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AWS Certified Associate Data-Engineer-Associate Full Course Free

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Question 8

A data engineer needs to join data from multiple sources to perform a one-time analysis job. The data is stored in Amazon DynamoDB, Amazon RDS, Amazon Redshift, and Amazon S3.

Which solution will meet this requirement MOST cost-effectively?

Options:

A.

Use an Amazon EMR provisioned cluster to read from all sources. Use Apache Spark to join the data and perform the analysis.

B.

Copy the data from DynamoDB, Amazon RDS, and Amazon Redshift into Amazon S3. Run Amazon Athena queries directly on the S3 files.

C.

Use Amazon Athena Federated Query to join the data from all data sources.

D.

Use Redshift Spectrum to query data from DynamoDB, Amazon RDS, and Amazon S3 directly from Redshift.

Question 9

A financial services company stores financial data in Amazon Redshift. A data engineer wants to run real-time queries on the financial data to support a web-based trading application. The data engineer wants to run the queries from within the trading application.

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

Options:

A.

Establish WebSocket connections to Amazon Redshift.

B.

Use the Amazon Redshift Data API.

C.

Set up Java Database Connectivity (JDBC) connections to Amazon Redshift.

D.

Store frequently accessed data in Amazon S3. Use Amazon S3 Select to run the queries.

Question 10

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:

A.

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

B.

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

C.

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

D.

Reduce the scaling cooldown period for the provisioned EMR cluster.

Question 11

A retail company uses an Amazon Redshift data warehouse and an Amazon S3 bucket. The company ingests retail order data into the S3 bucket every day.

The company stores all order data at a single path within the S3 bucket. The data has more than 100 columns. The company ingests the order data from a third-party application that generates more than 30 files in CSV format every day. Each CSV file is between 50 and 70 MB in size.

The company uses Amazon Redshift Spectrum to run queries that select sets of columns. Users aggregate metrics based on daily orders. Recently, users have reported that the performance of the queries has degraded. A data engineer must resolve the performance issues for the queries.

Which combination of steps will meet this requirement with LEAST developmental effort? (Select TWO.)

Options:

A.

Configure the third-party application to create the files in a columnar format.

B.

Develop an AWS Glue ETL job to convert the multiple daily CSV files to one file for each day.

C.

Partition the order data in the S3 bucket based on order date.

D.

Configure the third-party application to create the files in JSON format.

E.

Load the JSON data into the Amazon Redshift table in a SUPER type column.

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Exam Name: AWS Certified Data Engineer - Associate (DEA-C01)
Last Update: Nov 14, 2024
Questions: 130
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