A large company receives files from external parties in Amazon EC2 throughout the day. At the end of the day, the files are combined into a single file, compressed into a gzip file, and uploaded to Amazon S3. The total size of all the files is close to 100 GB daily. Once the files are uploaded to Amazon S3, an AWS Batch program executes a COPY command to load the files into an Amazon Redshift cluster.
Which program modification will accelerate the COPY process?

A. Upload the individual files to Amazon S3 and run the COPY command as soon as the files become available.
B. Split the number of files so they are equal to a multiple of the number of slices in the Amazon Redshift cluster. Gzip and upload the files to Amazon S3. Run the COPY command on the files.
C. Split the number of files so they are equal to a multiple of the number of compute nodes in the Amazon Redshift cluster. Gzip and upload the files to Amazon S3. Run the COPY command on the files.
D. Apply sharding by breaking up the files so the distkey columns with the same values go to the same file. Gzip and upload the sharded files to Amazon S3. Run the COPY command on the files.

A university intends to use Amazon Kinesis Data Firehose to collect JSON-formatted batches of water quality readings in Amazon S3. The readings are from 50 sensors scattered across a local lake. Students will query the stored data using Amazon Athena to observe changes in a captured metric over time, such as water temperature or acidity. Interest has grown in the study, prompting the university to reconsider how data will be stored.
Which data format and partitioning choices will MOST significantly reduce costs? (Choose two.)

A. Store the data in Apache Avro format using Snappy compression.
B. Partition the data by year, month, and day.
C. Store the data in Apache ORC format using no compression.
D. Store the data in Apache Parquet format using Snappy compression.
E. Partition the data by sensor, year, month, and day.

A data analyst runs a large number of data manipulation language (DML) queries by using Amazon Athena with the JDBC driver. Recently, a query failed after it ran for 30 minutes. The query returned the following message: java.sql.SQLException: Query timeout The data analyst does not immediately need the query results. However, the data analyst needs a long-term solution for this problem.
Which solution will meet these requirements?

A. Split the query into smaller queries to search smaller subsets of data
B. In the settings for Athena, adjust the DML query timeout limit
C. In the Service Quotas console, request an increase for the DML query timeout
D. Save the tables as compressed .csv files

An ecommerce company ingests a large set of clickstream data in JSON format and stores the data in Amazon S3. Business analysts from multiple product divisions need to use Amazon Athena to analyze the data. The company's analytics team must design a solution to monitor the daily data usage for Athena by each product division. The solution also must produce a warning when a division exceeds its quota.
Which solution will meet these requirements with the LEAST operational overhead?

A. Use a CREATE TABLE AS SELECT (CTAS) statement to create separate tables for each product division. Use AWS Budgets to track Athena usage. Configure a threshold for the budget. Use Amazon Simple Notification Service (Amazon SNS) to send notifications when thresholds are breached.
B. Create an AWS account for each division. Provide cross-account access to an AWS Glue Data Catalog to all the accounts. Set an Amazon CloudWatch alarm to monitor Athena usage. Use Amazon Simple Notification Service (Amazon SNS) to send notifications.
C. Create an Athena workgroup for each division. Configure a data usage control for each workgroup and a time period of 1 day. Configure an action to send notifications to an Amazon Simple Notification Service (Amazon SNS) topic.
D. Create an AWS account for each division. Configure an AWS Glue Data Catalog in each account. Set an Amazon CloudWatch alarm to monitor Athena usage. Use Amazon Simple Notification Service (Amazon SNS) to send notifications.

A company has a data lake on AWS that ingests sources of data from multiple business units and uses Amazon Athena for queries. The storage layer is Amazon S3 using the AWS Glue Data Catalog. The company wants to make the data available to its data scientists and business analysts. However, the company first needs to manage data access for Athena based on user roles and responsibilities.
What should the company do to apply these access controls with the LEAST operational overhead?

A. Define security policy-based rules for the users and applications by role in AWS Lake Formation.
B. Define security policy-based rules for the users and applications by role in AWS Identity and Access Management (IAM).
C. Define security policy-based rules for the tables and columns by role in AWS Glue.
D. Define security policy-based rules for the tables and columns by role in AWS Identity and Access Management (IAM).

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 uses Amazon Redshift as its data warehouse. A new table includes some columns that contain sensitive data and some columns that contain non-sensitive data. The data in the table eventually will be referenced by several existing queries that run many times each day.
A data analytics specialist must ensure that only members of the company's auditing team can read the columns that contain sensitive data. All other users must have read-only access to the columns that contain non-sensitive data.
Which solution will meet these requirements with the LEAST operational overhead?

A. Grant the auditing team permission to read from the table. Load the columns that contain non-sensitive data into a second table. Grant the appropriate users read-only permissions to the second table.
B. Grant all users read-only permissions to the columns that contain non-sensitive data. Use the GRANT SELECT command to allow the auditing team to access the columns that contain sensitive data.
C. Grant all users read-only permissions to the columns that contain non-sensitive data. Attach an IAM policy to the auditing team with an explicit. Allow action that grants access to the columns that contain sensitive data.
D. Grant the auditing team permission to read from the table. Create a view of the table that includes the columns that contain non-sensitive data. Grant the appropriate users read-only permissions to that view.

A company wants to enrich application logs in near-real-time and use the enriched dataset for further analysis.
The application is running on Amazon EC2 instances across multiple Availability Zones and storing its logs using Amazon CloudWatch Logs. The enrichment source is stored in an Amazon DynamoDB table.
Which solution meets the requirements for the event collection and enrichment?

A. Use a CloudWatch Logs subscription to send the data to Amazon Kinesis Data Firehose. Use AWS Lambda to transform the data in the Kinesis Data Firehose delivery stream and enrich it with the data in the DynamoDB table. Configure Amazon S3 as the Kinesis Data Firehose delivery destination.
B. Export the raw logs to Amazon S3 on an hourly basis using the AWS CLI. Use AWS Glue crawlers to catalog the logs. Set up an AWS Glue connection for the DynamoDB table and set up an AWS Glue ETL job to enrich the data. Store the enriched data in Amazon S3.
C. Configure the application to write the logs locally and use Amazon Kinesis Agent to send the data to Amazon Kinesis Data Streams. Configure a Kinesis Data Analytics SQL application with the Kinesis data stream as the source. Join the SQL application input stream with DynamoDB records, and then store the enriched output stream in Amazon S3 using Amazon Kinesis Data Firehose.
D. Export the raw logs to Amazon S3 on an hourly basis using the AWS CLI. Use Apache Spark SQL on Amazon EMR to read the logs from Amazon S3 and enrich the records with the data from DynamoDB. Store the enriched data in Amazon S3.

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 Flexible Retrieval 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 Flexible Retrieval after 1 year.

A financial services company needs to aggregate daily stock trade data from the exchanges into a data store.
The company requires that data be streamed directly into the data store, but also occasionally allows data to be modified using SQL. The solution should integrate complex, analytic queries running with minimal latency. The solution must provide a business intelligence dashboard that enables viewing of the top contributors to anomalies in stock prices.
Which solution meets the company's requirements?

A. Use Amazon Kinesis Data Firehose to stream data to Amazon S3. Use Amazon Athena as a data source for Amazon QuickSight to create a business intelligence dashboard.
B. Use Amazon Kinesis Data Streams to stream data to Amazon Redshift. Use Amazon Redshift as a data source for Amazon QuickSight to create a business intelligence dashboard.
C. Use Amazon Kinesis Data Firehose to stream data to Amazon Redshift. Use Amazon Redshift as a data source for Amazon QuickSight to create a business intelligence dashboard.
D. Use Amazon Kinesis Data Streams to stream data to Amazon S3. Use Amazon Athena as a data source for Amazon QuickSight to create a business intelligence dashboard.