The AWS Certified Data Engineer - Associate (Amazon-DEA-C01) exam validates your ability to design, build, and maintain data pipelines and analytics solutions on Amazon Web Services. This certification is ideal for data engineers who work with AWS data services to ingest, transform, store, and secure data at scale. This page provides a clear roadmap of the exam syllabus, question formats, and practical preparation strategies to help you study efficiently and build confidence. Whether you are new to AWS data services or advancing your career, understanding the core topics and exam structure is essential for success.
Use this topic map to guide your study for Amazon 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 foundational knowledge and the ability to apply that knowledge to real-world data engineering scenarios. Questions progress in difficulty and require you to think critically about trade-offs and best practices.
Questions reflect production-level challenges, so hands-on experience with AWS services and familiarity with common data engineering workflows significantly improve your ability to answer correctly under time pressure.
An effective study plan maps the four core topics to a structured weekly schedule, combines hands-on practice with question review, and includes timed practice tests to build confidence. Allocate more study time to areas where you have less experience, and regularly revisit weak topics to reinforce understanding.
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Data Ingestion and Transformation, and Data Store Management typically account for a larger portion of the exam questions because they form the foundation of most data engineering work. However, Data Operations and Support and Data Security and Governance are equally important for real-world job performance and will be tested thoroughly. Balance your study time across all four domains, but allocate extra practice to ingestion and storage if you are less familiar with those services.
In practice, data flows through all four domains: you ingest raw data from various sources (Data Ingestion and Transformation), store it in an optimized location (Data Store Management), monitor and maintain the pipeline (Data Operations and Support), and apply encryption and access controls throughout (Data Security and Governance). Understanding these connections helps you answer scenario questions because you can see the full picture rather than treating each topic in isolation.
AWS recommends at least one year of experience designing and implementing data solutions on AWS. However, candidates with strong foundational knowledge of data engineering concepts and a few months of hands-on lab work with AWS Glue, S3, and Redshift can also succeed. Prioritize building experience with services that appear most frequently in the exam domains: AWS Glue for transformation, S3 for storage, and CloudWatch for operations.
Many candidates choose answers based on general AWS knowledge rather than reading the specific scenario details. Others confuse similar services (for example, Kinesis Data Streams vs. Kinesis Data Firehose) or overlook security and governance requirements that are buried in the scenario. Slow down to identify the key constraints in each question, re-read the scenario before answering, and always consider security implications.
Review your weak topic areas identified during practice tests, complete one full-length timed mock exam, and spend time on scenario-based questions rather than memorizing facts. In the last few days, skim AWS service comparison charts and refresh your memory on common configuration parameters. Ensure you are well-rested and familiar with the exam interface and time limits so you can focus entirely on the questions on exam day.
A data engineer uses Amazon Kinesis Data Streams to ingest and process records that contain user behavior data from an application every day.
The data engineer notices that the data stream is experiencing throttling because hot shards receive much more data than other shards in the data stream.
How should the data engineer resolve the throttling issue?
A company runs an extract, transform, and load (ETL) job in AWS Glue. The job processes personally identifiable information (PII) data and writes logs to an Amazon CloudWatch Logs log group. A data engineer needs to mask PII data in the CloudWatch Logs log group.
Which solution will meet these requirements?
Option B is the right approach because the requirement is to mask PII in the log destination (the CloudWatch Logs log group). The exam guide explicitly calls out security responsibilities that include ''data encryption and masking'' and also emphasizes enabling and preparing logs for audit and governance needs. A log-group--level masking mechanism is therefore the most direct control point to prevent sensitive values from being exposed to anyone who can view logs.
Option A (Glue security configuration) is primarily used to apply protections such as encryption settings for Glue jobs and related outputs; it does not inherently solve the problem of masking PII that has already been emitted into application logs. Option C (Macie) is intended to discover and classify sensitive data---most commonly in Amazon S3---and to produce findings; it is not a log-masking control for CloudWatch Logs. The material reinforces Macie as a discovery/classification service for PII rather than a masking mechanism. Option D adds custom code paths in the ETL job and still may miss PII that appears in framework/system logs; it also violates the ''least operational effort'' spirit compared to applying a centralized log policy.
Therefore, attaching a data protection policy directly to the CloudWatch Logs log group best meets the masking requirement.
A data engineer is using an AWS Glue ETL job to remove outdated customer records from a table that contains customer account information. The data engineer is using the following SQL command:
MERGE INTO accounts t USING monthly_accounts_update s
ON t.customer = s.customer
WHEN MATCHED THEN DELETE
What will happen when the data engineer runs the SQL command?
In AWS Glue's SQL implementation (Spark SQL-compatible), the MERGE INTO statement supports conditional actions.
The clause WHEN MATCHED THEN DELETE deletes matching records from the target table (accounts) where the join condition is true.
''A MERGE INTO statement can perform updates, inserts, or deletes based on the match condition between source and target tables.''
-- Ace the AWS Certified Data Engineer - Associate Certification - version 2 - apple.pdf
A company stores details about transactions in an Amazon S3 bucket. The company wants to log all writes to the S3 bucket into another S3 bucket that is in the same AWS Region.
Which solution will meet this requirement with the LEAST operational effort?
This solution meets the requirement of logging all writes to the S3 bucket into another S3 bucket with the least operational effort. AWS CloudTrail is a service that records the API calls made to AWS services, including Amazon S3. By creating a trail of data events, you can capture the details of the requests that are made to the transactions S3 bucket, such as the requester, the time, the IP address, and the response elements. By specifying an empty prefix and write-only events, you can filter the data events to only include the ones that write to the bucket. By specifying the logs S3 bucket as the destination bucket, you can store the CloudTrail logs in another S3 bucket that is in the same AWS Region. This solution does not require any additional coding or configuration, and it is more scalable and reliable than using S3 Event Notifications and Lambda functions.Reference:
Logging Amazon S3 API calls using AWS CloudTrail
Creating a trail for data events
Enabling Amazon S3 server access logging
A company uses Amazon Redshift as its data warehouse service. A data engineer needs to design a physical data model.
The data engineer encounters a de-normalized table that is growing in size. The table does not have a suitable column to use as the distribution key.
Which distribution style should the data engineer use to meet these requirements with the LEAST maintenance overhead?
