The AWS Certified Data Engineer - Associate exam validates your ability to design, build, and manage data solutions on Amazon Web Services. This certification is ideal for professionals who work with data pipelines, storage systems, and data governance in cloud environments. The Amazon-DEA-C01 exam tests both foundational knowledge and practical decision-making across data engineering workflows. This page guides you through the exam syllabus, question formats, and a structured study approach to help you prepare effectively.
Use this topic map to guide your study for Amazon-DEA-C01 (AWS Certified Data Engineer - Associate) within the AWS Certified Data Engineer Associate path.
The Amazon-DEA-C01 exam uses multiple question types to assess both conceptual understanding and practical reasoning. Questions progress in difficulty and emphasize real-world application of data engineering principles.
An effective study plan breaks the four domains into manageable weekly goals and reinforces connections between topics. Dedicate time to both conceptual learning and hands-on practice to build confidence and speed.
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Data Ingestion and Transformation and Data Store Management typically represent the largest portion of the exam, reflecting their importance in real-world data engineering. However, all four domains are tested, and questions often blend concepts across multiple areas. Allocate study time proportionally but ensure you have solid coverage of every topic.
Data flows through a complete lifecycle: you ingest data from sources (Data Ingestion and Transformation), store it in appropriate systems (Data Store Management), keep pipelines running reliably (Data Operations and Support), and protect it with security and governance controls (Data Security and Governance). Understanding these connections helps you answer scenario questions and design solutions that work end-to-end.
Build and deploy at least one complete data pipeline using AWS services like AWS Glue, Amazon S3, and Amazon RDS or Redshift. Practice configuring IAM policies, encryption, and monitoring. Labs that cover failure scenarios and troubleshooting are especially valuable because the exam tests your ability to diagnose and resolve real problems.
Common errors include choosing the cheapest solution instead of the best fit for the use case, overlooking security and compliance requirements, and misunderstanding when to use batch versus streaming ingestion. Read each scenario carefully, identify all constraints (cost, performance, security, compliance), and eliminate options that violate any of them.
Review your practice test results to identify topics where you scored below 80 percent. Spend 60 percent of your time on weak areas and 40 percent on reinforcing strong ones. Take one final timed practice test three days before the exam, then use the last few days for light review and rest. Avoid cramming new material the night before; focus on staying calm and reviewing key definitions.
A company implements a data mesh that has a central governance account. The company needs to catalog all data in the governance account. The governance account uses AWS Lake Formation to centrally share data and grant access permissions.
The company has created a new data product that includes a group of Amazon Redshift Serverless tables. A data engineer needs to share the data product with a marketing team. The marketing team must have access to only a subset of columns. The data engineer needs to share the same data product with a compliance team. The compliance team must have access to a different subset of columns than the marketing team needs access to.
Which combination of steps should the data engineer take to meet these requirements? (Select TWO.)
The company is using a data mesh architecture with AWS Lake Formation for governance and needs to share specific subsets of data with different teams (marketing and compliance) using Amazon Redshift Serverless.
Option A: Create views of the tables that need to be shared. Include only the required columns.Creating views in Amazon Redshift that include only the necessary columns allows for fine-grained access control. This method ensures that each team has access to only the data they are authorized to view.
Option E: Share the Amazon Redshift data share to the Amazon Redshift Serverless workgroup in the marketing team's account.Amazon Redshift data sharing enables live access to data across Redshift clusters or Serverless workgroups. By sharing data with specific workgroups, you can ensure that the marketing team and compliance team each access the relevant subset of data based on the views created.
Option B (creating a Redshift data share) is close but does not address the fine-grained column-level access.
Option C (creating a managed VPC endpoint) is unnecessary for sharing data with specific teams.
Option D (sharing with the Lake Formation catalog) is incorrect because Redshift data shares do not integrate directly with Lake Formation catalogs; they are specific to Redshift workgroups.
Amazon Redshift Data Sharing
AWS Lake Formation Documentation
A university is developing an educational application that analyzes student essays. The application provides personalized feedback with accurate citations to the university's textbooks. The application needs to process essays in multiple languages. Application responses must include direct references to specific sections in the course materials and must be in the student's selected language.
Which solution will meet these requirements with the LEAST operational overhead?
Option B is correct because Amazon Bedrock Knowledge Bases is the managed AWS service for retrieval-augmented generation using an organization's own content. AWS states that the RetrieveAndGenerate flow queries a knowledge base, generates responses based on retrieved results, and that the response includes citations only for relevant sources. AWS user guide pages also state that Knowledge Bases can return natural-language responses based on retrieved chunks from source documents. This directly fits the requirement for feedback with direct references to textbook sections.
This is also the least operational overhead choice because Amazon Bedrock manages ingestion, chunking, embedding, retrieval, and generation workflow components. Option A would require building and operating a custom vector solution. Option C adds multiple services and custom glue logic. Option D requires custom model hosting and fine-tuning, which is far more operationally heavy. For multilingual processing, Amazon Bedrock supports multilingual-capable models and multilingual embedding options, making it suitable for responses in the student's selected language when paired with an appropriate model.
A company is designing a serverless data processing workflow in AWS Step Functions that involves multiple steps. The processing workflow ingests data from an external API, transforms the data by using multiple AWS Lambda functions, and loads the transformed data into Amazon DynamoDB.
The company needs the workflow to perform specific steps based on the content of the incoming data.
Which Step Functions state type should the company use to meet this requirement?
The Choice state type in AWS Step Functions is designed to perform branching logic, i.e., routing execution to different paths based on conditions in the input data.
''The Step Functions Choice state lets you branch the execution flow depending on values in the state's input. This allows you to run different processing logic based on dynamic conditions like values in the input JSON.''
-- Ace the AWS Certified Data Engineer - Associate Certification - version 2 - apple.pdf
This makes Choice the correct answer for content-driven conditional workflows.
A company stores raw clickstream data in an Amazon S3 bucket. The company needs a solution to process the data every day by using complex PySpark transformations that rely on custom internal libraries. After the data is transformed, the company must store the data in Amazon Redshift for analytics. The solution must be highly scalable to handle large data workloads.
Which solution will meet these requirements with the LEAST operational overhead?
Option A is correct because AWS Glue is a serverless ETL service built to run PySpark workloads at scale with minimal infrastructure management. AWS documentation states that you can install additional Python modules and libraries for use with AWS Glue ETL jobs, including by using the --additional-python-modules parameter and Amazon S3 paths for wheel artifacts or other supported package delivery methods. That directly addresses the requirement for custom internal libraries. Since the data is already in Amazon S3 and the result must be loaded into Amazon Redshift, Glue is a natural low-overhead fit for this daily transformation pipeline.
Option B and C require managing compute infrastructure or cluster lifecycle, which increases operational overhead. Option D is not the best fit because SageMaker Processing is designed primarily for ML-oriented data preparation, not as the standard AWS service for large-scale ETL into Redshift. The question explicitly asks for least operational overhead with scalable PySpark and custom libraries, and AWS Glue provides exactly that managed capability.
A company uses Amazon S3 to store semi-structured data in a transactional data lake. Some of the data files are small, but other data files are tens of terabytes.
A data engineer must perform a change data capture (CDC) operation to identify changed data from the data source. The data source sends a full snapshot as a JSON file every day and ingests the changed data into the data lake.
Which solution will capture the changed data MOST cost-effectively?
An open source data lake format, such as Apache Parquet, Apache ORC, or Delta Lake, is a cost-effective way to perform a change data capture (CDC) operation on semi-structured data stored in Amazon S3. An open source data lake format allows you to query data directly from S3 using standard SQL, without the need to move or copy data to another service. An open source data lake format also supports schema evolution, meaning it can handle changes in the data structure over time. An open source data lake format also supports upserts, meaning it can insert new data and update existing data in the same operation, using a merge command. This way, you can efficiently capture the changes from the data source and apply them to the S3 data lake, without duplicating or losing any data.
The other options are not as cost-effective as using an open source data lake format, as they involve additional steps or costs. Option A requires you to create and maintain an AWS Lambda function, which can be complex and error-prone. AWS Lambda also has some limits on the execution time, memory, and concurrency, which can affect the performance and reliability of the CDC operation. Option B and D require you to ingest the data into a relational database service, such as Amazon RDS or Amazon Aurora, which can be expensive and unnecessary for semi-structured data. AWS Database Migration Service (AWS DMS) can write the changed data to the data lake, but it also charges you for the data replication and transfer. Additionally, AWS DMS does not support JSON as a source data type, so you would need to convert the data to a supported format before using AWS DMS.Reference:
What is a data lake?
Choosing a data format for your data lake
Using the MERGE INTO command in Delta Lake
[AWS Lambda quotas]
[AWS Database Migration Service quotas]
