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NEW QUESTION 36
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 execute. The company needs to optimize query execution and avoid any downtime.
What is the MOST cost-effective solution?

• A. Enable concurrency scaling in the workload management (WLM) queue.
• B. Use a snapshot, restore, and resize operation. Switch to the new target cluster.
• C. Use elastic resize to quickly add nodes during peak times. Remove the nodes when they are not needed.
• D. Add more nodes using the AWS Management Console during peak hours. Set the distribution style to ALL.

Answer: A

Explanation:
Explanation
https://docs.aws.amazon.com/redshift/latest/dg/cm-c-implementing-workload-management.html

NEW QUESTION 37
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?

• A. Use Amazon QuickSight with Amazon Athena as the data source. Use heat maps as the visual type.
• B. Use Amazon QuickSight with Amazon Athena as the data source. Use pivot tables as the visual type.
• C. Use Amazon QuickSight with Amazon S3 as the data source. Use heat maps as the visual type.
• D. Use Amazon QuickSight with Amazon S3 as the data source. Use pivot tables as the visual type.

Answer: B

NEW QUESTION 38
A streaming application is reading data from Amazon Kinesis Data Streams and immediately writing the data to an Amazon S3 bucket every 10 seconds. The application is reading data from hundreds of shards. The batch interval cannot be changed due to a separate requirement. The data is being accessed by Amazon Athena.
Users are seeing degradation in query performance as time progresses.
Which action can help improve query performance?

• A. Increase the number of shards in Kinesis Data Streams.
• B. Write the files to multiple S3 buckets.
• C. Add more memory and CPU capacity to the streaming application.
• D. Merge the files in Amazon S3 to form larger files.

Answer: D

Explanation:
Explanation
https://aws.amazon.com/blogs/big-data/top-10-performance-tuning-tips-for-amazon-athena/

NEW QUESTION 39
A transportation company uses IoT sensors attached to trucks to collect vehicle data for its global delivery fleet. The company currently sends the sensor data in small .csv files to Amazon S3. The files are then loaded into a 10-node Amazon Redshift cluster with two slices per node and queried using both Amazon Athena and Amazon Redshift. The company wants to optimize the files to reduce the cost of querying and also improve the speed of data loading into the Amazon Redshift cluster.
Which solution meets these requirements?

• A. Use AWS Glue to convert the files from .csv to Apache Parquet to create 20 Parquet files. COPY the files into Amazon Redshift and query the files with Athena from Amazon S3.
• B. Use AWS Glue to convert all the files from .csv to a single large Apache Parquet file. COPY the file into Amazon Redshift and query the file with Athena from Amazon S3.
• C. Use Amazon EMR to convert each .csv file to Apache Avro. COPY the files into Amazon Redshift and query the file with Athena from Amazon S3.
• D. Use AWS Glue to convert the files from .csv to a single large Apache ORC file. COPY the file into Amazon Redshift and query the file with Athena from Amazon S3.

Answer: A

NEW QUESTION 40
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. Create dataset rules with row-level security.
• B. Create a manifest file with row-level security.
• C. Separate the data by product and use S3 bucket policies for authorization.
• D. Separate the data by product and use IAM policies for authorization.

Answer: D

NEW QUESTION 41
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