Attach a policy without these permissions to the other role.
B. Register the S3 locations with AWS Lake Formation. Create two IAM roles. Use Lake Formation data permissions to grant Select permissions to all of the columns for one role. Grant Select permissions to only columns that contain non-PII data for the other role.
C. Register the S3 locations with AWS Lake Formation. Create an AWS Glue job to create an ETL workflow that removes the PII columns from the data and creates a separate copy of the data in another data lake S3 bucket. Register the new S3 locations with Lake Formation. Grant users the permissions to each data lake data based on whether the users are authorized to see PII data.
D. Register the S3 locations with AWS Lake Formation. Create two IAM roles. Attach a permissions policy with access to PII columns to one role. Attach a policy without these permissions to the other role. For each downstream analytics service, use its native security functionality and the IAM roles to secure the PII data.
A company needs to implement a near-real-time messaging system for hotel inventory. The messages are collected from 1,000 data sources and contain hotel inventory data. The data is then processed and distributed to 20 HTTP endpoint destinations. The range of data size for messages is 2-500 KB.
The messages must be delivered to each destination in order. The performance of a single destination HTTP endpoint should not impact the performance of the delivery for other destinations.
Which solution meets these requirements with the LOWEST latency from message ingestion to delivery?
A. Create an Amazon Kinesis data stream, and ingest the data for each source into the stream. Create 30 AWS Lambda functions to read these messages and send the messages to each destination endpoint.
B. Create an Amazon Kinesis data stream, and ingest the data for each source into the stream. Create a single enhanced fan-out AWS Lambda function to read these messages and send the messages to each destination endpoint. Register the function as an enhanced fan-out consumer.
C. Create an Amazon Kinesis Data Firehose delivery stream, and ingest the data for each source into the stream. Configure Kinesis Data Firehose to deliver the data to an Amazon S3 bucket. Invoke an AWS Lambda function with an S3 event notification to read these messages and send the messages to each destination endpoint.
D. Create an Amazon Kinesis data stream, and ingest the data for each source into the stream. Create 20 enhanced fan-out AWS Lambda functions to read these messages and send the messages to each destination endpoint. Register the 20 functions as enhanced fan-out consumers.
A retail company stores order invoices in an Amazon OpenSearch Service (Amazon Elasticsearch Service) cluster Indices on the cluster are created monthly. Once a new month begins, no new writes are made to any of the indices from the previous months. The company has been expanding the storage on the Amazon OpenSearch Service (Amazon Elasticsearch Service) cluster to avoid running out of space, but the company wants to reduce costs. Most searches on the cluster are on the most recent 3 months of data, while the audit team requires infrequent access to older data to generate periodic reports. The most recent 3 months of data must be quickly available for queries, but the audit team can tolerate slower queries if the solution saves on cluster costs
Which of the following is the MOST operationally efficient solution to meet these requirements?
A. Archive indices that are older than 3 months by using Index State Management (ISM) to create a policy to store the indices in Amazon S3 Glacier. When the audit team requires the archived data, restore the archived indices back to the Amazon OpenSearch Service (Amazon Elasticsearch Service) cluster.
B. Archive indices that are older than 3 months by taking manual snapshots and storing the snapshots in Amazon S3. When the audit team requires the archived data, restore the archived indices back to the Amazon OpenSearch Service (Amazon Elasticsearch Service) cluster.
C. Archive indices that are older than 3 months by using Index State Management (ISM) to create a policy to migrate the indices to Amazon OpenSearch Service (Amazon Elasticsearch Service) UltraWarm storage.
D. Archive indices that are older than 3 months by using Index State Management (ISM) to create a policy to migrate the indices to Amazon OpenSearch Service (Amazon Elasticsearch Service) UltraWarm storage. When the audit team requires the older data, migrate the indices in UltraWarm storage back to hot storage.
A company provides an incentive to users who are physically active. The company wants to determine how active the users are by using an application on their mobile devices to track the number of steps they take each day. The company needs to ingest and perform near-real-time analytics on live data. The processed data must be stored and must remain available for 1 year for analytics purposes.
Which solution will meet these requirements with the LEAST operational overhead?
A. Use Amazon Cognito to write the data from the application to Amazon DynamoDB. Use an AWS Step Functions workflow to create a transient Amazon EMR cluster every hour and process the new data from DynamoDB. Output the processed data to Amazon Redshift for analytics. Archive the data from Amazon Redshift after 1 year.
B. Ingest the data into Amazon DynamoDB by using an Amazon API Gateway API as a DynamoDB proxy. Use an AWS Step Functions workflow to create a transient Amazon EMR cluster every hour and process the new data from DynamoDB. Output the processed data to Amazon Redshift to run analytics calculations. Archive the data from Amazon Redshift after 1 year.
C. Ingest the data into Amazon Kinesis Data Streams by using an Amazon API Gateway API as a Kinesis proxy. Run Amazon Kinesis Data Analytics on the stream data. Output the processed data into Amazon S3 by using Amazon Kinesis Data Firehose. Use Amazon Athena to run analytics calculations. Use S3 Lifecycle rules to transition objects to S3 Glacier after 1 year.
D. Write the data from the application into Amazon S3 by using Amazon Kinesis Data Firehose. Use Amazon Athena to run the analytics on the data in Amazon S3. Use S3 Lifecycle rules to transition objects to S3 Glacier after 1 year.
A company uses Amazon Redshift to store its data. The reporting team runs ad-hoc queries to generate reports from the Amazon Redshift database. The reporting team recently started to experience inconsistencies in report generation. Ad-hoc queries used to generate reports that would typically take minutes to run can take hours to run. A data analytics specialist debugging the issue finds that ad-hoc queries are stuck in the queue behind long-running queries.
How should the data analytics specialist resolve the issue?
A. Create partitions in the tables queried in ad-hoc queries.
B. Configure automatic workload management (WLM) from the Amazon Redshift console.
C. Create Amazon Simple Queue Service (Amazon SQS) queues with different priorities. Assign queries to a queue based on priority.
D. Run the VACUUM command for all tables in the database.
A company using Amazon QuickSight Enterprise edition has thousands of dashboards, analyses, and datasets. The company struggles to manage and assign permissions for granting users access to various items within QuickSight. The company wants to make it easier to implement sharing and permissions management.
Which solution should the company implement to simplify permissions management?
A. Use QuickSight folders to organize dashboards, analyses, and datasets. Assign individual users permissions to these folders.
B. Use QuickSight folders to organize dashboards, analyses, and datasets. Assign group permissions by using these folders.
C. Use AWS IAM resource-based policies to assign group permissions to QuickSight items.
D. Use QuickSight user management APIs to provision group permissions based on dashboard naming conventions.
A company has 10-15 of uncompressed .csv files in Amazon S3. The company is evaluating Amazon Athena as a one-time query engine. The company wants to transform the data to optimize query runtime and storage costs. Which option for data format and compression meets these requirements?
A. CSV compressed with zip
B. JSON compressed with bzip2
C. Apache Parquet compressed with Snappy
D. Apache Avro compressed with LZO
A reseller that has thousands of AWS accounts receives AWS Cost and Usage Reports in an Amazon S3 bucket. The reports are delivered to the S3 bucket in the following format:
are experiencing slow queries because of the accumulation of reports from the last 5 years. The business analysts want the operations team to make changes to improve query performance. Which action should the operations team take to meet these requirements?
A. Change the file format to .csv.zip
B. Partition the data by date and account ID
C. Partition the data by month and account ID
D. Partition the data by account ID, year, and month
A data engineer is using AWS Glue ETL jobs to process data at frequent intervals. The processed data is then copied into Amazon S3. The ETL jobs run every 15 minutes. The AWS Glue Data Catalog partitions need to be updated automatically after the completion of each job.
Which solution will meet these requirements MOST cost-effectively?
A. Use the AWS Glue Data Catalog to manage the data catalog. Define an AWS Glue workflow for the ETL process. Define a trigger within the workflow that can start the crawler when an ETL job run is complete.
B. Use the AWS Glue Data Catalog to manage the data catalog. Use AWS Glue Studio to manage ETL jobs. Use the AWS Glue Studio feature that supports updates to the AWS Glue Data Catalog during job runs.
C. Use an Apache Hive metastore to manage the data catalog. Update the AWS Glue ETL code to include the enableUpdateCatalog and partitionKeys arguments.
D. Use the AWS Glue Data Catalog to manage the data catalog. Update the AWS Glue ETL code to include the enableUpdateCatalog and partitionKeys arguments.
A marketing company collects data from third-party providers and uses transient Amazon EMR clusters to process this data. The company wants to host an Apache Hive metastore that is persistent, reliable, and can be accessed by EMR clusters and multiple AWS services and accounts simultaneously. The metastore must also be available at all times.
Which solution meets these requirements with the LEAST operational overhead?
A. Use AWS Glue Data Catalog as the metastore
B. Use an external Amazon EC2 instance running MySQL as the metastore
C. Use Amazon RDS for MySQL as the metastore
D. Use Amazon S3 as the metastore
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