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NEW QUESTION 43
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 dat a. 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 divisions exceeds its quota Which solution will meet these requirements with the LEAST operational overhead?
- A. 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
- B. 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.
- C. 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.
- D. 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.
Answer: A
NEW QUESTION 44
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 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.
- B. 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.
- C. 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.
- D. 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.
Answer: B
NEW QUESTION 45
A financial company uses Apache Hive on Amazon EMR for ad-hoc queries. Users are complaining of sluggish performance.
A data analyst notes the following:
* Approximately 90% of queries are submitted 1 hour after the market opens.
* Hadoop Distributed File System (HDFS) utilization never exceeds 10%.
Which solution would help address the performance issues?
- A. Create instance group configurations for core and task nodes. Create an automatic scaling policy to scale out the instance groups based on the Amazon CloudWatch CapacityRemainingGB metric. Create an automatic scaling policy to scale in the instance groups based on the CloudWatch CapacityRemainingGB metric.
- B. Create instance fleet configurations for core and task nodes. Create an automatic scaling policy to scale out the instance groups based on the Amazon CloudWatch CapacityRemainingGB metric. Create an automatic scaling policy to scale in the instance fleet based on the CloudWatch CapacityRemainingGB metric.
- C. Create instance group configurations for core and task nodes. Create an automatic scaling policy to scale out the instance groups based on the Amazon CloudWatch YARNMemoryAvailablePercentage metric.
Create an automatic scaling policy to scale in the instance groups based on the CloudWatch YARNMemoryAvailablePercentage metric. - D. Create instance fleet configurations for core and task nodes. Create an automatic scaling policy to scale out the instance groups based on the Amazon CloudWatch YARNMemoryAvailablePercentage metric.
Create an automatic scaling policy to scale in the instance fleet based on the CloudWatch YARNMemoryAvailablePercentage metric.
Answer: C
Explanation:
Explanation
https://docs.aws.amazon.com/emr/latest/ManagementGuide/emr-plan-instances-guidelines.html
NEW QUESTION 46
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. Enable job bookmarks on the AWS Glue jobs.
- B. Create custom logic on the ETL jobs to track the processed S3 objects.
- C. Have the ETL jobs delete the processed objects or data from Amazon S3 after each run.
- D. Have the ETL jobs read the data from Amazon S3 using a DataFrame.
Answer: C
NEW QUESTION 47
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