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AWS Certified Data Engineer - Associate (DEA-C01)

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Question # 21

A data engineer is using an Apache Iceberg framework to build a data lake that contains 100 TB of data. The data engineer wants to run AWS Glue Apache Spark Jobs that use the Iceberg framework.

What combination of steps will meet these requirements? (Select TWO.)

Options:

Create a key named -conf for an AWS Glue job. Set Iceberg as a value for the --datalake-formats job parameter.

Specify the path to a specific version of Iceberg by using the --extra-Jars job parameter. Set Iceberg as a value for the ~ datalake-formats job parameter.

Set Iceberg as a value for the -datalake-formats job parameter.

Set the -enable-auto-scaling parameter to true.

Add the -job-bookmark-option: job-bookmark-enable parameter to an AWS Glue job.

Discussion 0

Question # 22

A company wants to migrate a data warehouse from Teradata to Amazon Redshift. Which solution will meet this requirement with the LEAST operational effort?

Options:

Use AWS Database Migration Service (AWS DMS) Schema Conversion to migrate the schema. Use AWS DMS to migrate the data.

Use the AWS Schema Conversion Tool (AWS SCT) to migrate the schema. Use AWS Database Migration Service (AWS DMS) to migrate the data.

Use AWS Database Migration Service (AWS DMS) to migrate the data. Use automatic schema conversion.

Manually export the schema definition from Teradata. Apply the schema to the Amazon Redshift database. Use AWS Database Migration Service (AWS DMS) to migrate the data.

Discussion 0

Question # 23

A data engineer is designing a new data lake architecture for a company. The data engineer plans to use Apache Iceberg tables and AWS Glue Data Catalog to achieve fast query performance and enhanced metadata handling. The data engineer needs to query historical data for trend analysis and optimize storage costs for a large volume of event data.

Which solution will meet these requirements with the LEAST development effort?

Options:

Store Iceberg table data files in Amazon S3 Intelligent-Tiering.

Define partitioning schemes based on event type and event date.

Use AWS Glue Data Catalog to automatically optimize Iceberg storage.

Run a custom AWS Glue job to compact Iceberg table data files.

Discussion 0

Question # 24

A company is planning to upgrade its Amazon Elastic Block Store (Amazon EBS) General Purpose SSD storage from gp2 to gp3. The company wants to prevent any interruptions in its Amazon EC2 instances that will cause data loss during the migration to the upgraded storage.

Which solution will meet these requirements with the LEAST operational overhead?

Options:

Create snapshots of the gp2 volumes. Create new gp3 volumes from the snapshots. Attach the new gp3 volumes to the EC2 instances.

Create new gp3 volumes. Gradually transfer the data to the new gp3 volumes. When the transfer is complete, mount the new gp3 volumes to the EC2 instances to replace the gp2 volumes.

Change the volume type of the existing gp2 volumes to gp3. Enter new values for volume size, IOPS, and throughput.

Use AWS DataSync to create new gp3 volumes. Transfer the data from the original gp2 volumes to the new gp3 volumes.

Discussion 0

Question # 25

A company needs to generate a one-time performance report by joining data that is stored in Amazon DynamoDB, Amazon RDS, Amazon Redshift, and Amazon S3. The company wants to avoid unnecessary data movement and to minimize query execution time.

Which solution will meet these requirements?

Options:

Capture data from DynamoDB by using DynamoDB Streams. Migrate data from Amazon RDS by using AWS DMS. Export Amazon Redshift data. Store all data in Amazon S3. Use Redshift Spectrum to run queries.

Set up an AWS Glue ETL pipeline to extract, transform, and centralize data in Amazon S3. Use Amazon Athena to run analytical queries.

Deploy an Amazon EMR cluster powered by Apache Spark to ingest, process, and merge datasets from multiple sources. Run analytical workloads on the merged data.

Use Amazon Athena Federated Query to perform one-time joins and analysis across DynamoDB, Amazon RDS, Amazon Redshift, and Amazon S3.

Discussion 0

Question # 26

A company is building a new application that ingests CSV files into Amazon Redshift. The company has developed the frontend for the application.

The files are stored in an Amazon S3 bucket. Files are no larger than 5 M

A data engineer is developing the extract, transform, and load (ETL) pipeline for the CSV files. The data engineer configured a Redshift cluster and an AWS Lambda function that copies the data out of the files into the Redshift cluster.

Which additional steps should the data engineer perform to meet these requirements?

Options:

Configure the bucket to send S3 event notifications to Amazon EventBridge. Configure an EventBridge rule that matches S3 new object created events. Set the Lambda function as the target.

Configure the S3 bucket to send S3 event notifications to an Amazon Simple Queue Service (Amazon SQS) queue. Configure the Lambda function to process the queue.

Configure AWS Database Migration Service (AWS DMS) to stream new S3 objects to a data stream in Amazon Kinesis Data Streams. Set the Lambda function as the target of the data stream.

Configure an Amazon EventBridge rule that matches S3 new object created events. Set an Amazon Simple Queue Service (Amazon SQS) queue as the target of the rule. Configure the Lambda function to process the queue.

Discussion 0

Question # 27

A company currently uses a provisioned Amazon EMR cluster that includes general purpose Amazon EC2 instances. The EMR cluster uses EMR managed scaling between one to five task nodes for the company ' s long-running Apache Spark extract, transform, and load (ETL) job. The company runs the ETL job every day.

When the company runs the ETL job, the EMR cluster quickly scales up to five nodes. The EMR cluster often reaches maximum CPU usage, but the memory usage remains under 30%.

The company wants to modify the EMR cluster configuration to reduce the EMR costs to run the daily ETL job.

Which solution will meet these requirements MOST cost-effectively?

Options:

Increase the maximum number of task nodes for EMR managed scaling to 10.

Change the task node type from general purpose EC2 instances to memory optimized EC2 instances.

Switch the task node type from general purpose EC2 instances to compute optimized EC2 instances.

Reduce the scaling cooldown period for the provisioned EMR cluster.

Discussion 0

Question # 28

A company uses AWS Key Management Service (AWS KMS) to encrypt an Amazon Redshift cluster. The company wants to configure a cross-Region snapshot of the Redshift cluster as part of disaster recovery (DR) strategy.

A data engineer needs to use the AWS CLI to create the cross-Region snapshot.

Which combination of steps will meet these requirements? (Select TWO.)

Options:

Create a KMS key and configure a snapshot copy grant in the source AWS Region.

In the source AWS Region, enable snapshot copying. Specify the name of the snapshot copy grant that is created in the destination AWS Region.

In the source AWS Region, enable snapshot copying. Specify the name of the snapshot copy grant that is created in the source AWS Region.

Create a KMS key and configure a snapshot copy grant in the destination AWS Region.

Convert the cluster to a Multi-AZ deployment.

Discussion 0

Question # 29

A data engineer needs to build an extract, transform, and load (ETL) job. The ETL job will process daily incoming .csv files that users upload to an Amazon S3 bucket. The size of each S3 object is less than 100 M

Which solution will meet these requirements MOST cost-effectively?

Options:

Write a custom Python application. Host the application on an Amazon Elastic Kubernetes Service (Amazon EKS) cluster.

Write a PySpark ETL script. Host the script on an Amazon EMR cluster.

Write an AWS Glue PySpark job. Use Apache Spark to transform the data.

Write an AWS Glue Python shell job. Use pandas to transform the data.

Discussion 0

Question # 30

A company stores employee data in Amazon Redshift A table named Employee uses columns named Region ID, Department ID, and Role ID as a compound sort key. Which queries will MOST increase the speed of a query by using a compound sort key of the table? (Select TWO.)

Options:

Select * from Employee where Region ID= ' North America ' ;

Select * from Employee where Region ID= ' North America ' and Department ID=20;

Select * from Employee where Department ID=20 and Region ID= ' North America ' ;

Select " from Employee where Role ID=50;

Select * from Employee where Region ID= ' North America ' and Role ID=50;

Discussion 0

Answer:

B, C

Explanation:

In Amazon Redshift, a compound sort key is designed to optimize the performance of queries that use filtering and join conditions on the columns in the sort key. A compound sort key orders the data based on the first column, followed by the second, and so on. In the scenario given, the compound sort key consists of Region ID, Department ID, and Role I

Therefore, queries that filter on the leading columns of the sort key are more likely to benefit from this order.

Option B: " Select * from Employee where Region ID= ' North America ' and Department ID=20; " This query will perform well because it uses both the Region ID and Department ID, which are the first two columns of the compound sort key. The order of the columns in the WHERE clause matches the order in the sort key, thus allowing the query to scan fewer rows and improve performance.

Option C: " Select * from Employee where Department ID=20 and Region ID= ' North America ' ; " This query also benefits from the compound sort key because it includes both Region ID and Department ID, which are the first two columns in the sort key. Although the order in the WHERE clause does not match exactly, Amazon Redshift will still leverage the sort key to reduce the amount of data scanned, improving query speed.

Options A, D, and E are less optimal because they do not utilize the sort key as effectively:

Option A only filters by the Region ID, which may still use the sort key but does not take full advantage of the compound nature.

Option D uses only Role ID, the last column in the compound sort key, which will not benefit much from sorting since it is the third key in the sort order.

Option E filters on Region ID and Role ID but skips the Department ID column, making it less efficient for the compound sort key.

[References:, Amazon Redshift Documentation - Sorting Data, AWS Certified Data Analytics Study Guide, AWS Certification - Data Engineer Associate Exam Guide, , , , , ]

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