Amazon DEA-C01

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The dumps for Amazon DEA-C01 exam was last updated on May 15,2026 .

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Question#1

A data engineer uses Amazon Redshift to run resource-intensive analytics processes once every month. Every month, the data engineer creates a new Redshift provisioned cluster. The data engineer deletes the Redshift provisioned cluster after the analytics processes are complete every month. Before the data engineer deletes the cluster each month, the data engineer unloads backup data from the cluster to an Amazon S3 bucket.
The data engineer needs a solution to run the monthly analytics processes that does not require the data engineer to manage the infrastructure manually.
Which solution will meet these requirements with the LEAST operational overhead?

A. Use Amazon Step Functions to pause the Redshift cluster when the analytics processes are complete and to resume the cluster to run new processes every month.

B. Use Amazon Redshift Serverless to automatically process the analytics workload.

C. Use the AWS CLI to automatically process the analytics workload.

D. Use AWS CloudFormation templates to automatically process the analytics workload.

Explanation:
Amazon Redshift Serverless is a new feature of Amazon Redshift that enables you to run SQL queries on data in Amazon S3 without provisioning or managing any clusters. You can use Amazon Redshift Serverless to automatically process the analytics workload, as it scales up and down the compute resources based on the query demand, and charges you only for the resources consumed. This solution will meet the requirements with the least operational overhead, as it does not require the data engineer to create, delete, pause, or resume any Redshift clusters, or to manage any infrastructure manually. You can use the Amazon Redshift Data API to run queries from the AWS CLI, AWS SDK, or AWS Lambda functions12.
The other options are not optimal for the following reasons:
A. Use Amazon Step Functions to pause the Redshift cluster when the analytics processes are complete and to resume the cluster to run new processes every month. This option is not recommended, as it would still require the data engineer to create and delete a new Redshift provisioned cluster every month, which can incur additional costs and time. Moreover, this option would require the data engineer to use Amazon Step Functions to orchestrate the workflow of pausing and resuming the cluster, which can add complexity and overhead.
C. Use the AWS CLI to automatically process the analytics workload. This option is vague and does not specify how the AWS CLI is used to process the analytics workload. The AWS CLI can be used to run queries on data in Amazon S3 using Amazon Redshift Serverless, Amazon Athena, or Amazon EMR, but each of these services has different features and benefits. Moreover, this option does not address the requirement of not managing the infrastructure manually, as the data engineer may still need to provision and configure some resources, such as Amazon EMR clusters or Amazon Athena workgroups.
D. Use AWS CloudFormation templates to automatically process the analytics workload. This option is also vague and does not specify how AWS CloudFormation templates are used to process the analytics workload. AWS CloudFormation is a service that lets you model and provision AWS resources using templates. You can use AWS CloudFormation templates to create and delete a Redshift provisioned cluster every month, or to create and configure other AWS resources, such as Amazon EMR, Amazon Athena, or Amazon Redshift Serverless. However, this option does not address the requirement of not managing the infrastructure manually, as the data engineer may still need to write and maintain the AWS CloudFormation templates, and to monitor the status and performance of the resources.
1: Amazon Redshift Serverless
2: Amazon Redshift Data API
: Amazon Step Functions
: AWS CLI
: AWS CloudFormation

Question#2

A data engineer is troubleshooting an AWS Glue workflow that occasionally fails. The engineer determines that the failures are a result of data quality issues. A business reporting team needs to receive an email notification any time the workflow fails in the future.
Which solution will meet this requirement?

A. Create an Amazon Simple Notification Service (Amazon SNS) FIFO topic. Subscribe the team's email account to the SNS topic. Create an AWS Lambda function that initiates when the AWS Glue job state changes to FAILE

B. Set the SNS topic as the target.

C. Create an Amazon Simple Notification Service (Amazon SNS) standard topic. Subscribe the team's email account to the SNS topic. Create an Amazon EventBridge rule that triggers when the AWS Glue Job state changes to FAILE

D. Set the SNS topic as the target.

E. Create an Amazon Simple Queue Service (Amazon SQS) FIFO queue. Subscribe the team's email account to the SQS queue. Create an AWS Config rule that triggers when the AWS Glue job state changes to FAILE

F. Set the SQS queue as the target.

G. Create an Amazon Simple Queue Service (Amazon SQS) standard queue. Subscribe the team's email account to the SQS queue. Create an Amazon EventBridge rule that triggers when the AWS Glue job state changes to FAILE

H. Set the SQS queue as the target.

Question#3

A company uses an Amazon Redshift cluster as a data warehouse that is shared across two departments. To comply with a security policy, each department must have unique access permissions.
Department A must have access to tables and views for Department A. Department B must have access to tables and views for Department B.
The company often runs SQL queries that use objects from both departments in one query.
Which solution will meet these requirements with the LEAST operational overhead?
A. Group tables and views for each department into dedicated schemas. Manage permissions at the schema level.
B. Group tables and views for each department into dedicated databases. Manage permissions at the database level.
C. Update the names of the tables and views to follow a naming convention that contains the department names. Manage permissions based on the new naming convention.
D. Create an IAM user group for each department. Use identity-based IAM policies to grant table and view permissions based on the IAM user group.

A. A

Question#4

A company is using Amazon S3 to build a data lake. The company needs to replicate records from multiple source databases into Apache Parquet format.
Most of the source databases are hosted on Amazon RDS. However, one source database is an on-premises Microsoft SQL Server Enterprise instance. The company needs to implement a solution to replicate existing data from all source databases and all future changes to the target S3 data lake.
Which solution will meet these requirements MOST cost-effectively?

A. Use one AWS Glue job to replicate existing data. Use a second AWS Glue job to replicate future changes.

B. Use AWS Database Migration Service (AWS DMS) to replicate existing data. Use AWS Glue jobs to replicate future changes.

C. Use AWS Database Migration Service (AWS DMS) to replicate existing data and future changes.

D. Use AWS Glue jobs to replicate existing data. Use Amazon Kinesis Data Streams to replicate future changes.

Explanation:
AWS Database Migration Service (AWS DMS) is purpose-built to migrate and continuously replicate data from both AWS-hosted and on-premises databases. It supports full-load (existing data) and change data capture (CDC) for ongoing changes, making it the most cost-effective and operationally simple solution in this scenario.
“DMS supports both full-load and continuous replication via CDC. This enables replicating existing and future data from various sources to a data lake in Amazon S3.”
C Ace the AWS Certified Data Engineer - Associate Certification - version 2 - apple.pdf AWS Glue is not suitable for real-time CDC replication across hybrid environments.

Question#5

A data engineer needs to build an extract, transform, and load (ETL) job. The ETL job will process daily incoming .csv files that users upload to an Amazon S3 bucket. The size of each S3 object is less than 100 MB.
Which solution will meet these requirements MOST cost-effectively?

A. Write a custom Python application. Host the application on an Amazon Elastic Kubernetes Service (Amazon EKS) cluster.

B. Write a PySpark ETL script. Host the script on an Amazon EMR cluster.

C. Write an AWS Glue PySpark job. Use Apache Spark to transform the data.

D. Write an AWS Glue Python shell job. Use pandas to transform the data.

Explanation:
AWS Glue is a fully managed serverless ETL service that can handle various data sources and formats, including .csv files in Amazon S3. AWS Glue provides two types of jobs: PySpark and Python shell. PySpark jobs use Apache Spark to process large-scale data in parallel, while Python shell jobs use Python scripts to process small-scale data in a single execution environment. For this requirement, a Python shell job is more suitable and cost-effective, as the size of each S3 object is less than 100 MB, which does not require distributed processing. A Python shell job can use pandas, a popular Python library for data analysis, to transform the .csv data as needed. The other solutions are not optimal or relevant for this requirement. Writing a custom Python application and hosting it on an Amazon EKS cluster would require more effort and resources to set up and manage the Kubernetes environment, as well as to handle the data ingestion and transformation logic. Writing a PySpark ETL script and hosting it on an Amazon EMR cluster would also incur more costs and complexity to provision and configure the EMR cluster, as well as to use Apache Spark for processing small data files. Writing an AWS Glue PySpark job would also be less efficient and economical than a Python shell job, as it would involve unnecessary overhead and charges for using Apache Spark for small data files.
Reference: AWS Glue
Working with Python Shell Jobs
pandas
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Exam Code: Amazon DEA-C01 Q & A: 294 Q&As Updated: May 15,2026

Full Amazon DEA-C01 Exam Dumps Here

Exam Code: Amazon DEA-C01
Q & A: 294 Q&As
Updated: May 15,2026

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