Free Amazon AWS-DEA-C01 Exam Questions

Question 1

A Cloud Data Engineer is designing a serverless data processing pipeline on AWS. The pipeline uses AWS Lambda for data transformation, AWS Step Functions for workflow management, and Amazon DynamoDB for storing intermediate data. The engineer decides to use the AWS Serverless Application Model (AWS SAM) to manage the deployment of these components. The primary requirement is to ensure that the deployment process is repeatable and easily modifiable.

What should the engineer include in the SAM template to meet these requirements efficiently?

A : Include the Lambda functions and DynamoDB tables in the SAM template, but exclude the Step Functions, as they are not supported by AWS SAM.

B : Specify only the Lambda function in the SAM template, manually configuring the Step Functions and DynamoDB tables through the AWS Console.

C : Use separate SAM templates for each component (Lambda, Step Functions, DynamoDB) to manage deployments individually for better control.

D : Define all the components (Lambda functions, Step Functions, DynamoDB tables) in the SAM template, and use AWS CloudFormation to deploy the entire pipeline

Answer: D

Question 2

A company receives test results from testing facilities that are located around the world. The company storesthe test results in millions of 1 KB JSON files in an Amazon S3 bucket. A data engineer needs to process thefiles, convert them into Apache Parquet format, and load them into Amazon Redshift tables. The dataengineer uses AWS Glue to process the files, AWS Step Functions to orchestrate the processes, and AmazonEventBridge to schedule jobs.The company recently added more testing facilities. The time required to process files is increasing. The dataengineer must reduce the data processing time.Which solution will MOST reduce the data processing time?

A : Use AWS Lambda to group the raw input files into larger files. Write the larger files back to Amazon S3. Use AWS Glue to process the files. Load the files into the Amazon Redshift tables.

B : Use the AWS Glue dynamic frame file-grouping option to ingest the raw input files. Process the files. Load the files into the Amazon Redshift tables.

C : Use the Amazon Redshift COPY command to move the raw input files from Amazon S3 directly into the Amazon Redshift tables. Process the files in Amazon Redshift.

D : Use Amazon EMR instead of AWS Glue to group the raw input files. Process the files in Amazon EMR. Load the files into the Amazon Redshift tables.

Answer: B

Question 3

A company uses Amazon S3 as a data lake. The company sets up a data warehouse by using a multi-nodeAmazon Redshift cluster. The company organizes the data files in the data lake based on the data source ofeach data file.The company loads all the data files into one table in the Redshift cluster by using a separate COPY commandfor each data file location. This approach takes a long time to load all the data files into the table. Thecompany must increase the speed of the data ingestion. The company does not want to increase the cost of theprocess.Which solution will meet these requirements?

A : Use a provisioned Amazon EMR cluster to copy all the data files into one folder. Use a COPY command to load the data into Amazon Redshift.

B : Load all the data files in parallel into Amazon Aurora. Run an AWS Glue job to load the data into Amazon Redshift.

C : Use an AWS Glue job to copy all the data files into one folder. Use a COPY command to load the data into Amazon Redshift.

D : Create a manifest file that contains the data file locations. Use a COPY command to load the data into Amazon Redshift.

Answer: D

Question 4

A company has three subsidiaries. Each subsidiary uses a different data warehousing solution. The firstsubsidiary hosts its data warehouse in Amazon Redshift. The second subsidiary uses Teradata Vantage onAWS. The third subsidiary uses Google BigQuery.The company wants to aggregate all the data into a central Amazon S3 data lake. The company wants to useApache Iceberg as the table format.A data engineer needs to build a new pipeline to connect to all the data sources, run transformations by usingeach source engine, join the data, and write the data to Iceberg.Which solution will meet these requirements with the LEAST operational effort?

A : Use native Amazon Redshift, Teradata, and BigQuery connectors to build the pipeline in AWS Glue. Use native AWS Glue transforms to join the data. Run a Merge operation on the data lake Iceberg table.

B : Use the Amazon Athena federated query connectors for Amazon Redshift, Teradata, and BigQuery to build the pipeline in Athena. Write a SQL query to read from all the data sources, join the data, and run a Merge operation on the data lake Iceberg table

C : Use the native Amazon Redshift connector, the Java Database Connectivity (JDBC) connector for Teradata, and the open source Apache Spark BigQuery connector to build the pipeline in Amazon EMR. Write code in PySpark to join the data. Run a Merge operation on the data lake Iceberg table.

D : Use the native Amazon Redshift, Teradata, and BigQuery connectors in Amazon Appflow to write data to Amazon S3 and AWS Glue Data Catalog. Use Amazon Athena to join the data. Run a Merge operation on the data lake Iceberg table.

Answer: B

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