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AWS Certified Data Engineer - Associate (DEA-C01)

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Total Questions : 289

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Question # 41

A media company uses software as a service (SaaS) applications to gather data by using third-party tools. The company needs to store the data in an Amazon S3 bucket. The company will use Amazon Redshift to perform analytics based on the data.

Which AWS service or feature will meet these requirements with the LEAST operational overhead?

Options:

A.  

Amazon Managed Streaming for Apache Kafka (Amazon MSK)

B.  

Amazon AppFlow

C.  

AWS Glue Data Catalog

D.  

Amazon Kinesis

Discussion 0
Question # 42

A company maintains an Amazon Redshift provisioned cluster that the company uses for extract, transform, and load (ETL) operations to support critical analysis tasks. A sales team within the company maintains a Redshift cluster that the sales team uses for business intelligence (BI) tasks.

The sales team recently requested access to the data that is in the ETL Redshift cluster so the team can perform weekly summary analysis tasks. The sales team needs to join data from the ETL cluster with data that is in the sales team ' s BI cluster.

The company needs a solution that will share the ETL cluster data with the sales team without interrupting the critical analysis tasks. The solution must minimize usage of the computing resources of the ETL cluster.

Which solution will meet these requirements?

Options:

A.  

Set up the sales team Bl cluster as a consumer of the ETL cluster by using Redshift data sharing.

B.  

Create materialized views based on the sales team ' s requirements. Grant the sales team direct access to the ETL cluster.

C.  

Create database views based on the sales team ' s requirements. Grant the sales team direct access to the ETL cluster.

D.  

Unload a copy of the data from the ETL cluster to an Amazon S3 bucket every week. Create an Amazon Redshift Spectrum table based on the content of the ETL cluster.

Discussion 0
Question # 43

A company is designing a serverless data processing workflow in AWS Step Functions that involves multiple steps. The processing workflow ingests data from an external API, transforms the data by using multiple AWS Lambda functions, and loads the transformed data into Amazon DynamoD

B.  

The company needs the workflow to perform specific steps based on the content of the incoming data.

Which Step Functions state type should the company use to meet this requirement?

Options:

A.  

Parallel

B.  

Choice

C.  

Task

D.  

Map

Discussion 0
Question # 44

A data engineer needs Amazon Athena queries to finish faster. The data engineer notices that all the files the Athena queries use are currently stored in uncompressed .csv format. The data engineer also notices that users perform most queries by selecting a specific column.

Which solution will MOST speed up the Athena query performance?

Options:

A.  

Change the data format from .csvto JSON format. Apply Snappy compression.

B.  

Compress the .csv files by using Snappy compression.

C.  

Change the data format from .csvto Apache Parquet. Apply Snappy compression.

D.  

Compress the .csv files by using gzjg compression.

Discussion 0
Question # 45

A company plans to use Amazon Kinesis Data Firehose to store data in Amazon S3. The source data consists of 2 MB csv files. The company must convert the .csv files to JSON format. The company must store the files in Apache Parquet format.

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

Options:

A.  

Use Kinesis Data Firehose to convert the csv files to JSON. Use an AWS Lambda function to store the files in Parquet format.

B.  

Use Kinesis Data Firehose to convert the csv files to JSON and to store the files in Parquet format.

C.  

Use Kinesis Data Firehose to invoke an AWS Lambda function that transforms the .csv files to JSON and stores the files in Parquet format.

D.  

Use Kinesis Data Firehose to invoke an AWS Lambda function that transforms the .csv files to JSON. Use Kinesis Data Firehose to store the files in Parquet format.

Discussion 0
Question # 46

A company needs to store semi-structured transactional data for an application in a database. The database must be serverless. The application writes the data infrequently, but it reads the data frequently. The application must retrieve the data within milliseconds.

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

Options:

A.  

Store the data in an Amazon S3 Standard bucket. Enable S3 Transfer Acceleration.

B.  

Store the data in an Amazon S3 Apache Iceberg table. Enable S3 Transfer Acceleration.

C.  

Store the data in an Amazon RDS for MySQL cluster. Configure RDS Optimized Reads for the cluster.

D.  

Store the data in an Amazon DynamoDB table. Configure a DynamoDB Accelerator cache.

Discussion 0
Question # 47

A company wants to migrate data from an Amazon RDS for PostgreSQL DB instance in the eu-east-1 Region of an AWS account named Account_

A.  

The company will migrate the data to an Amazon Redshift cluster in the eu-west-1 Region of an AWS account named Account_

B.  

Which solution will give AWS Database Migration Service (AWS DMS) the ability to replicate data between two data stores?

Options:

A.  

Set up an AWS DMS replication instance in Account_B in eu-west-1.

B.  

Set up an AWS DMS replication instance in Account_B in eu-east-1.

C.  

Set up an AWS DMS replication instance in a new AWS account in eu-west-1

D.  

Set up an AWS DMS replication instance in Account_A in eu-east-1.

Discussion 0
Question # 48

A data engineer uses Amazon Kinesis Data Streams to ingest and process records that contain user behavior data from an application every day.

The data engineer notices that the data stream is experiencing throttling because hot shards receive much more data than other shards in the data stream.

How should the data engineer resolve the throttling issue?

Options:

A.  

Use a random partition key to distribute the ingested records.

B.  

Increase the number of shards in the data stream. Distribute the records across the shards.

C.  

Limit the number of records that are sent each second by the producer to match the capacity of the stream.

D.  

Decrease the size of the records that the producer sends to match the capacity of the stream.

Discussion 0
Question # 49

A company ' s application needs to search and analyze data in near real time. The application must handle up to 1,000 requests each second with low query latency. The company wants a solution that individual data teams can own and configure to meet each team ' s cost and performance optimization requirements.

Which solution will meet these requirements?

Options:

A.  

Use Amazon S3 buckets to store the data. Use Amazon Athena to query and analyze the data. Assign each data team a separate S3 bucket prefix to optimize queries.

B.  

Use streams in Amazon Kinesis Data Streams and Amazon Managed Service for Apache Flink to query and analyze the data. Assign each data team a separate stream to manage and consume.

C.  

Use Amazon OpenSearch Service clusters with indexing to query the data. Assign each data team a separate cluster to configure for storage and queries.

D.  

Use Amazon Aurora clusters that run on Aurora I/O-Optimized instances. Assign each data team a separate Aurora cluster to configure for storage and queries.

Discussion 0
Question # 50

A company has an on-premises PostgreSQL database that contains customer data. The company wants to migrate the customer data to an Amazon Redshift data warehouse. The company has established a VPN connection between the on-premises database and AWS.

The on-premises database is continuously updated. The company must ensure that the data in Amazon Redshift is updated as quickly as possible.

Which solution will meet these requirements?

Options:

A.  

Use the pg_dump utility to generate a backup of the PostgreSQL database. Use the AWS Schema Conversion Tool (AWS SCT) to upload the backup to Amazon Redshift. Set up a cron job to perform a backup. Upload the backup to Amazon Redshift every night.

B.  

Create an AWS Database Migration Service (AWS DMS) full-load task. Set Amazon Redshift as the target. Configure the task to use the change data capture (CDC) feature.

C.  

Use the pg_dump utility to generate a backup of the PostgreSQL database. Upload the backup to an Amazon S3 bucket. Use the COPY command to import the data into Amazon Redshift.

D.  

Create an AWS Database Migration Service (AWS DMS) full-load task. Set Amazon Redshift as the target. Configure the task to perform a full load of the database to Amazon Redshift every night.

Discussion 0
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