Free Amazon AWS-DEA-C01 Exam Questions

Question 1

An analyst is using Amazon Athena and needs to create a new table in the Parquet format with SNAPPY compression from an existing table named old_table. The task requires the correct syntax for a Create Table As Select (CTAS) statement in Athena. Which of the following SQL statements correctly achieves this objective?

A : CREATE NEW TABLE new_tableFROM old_tableWITH FORMAT AS 'Parquet' AND COMPRESSION AS 'SNAPPY';

B : CREATE TABLE new_tableSET FORMAT = 'Parquet', COMPRESSION = 'SNAPPY'AS SELECT *FROM old_table;

C : CREATE TABLE new_table ASSELECT *FROM old_tableUSING ('format' = 'Parquet', 'compression' = 'SNAPPY');

D : CREATE TABLE new_tableWITH ( format = 'Parquet', write_compression = 'SNAPPY')AS SELECT *FROM old_table;

Answer: D

Question 2

A telecommunications company collects network usage data throughout each day at a rate of several thousanddata points each second. The company runs an application to process the usage data in real time. Thecompany aggregates and stores the data in an Amazon Aurora DB instance.Sudden drops in network usage usually indicate a network outage. The company must be able to identifysudden drops in network usage so the company can take immediate remedial actions.Which solution will meet this requirement with the LEAST latency?

A : Create an AWS Lambda function to query Aurora for drops in network usage. Use Amazon EventBridge to automatically invoke the Lambda function every minute.

B : Modify the processing application to publish the data to an Amazon Kinesis data stream. Create an Amazon Managed Service for Apache Flink (previously known as Amazon Kinesis Data Analytics) application to detect drops in network usage.

C : Replace the Aurora database with an Amazon DynamoDB table. Create an AWS Lambda function to query the DynamoDB table for drops in network usage every minute. Use DynamoDB Accelerator (DAX) between the processing application and DynamoDB table.

D : Create an AWS Lambda function within the Database Activity Streams feature of Aurora to detect drops in network usage.

Answer: B

Question 3

A data engineer wants to orchestrate a set of extract, transform, and load (ETL) jobs that run on AWS. TheETL jobs contain tasks that must run Apache Spark jobs on Amazon EMR, make API calls to Salesforce, andload data into Amazon Redshift.The ETL jobs need to handle failures and retries automatically. The data engineer needs to use Python toorchestrate the jobs.Which service will meet these requirements?

A : Amazon Managed Workflows for Apache Airflow (Amazon MWAA)

B : AWS Step Functions

C : AWS Glue

D : Amazon EventBridge

Answer: A

Question 4

A company wants to migrate an application and an on-premises Apache Kafka server to AWS. Theapplication processes incremental updates that an on-premises Oracle database sends to the Kafka server. Thecompany wants to use the replatform migration strategy instead of the refactor strategy.Which solution will meet these requirements with the LEAST management overhead?

A : Amazon Kinesis Data Streams

B : Amazon Managed Streaming for Apache Kafka (Amazon MSK) provisioned cluster

C : Amazon Data Firehose

D : Amazon Managed Streaming for Apache Kafka (Amazon MSK) Serverless

Answer: D

Question 5

Your organization has deployed a series of IoT devices across its facilities to monitor environmental conditions. These devices send telemetry data every few seconds. As part of the data pipeline, you have been tasked with architecting a solution that ingests this streaming data, provides the ability to perform real-time analytics, and subsequently batches the data for storage in Amazon Redshift for further analysis. The solution should be scalable, manage large bursts of data effectively, and ensure that analytics can be performed promptly.

As a Cloud Data Engineering Consultant, which combination of AWS services would you employ to meet these requirements? (Select THREE)

A : Utilize Amazon DynamoDB to store and quickly retrieve data, which provides low latency access and flexibility to handle varying workloads.

B : Utilize Amazon Kinesis Data Firehose to batch and load the processed streaming data directly into Amazon Redshift.

C : Implement AWS Lambda functions to transform the data on the fly as it arrives on Kinesis Data Streams before sending it to Kinesis Data Firehose.

D : Deploy an Amazon Kinesis Data Streams application to collect streaming telematics data, enabling real-time analytics and quick response to data.

E : Configure Amazon QuickSight to connect to the Kinesis Data Stream for real-time analytics dashboards and visualizations.

F : Set up a scheduled Amazon Elastic MapReduce (EMR) job to process data, offering robust data transformation capabilities for complex analytics tasks.

Answer: B,C,D