Free Amazon AWS-DEA-C01 Exam Questions

Question 1

A Cloud Data Engineering Team is configuring access control for their company's new multi-account AWS environment. They have a centralized security account and require the ability to allow audit teams to access AWS resources across all accounts for monitoring and compliance purposes. The data engineering team needs to establish a mechanism to grant audit team members from the security account least-privilege access to the necessary resources without creating individual IAM users in each account.

Which of the following steps should the data engineering team implement to set up IAM roles effectively for this requirement? (Select TWO)

A : Enable AWS Organizations and use Service Control Policies (SCPs) to grant the audit team access to the necessary resources across all accounts within the organization.

B : Set up AWS Resource Access Manager (RAM) to share resources with the audit team members from the security account, permitting cross-account resource access without IAM roles.

C : Create cross-account IAM roles in each account with permissions policies granting the necessary access, and establish trust relationships to allow the audit team members from the security account to assume these roles.

D : Provision a dedicated IAM user for each audit team member in the security account and assign inline policies to each user that grant permissions to access resources in other accounts.

E : Implement a single IAM role in the security account with permission to assume other IAM roles in the member accounts, allowing the audit team members to switch roles as needed.

Answer: C,E

Question 2

A retail company's Data Engineering Team is tasked with refining their Amazon Redshift cluster to facilitate analysis in a separate testing environment, utilizing the primary cluster's data. The new testing process will engage the secondary cluster for a duration of 3 hours biweekly. The team is evaluating the most cost-effective solution to meet this requirement.

Which method would be the most economical for the company to implement?

A : Export data biweekly from the primary Redshift cluster to Amazon S3. Access this data from the Redshift test cluster via Amazon Redshift Spectrum.

B : Establish a data share between the primary Redshift cluster and the test Redshift cluster, utilizing Amazon Redshift Serverless for the latter.

C : Generate a manual snapshot of the primary Redshift cluster every two weeks and restore this to the test cluster, which should mirror the main cluster's node configuration.

D : Program an operation to transfer data from the primary Redshift cluster to Amazon S3 every two weeks, followed by configuring an AWS Glue job to import the data into the Redshift test cluster.

Answer: B

Question 3

As a Data Engineer, you are tasked with setting up a batch data ingestion pipeline for a substantial financial dataset. This data is currently housed in an Amazon RDS database, stemming from various financial services and trading platforms. The dataset is complex and heterogeneous, predominantly in JSON format, and is updated nightly, typically ranging between 100-200 GB.

Your primary objective is to efficiently transfer this data from the RDS database to a data lake in AWS, ensuring reliable storage and preparing the dataset for analytical processing with minimal yet capable transformation steps due to its complexity.

Which combination of AWS services would be most appropriate?

A : Amazon AppFlow to facilitate the data transfer from Amazon RDS to Amazon S3. Afterward, utilize AWS Glue ETL for the data transformation processes.

B : Use AWS Database Migration Service (AWS DMS) to continuously replicate data from the Amazon RDS database to Amazon S3. Then, employ AWS Lambda for performing the necessary data transformations.

C : Configure AWS DataSync to transfer the JSON data nightly from the RDS database to Amazon S3. Subsequently, use AWS Glue ETL service to perform the necessary data transformations.

D : Use AWS Database Migration Service (AWS DMS) to continuously replicate data to Amazon S3. Configure an AWS Glue Crawler to update the schema in the Glue Data Catalog, and use Amazon Glue ETL for transformation jobs.

Answer: D

Question 4

A data engineer has two datasets that contain sales information for multiple cities and states. One dataset is named reference, and the other dataset is named primary. The data engineer needs a solution to determine whether a specific set of values in the city and state columns of the primary dataset exactly match the same specific values in the reference dataset. The data engineer wants to use Data Quality Definition Language (DQDL) rules in an AWS Glue Data Quality job. Which rule will meet these requirements?

A : DatasetMatch "reference" "city->ref_city, state->ref_state" = 1.0

B : ReferentialIntegrity "city,state" "reference.{ref_city,ref_state}" = 1.0

C : DatasetMatch "reference" "city->ref_city, state->ref_state" = 100

D : ReferentialIntegrity "city,state" "reference.{ref_city,ref_state}" = 100

Answer: A

Question 5

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