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NEW QUESTION 34
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 tables and columns by role in AWS Glue.
- B. Define security policy-based rules for the tables and columns by role in AWS Identity and Access Management (IAM).
- C. Define security policy-based rules for the users and applications by role in AWS Lake Formation.
- D. Define security policy-based rules for the users and applications by role in AWS Identity and Access Management (IAM).
Answer: B
NEW QUESTION 35
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?
- A. Place Amazon QuickSight and Amazon Redshift in the security group and use an Amazon S3 endpoint to connect Amazon QuickSight to Amazon S3.
- B. Use a VPC endpoint to connect to Amazon S3 from Amazon QuickSight and an IAM role to authenticate Amazon Redshift.
- C. Use an Active Directory connector and single sign-on (SSO) in a corporate network environment.
- D. Establish a secure connection by creating an S3 endpoint to connect Amazon QuickSight and a VPC endpoint to connect to Amazon Redshift.
Answer: B
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. Add more nodes using the AWS Management Console during peak hours. Set the distribution style to ALL.
- 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. Enable concurrency scaling in the workload management (WLM) queue.
Answer: D
Explanation:
https://docs.aws.amazon.com/redshift/latest/dg/cm-c-implementing-workload-management.html
NEW QUESTION 37
A company launched a service that produces millions of messages every day and uses Amazon Kinesis Data Streams as the streaming service.
The company uses the Kinesis SDK to write data to Kinesis Data Streams. A few months after launch, a data analyst found that write performance is significantly reduced. The data analyst investigated the metrics and determined that Kinesis is throttling the write requests. The data analyst wants to address this issue without significant changes to the architecture.
Which actions should the data analyst take to resolve this issue? (Choose two.)
- A. Choose partition keys in a way that results in a uniform record distribution across shards.
- B. Replace the Kinesis API-based data ingestion mechanism with Kinesis Agent.
- C. Customize the application code to include retry logic to improve performance.
- D. Increase the number of shards in the stream using the UpdateShardCount API.
- E. Increase the Kinesis Data Streams retention period to reduce throttling.
Answer: A,D
Explanation:
Explanation
https://aws.amazon.com/blogs/big-data/under-the-hood-scaling-your-kinesis-data-streams/
NEW QUESTION 38
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