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NEW QUESTION 33
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 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.B. 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.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 CapacityRemainingGB metric. Create an automatic scaling policy to scale in the instance fleet based on the CloudWatch CapacityRemainingGB metric.

Answer: C

Explanation:
https://docs.aws.amazon.com/emr/latest/ManagementGuide/emr-plan-instances-guidelines.html

 

NEW QUESTION 34
A hospital uses an electronic health records (EHR) system to collect two types of data
* Patient information, which includes a patient's name and address
* Diagnostic tests conducted and the results of these tests
Patient information is expected to change periodically Existing diagnostic test data never changes and only new records are added The hospital runs an Amazon Redshift cluster with four dc2.large nodes and wants to automate the ingestion of the patient information and diagnostic test data into respective Amazon Redshift tables for analysis The EHR system exports data as CSV files to an Amazon S3 bucket on a daily basis Two sets of CSV files are generated One set of files is for patient information with updates, deletes, and inserts The other set of files is for new diagnostic test data only What is the MOST cost-effective solution to meet these requirements?

A. Use an AWS Lambda function to run a COPY command that appends new diagnostic test data to the diagnostic tests table Run another COPY command to load the patient information data into the staging tables Use a stored procedure to handle create update, and delete operations for the patient information tableB. Use Amazon EMR with Apache Hudi. Run daily ETL jobs using Apache Spark and the Amazon Redshift JDBC driverC. Use an AWS Glue crawler to catalog the data in Amazon S3 Use Amazon Redshift Spectrum to perform scheduled queries of the data in Amazon S3 and ingest the data into the patient information table and the diagnostic tests table.D. Use AWS Database Migration Service (AWS DMS) to collect and process change data capture (CDC) records Use the COPY command to load patient information data into the staging tables. Use a stored procedure to handle create, update and delete operations for the patient information table

Answer: C

 

NEW QUESTION 35
An ecommerce company stores customer purchase data in Amazon RDS. The company wants a solution to store and analyze historical data. The most recent 6 months of data will be queried frequently for analytics workloads. This data is several terabytes large. Once a month, historical data for the last 5 years must be accessible and will be joined with the more recent data. The company wants to optimize performance and cost.
Which storage solution will meet these requirements?

A. Use an ETL tool to incrementally load the most recent 6 months of data into an Amazon Redshift cluster. Run more frequent queries against this cluster. Create a read replica of the RDS database to run queries on the historical data.B. Incrementally copy data from Amazon RDS to Amazon S3. Create an AWS Glue Data Catalog of the data in Amazon S3. Use Amazon Athena to query the data.C. Create a read replica of the RDS database to store the most recent 6 months of data. Copy the historical data into Amazon S3. Create an AWS Glue Data Catalog of the data in Amazon S3 and Amazon RDS.
Run historical queries using Amazon Athena.D. Incrementally copy data from Amazon RDS to Amazon S3. Load and store the most recent 6 months of data in Amazon Redshift. Configure an Amazon Redshift Spectrum table to connect to all historical data.

Answer: D

 

NEW QUESTION 36
An online gaming company is using an Amazon Kinesis Data Analytics SQL application with a Kinesis data stream as its source. The source sends three non-null fields to the application: player_id, score, and us_5_digit_zip_code.
A data analyst has a .csv mapping file that maps a small number of us_5_digit_zip_code values to a territory code. The data analyst needs to include the territory code, if one exists, as an additional output of the Kinesis Data Analytics application.
How should the data analyst meet this requirement while minimizing costs?

A. Store the mapping file in an Amazon S3 bucket and configure it as a reference data source for the Kinesis Data Analytics application. Change the SQL query in the application to include a join to the reference table and add the territory code field to the SELECT columns.B. Store the contents of the mapping file in an Amazon DynamoDB table. Preprocess the records as they arrive in the Kinesis Data Analytics application with an AWS Lambda function that fetches the mapping and supplements each record to include the territory code, if one exists. Change the SQL query in the application to include the new field in the SELECT statement.C. Store the mapping file in an Amazon S3 bucket and configure the reference data column headers for the
.csv file in the Kinesis Data Analytics application. Change the SQL query in the application to include a join to the file's S3 Amazon Resource Name (ARN), and add the territory code field to the SELECT columns.D. Store the contents of the mapping file in an Amazon DynamoDB table. Change the Kinesis Data Analytics application to send its output to an AWS Lambda function that fetches the mapping and supplements each record to include the territory code, if one exists. Forward the record from the Lambda function to the original application destination.

Answer: A

 

NEW QUESTION 37
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