Amazon Data-Engineer-Associate real exam prep : AWS Certified Data Engineer - Associate (DEA-C01)

  • Exam Code: Data-Engineer-Associate
  • Exam Name: AWS Certified Data Engineer - Associate (DEA-C01)
  • Updated: Jul 21, 2026
  • Q&As: 305 Questions and Answers

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Amazon Data-Engineer-Associate Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: Data Operations and Support22%- Monitor data pipelines
  • 1. Amazon CloudWatch
  • 2. Logging and metrics
  • 3. AWS CloudTrail
- Manage and troubleshoot data processes
  • 1. Cost optimization
  • 2. Performance tuning
  • 3. Debugging failed jobs
- Automate data pipelines
  • 1. AWS Lambda triggers
  • 2. Scheduling jobs
  • 3. Event-driven triggers
Topic 2: Data Ingestion and Transformation34%- Apply programming concepts
  • 1. Infrastructure as Code (IaC)
  • 2. Version control
  • 3. SQL, Python, Scala
- Transform and process data
  • 1. Data partitioning and compression
  • 2. Data transformation services (AWS Glue, Amazon EMR, AWS Lambda)
  • 3. ETL/ELT patterns
  • 4. Batch and stream processing
- Perform data ingestion
  • 1. Data ingestion patterns (frequency and data history)
  • 2. Streaming data ingestion
  • 3. Batch data ingestion (scheduled ingestion, event-driven ingestion)
  • 4. Replayability of data
  • 5. Throughput and latency characteristics for AWS services
- Orchestrate data pipelines
  • 1. AWS Glue Workflows
  • 2. Event-driven architectures
  • 3. Amazon Managed Workflows for Apache Airflow (MWAA)
  • 4. AWS Step Functions
Topic 3: Data Security and Governance18%- Implement data quality checks
  • 1. AWS Glue DataBrew
  • 2. Data validation
- Manage data privacy and compliance
  • 1. PII data handling
  • 2. Data masking and tokenization
  • 3. AWS Lake Formation permissions
- Apply authentication and authorization
  • 1. Service control policies (SCPs)
  • 2. AWS IAM policies and roles
  • 3. Amazon S3 bucket policies
- Ensure data encryption
  • 1. Encryption at rest and in transit
  • 2. AWS KMS
Topic 4: Data Store Management26%- Choose a data store
  • 1. Data lakes vs. data warehouses
  • 2. Data characteristics (structured, semi-structured, unstructured)
  • 3. Access and storage patterns
  • 4. Amazon S3, Amazon RDS, Amazon DynamoDB, Amazon Redshift
- Design data models
  • 1. Partitioning and indexing strategies
  • 2. Schema design
  • 3. Normalization and denormalization
- Understand data cataloging
  • 1. Data discovery and classification
  • 2. Schema evolution
  • 3. AWS Glue Data Catalog
- Manage data lifecycle
  • 1. Amazon S3 storage classes
  • 2. Data archiving
  • 3. Data retention policies

Amazon AWS Certified Data Engineer - Associate (DEA-C01) Sample Questions:

1. A data engineer needs to join data from multiple sources to perform a one-time analysis job. The data is stored in Amazon DynamoDB, Amazon RDS, Amazon Redshift, and Amazon S3.
Which solution will meet this requirement MOST cost-effectively?

A) Use an Amazon EMR provisioned cluster to read from all sources. Use Apache Spark to join the data and perform the analysis.
B) Use Redshift Spectrum to query data from DynamoDB, Amazon RDS, and Amazon S3 directly from Redshift.
C) Use Amazon Athena Federated Query to join the data from all data sources.
D) Copy the data from DynamoDB, Amazon RDS, and Amazon Redshift into Amazon S3. Run Amazon Athena queries directly on the S3 files.


2. A data engineer is configuring Amazon SageMaker Studio to use AWS Glue interactive sessions to prepare data for machine learning (ML) models.
The data engineer receives an access denied error when the data engineer tries to prepare the data by using SageMaker Studio.
Which change should the engineer make to gain access to SageMaker Studio?

A) Add the AmazonSageMakerFullAccess managed policy to the data engineer ' s IAM user.
B) Add a policy to the data engineer ' s IAM user that allows the sts:AddAssociation action for the AWS Glue and SageMaker service principals in the trust policy.
C) Add a policy to the data engineer ' s IAM user that includes the sts:AssumeRole action for the AWS Glue and SageMaker service principals in the trust policy.
D) Add the AWSGlueServiceRole managed policy to the data engineer ' s IAM user.


3. A company has a data warehouse in Amazon Redshift. The Amazon Redshift provisioned cluster is created in a VPC. The company is developing a new application in AWS Lambda that needs to access the data from Amazon Redshift. The company security policy states that AWS services can access the Amazon Redshift cluster only from the AWS network. Traffic between Lambda and the Amazon Redshift Data API must remain in the AWS network.
Which solution will meet these requirements?

A) Use the Data API in the Lambda function to access the data. Set up an Amazon VPC endpoint for the Data API.
B) Use the Data API in the Lambda function to access the data. Set up an Amazon VPC endpoint for the Lambda function.
C) Connect to the Amazon Redshift cluster from the Lambda function by using an Amazon Redshift ODBC driver. Set up an Amazon VPC endpoint for the Lambda function.
D) Connect to the Amazon Redshift cluster from the Lambda function by using an Amazon Redshift JDBC driver. Set up an Amazon VPC endpoint for the Lambda function.


4. A data engineer is building an automated extract, transform, and load (ETL) ingestion pipeline by using AWS Glue. The pipeline ingests compressed files that are in an Amazon S3 bucket. The ingestion pipeline must support incremental data processing.
Which AWS Glue feature should the data engineer use to meet this requirement?

A) Workflows
B) Classifiers
C) Job bookmarks
D) Triggers


5. A company has a data warehouse in Amazon Redshift. To comply with security regulations, the company needs to log and store all user activities and connection activities for the data warehouse.
Which solution will meet these requirements?

A) Create an Amazon Aurora MySQL database. Enable logging for the Amazon Redshift cluster. Write the logs to a table in the Aurora MySQL database.
B) Create an Amazon Elastic Block Store (Amazon EBS) volume. Enable logging for the Amazon Redshift cluster. Write the logs to the EBS volume.
C) Create an Amazon Elastic File System (Amazon EFS) file system. Enable logging for the Amazon Redshift cluster. Write logs to the EFS file system.
D) Create an Amazon S3 bucket. Enable logging for the Amazon Redshift cluster. Specify the S3 bucket in the logging configuration to store the logs.


Solutions:

Question # 1
Answer: C
Question # 2
Answer: C
Question # 3
Answer: A
Question # 4
Answer: C
Question # 5
Answer: D

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