Free Amazon AWS-DEA-C01 Exam Questions

Question 1

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

Question 2

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 3

A healthcare organization stores patient records from multiple regions in an Amazon S3 bucket, which is used for regional health analysis. A data compliance team needs to ensure that health analysts can only access patient records from their specific region to comply with local privacy regulations.

Which solution will enable this requirement with the minimum amount of operational effort?

A : Import the data into an Amazon Aurora database, set up regional schemas, and create IAM policies that grant access to these schemas based on the analyst's region.

B : Implement AWS Lake Formation, register the S3 bucket as a data lake, and utilize Lake Formation's fine-grained access control to manage region-specific data access.

C : Distribute the data across multiple AWS Regions correlating to the patient locations, and implement IAM policies to grant data access to analysts within the same geographical region.

D : Segment the patient records into different S3 prefixes per region, and use IAM bucket policies to restrict access to analysts based on their regional assignment.

Answer: B

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

A company has a frontend ReactJS website that uses Amazon API Gateway to invoke REST APIs. The APIs

perform the functionality of the website. A data engineer needs to write a Python script that can be

occasionally invoked through API Gateway. The code must return results to API Gateway.

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

A : Deploy a custom Python script on an Amazon Elastic Container Service (Amazon ECS) cluster.

B : Create an AWS Lambda Python function with provisioned concurrency.

C : Deploy a custom Python script that can integrate with API Gateway on Amazon Elastic Kubernetes Service (Amazon EKS).

D : Create an AWS Lambda function. Ensure that the function is warm byscheduling an Amazon EventBridge rule to invoke the Lambda function every 5 minutes by usingmock events.

Answer: B