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NEW QUESTION 54
A company has developed an Apache Hive script to batch process data stared in Amazon S3. The script needs to run once every day and store the output in Amazon S3. The company tested the script, and it completes within 30 minutes on a small local three-node cluster.
Which solution is the MOST cost-effective for scheduling and executing the script?

• A. Use AWS Lambda layers and load the Hive runtime to AWS Lambda and copy the Hive script.
  Schedule the Lambda function to run daily by creating a workflow using AWS Step Functions.
• B. Create an AWS Lambda function to spin up an Amazon EMR cluster with a Hive execution step. Set KeepJobFlowAliveWhenNoSteps to false and disable the termination protection flag. Use Amazon CloudWatch Events to schedule the Lambda function to run daily.
• C. Use the AWS Management Console to spin up an Amazon EMR cluster with Python Hue. Hive, and Apache Oozie. Set the termination protection flag to true and use Spot Instances for the core nodes of the cluster. Configure an Oozie workflow in the cluster to invoke the Hive script daily.
• D. Create an AWS Glue job with the Hive script to perform the batch operation. Configure the job to run once a day using a time-based schedule.

Answer: D

NEW QUESTION 55
A company has developed several AWS Glue jobs to validate and transform its data from Amazon S3 and load it into Amazon RDS for MySQL in batches once every day. The ETL jobs read the S3 data using a DynamicFrame. Currently, the ETL developers are experiencing challenges in processing only the incremental data on every run, as the AWS Glue job processes all the S3 input data on each run.
Which approach would allow the developers to solve the issue with minimal coding effort?

• A. Have the ETL jobs read the data from Amazon S3 using a DataFrame.
• B. Enable job bookmarks on the AWS Glue jobs.
• C. Have the ETL jobs delete the processed objects or data from Amazon S3 after each run.
• D. Create custom logic on the ETL jobs to track the processed S3 objects.

Answer: B

NEW QUESTION 56
A retail company leverages Amazon Athena for ad-hoc queries against an AWS Glue Data Catalog. The data analytics team manages the data catalog and data access for the company. The data analytics team wants to separate queries and manage the cost of running those queries by different workloads and teams. Ideally, the data analysts want to group the queries run by different users within a team, store the query results in individual Amazon S3 buckets specific to each team, and enforce cost constraints on the queries run against the Data Catalog.
Which solution meets these requirements?

• A. Create Athena query groups for each team within the company and assign users to the groups.
• B. Create Athena workgroups for each team within the company. Set up IAM workgroup policies that control user access and actions on the workgroup resources.
• C. Create Athena resource groups for each team within the company and assign users to these groups. Add S3 bucket names and other query configurations to the properties list for the resource groups.
• D. Create IAM groups and resource tags for each team within the company. Set up IAM policies that control user access and actions on the Data Catalog resources.

Answer: B

Explanation:
Explanation
https://aws.amazon.com/about-aws/whats-new/2019/02/athena_workgroups/

NEW QUESTION 57
A real estate company has a mission-critical application using Apache HBase in Amazon EMR. Amazon EMR is configured with a single master node. The company has over 5 TB of data stored on an Hadoop Distributed File System (HDFS). The company wants a cost-effective solution to make its HBase data highly available.
Which architectural pattern meets company's requirements?

• A. Use Spot Instances for core and task nodes and a Reserved Instance for the EMR master node.
  Configure
  the EMR cluster with multiple master nodes. Schedule automated snapshots using Amazon EventBridge.
• B. Store the data on an EMR File System (EMRFS) instead of HDFS. Enable EMRFS consistent view.
  Create an EMR HBase cluster with multiple master nodes. Point the HBase root directory to an Amazon S3 bucket.
• C. Store the data on an EMR File System (EMRFS) instead of HDFS and enable EMRFS consistent view.
  Run two separate EMR clusters in two different Availability Zones. Point both clusters to the same HBase root directory in the same Amazon S3 bucket.
• D. Store the data on an EMR File System (EMRFS) instead of HDFS and enable EMRFS consistent view.
  Create a primary EMR HBase cluster with multiple master nodes. Create a secondary EMR HBase read- replica cluster in a separate Availability Zone. Point both clusters to the same HBase root directory in the same Amazon S3 bucket.

Answer: C

NEW QUESTION 58
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. Use a snapshot, restore, and resize operation. Switch to the new target cluster.
• B. Use elastic resize to quickly add nodes during peak times. Remove the nodes when they are not needed.
• C. Enable concurrency scaling in the workload management (WLM) queue.
• D. Add more nodes using the AWS Management Console during peak hours. Set the distribution style to ALL.

Answer: C

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

NEW QUESTION 59
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