AWS · DEA-C01
AWS Certified Data Engineer - Associate (DEA-C01) Practice Test
Validates ability to implement data pipelines and to monitor, troubleshoot, and optimize cost and performance issues in accordance with best practices.
Exam Details
Questions
1120
Duration
130 minutes
Passing Score
720/1000
Difficulty
AssociateLast Updated
Jan 2026
Topics Covered
Data Ingestion and TransformationData Store ManagementData Operations and SupportData Security and Governance
AWS Certified Data Engineer - Associate (DEA-C01) Practice Exam Preparation
Use this DEA-C01 practice exam to prepare for AWS Certified Data Engineer - Associate (DEA-C01) with realistic questions, detailed explanations, and focused study modes. The practice bank includes 1,120 questions for AWS DEA-C01, so you can review the exam steadily instead of relying on one long cram session.
As you practice, pay extra attention to recurring topics such as Data Ingestion and Transformation, Data Store Management, Data Operations and Support, and Data Security and Governance. Start with short sessions to identify weak areas, then move into timed quizzes once your accuracy is consistent.
The explanations are especially useful when you want to connect exam wording to the responsibilities and scenarios described in the official certification guidance. Use the free preview first, then unlock the full question bank when you are ready to build a complete study routine.
Exam Domain Breakdown
Data Ingestion and Transformation34%
Data Store Management26%
Data Operations and Support22%
Data Security and Governance18%
Exam Overview
The AWS Certified Data Engineer – Associate (DEA-C01) is an associate-level credential that validates a practitioner's ability to implement, monitor, and optimize data pipelines on AWS. Launched in 2023, it is the first AWS certification designed specifically for data engineers, replacing the need to cobble credentials from Solutions Architect or Data Analytics Specialty exams. The exam assesses proficiency across the full data engineering lifecycle: ingesting and transforming data, selecting and managing appropriate data stores, orchestrating pipelines using programming concepts, and enforcing data security and governance policies using AWS-native tooling.
Key AWS services in scope include Amazon S3, AWS Glue, Amazon Redshift, Amazon Kinesis, Amazon EMR, AWS Lake Formation, Amazon DynamoDB, AWS Database Migration Service, and Amazon Athena, among others. Candidates are evaluated on their ability to compare cost and performance trade-offs between services, apply SQL on AWS platforms, implement encryption and access controls, and validate data quality and consistency. Out-of-scope topics include ML model training and inference, programming-language-specific syntax, and deriving business conclusions from data analysis.
Who Should Take This Exam
The target candidate is a data engineer or data architect with roughly 2–3 years of experience in data engineering and at least 1–2 years of hands-on AWS experience. This includes professionals who design and maintain ETL/ELT pipelines, manage data lakes and warehouses, or work with real-time streaming architectures. Adjacent roles transitioning into cloud data engineering — such as database administrators, backend developers, or traditional ETL developers — will also find this certification a clear roadmap for bridging legacy skills with AWS-native approaches.
The exam suits those who regularly work with concepts such as volume, variety, and velocity of data; data modeling and schema design; data lifecycle management; and cloud security and governance. It is not aimed at data scientists, ML engineers, or business analysts, as those domains fall outside the exam's scope.
Prerequisites
AWS does not enforce formal prerequisites for the DEA-C01, but the official exam guide recommends 2–3 years of data engineering or data architecture experience and 1–2 years of hands-on work with AWS services. Candidates should be comfortable setting up and maintaining ETL pipelines from ingestion to destination, writing and executing SQL queries, using Git-based source control workflows, and applying language-agnostic programming concepts (loops, conditionals, data structures).
On the AWS side, recommended knowledge includes familiarity with data pipeline orchestration services (AWS Glue, AWS Step Functions), storage systems (Amazon S3, Amazon Redshift, Amazon DynamoDB), streaming platforms (Amazon Kinesis), and security/governance services (AWS IAM, AWS KMS, AWS Lake Formation). Understanding of data lakes, networking fundamentals (VPC, subnets, connectivity), compute options (Amazon EMR, AWS Lambda), and vector/embedding concepts is also beneficial. While no prior AWS certification is required, having the AWS Cloud Practitioner or AWS Solutions Architect – Associate background provides a useful foundation.
Exam Format
The DEA-C01 exam consists of 65 total questions: 50 scored questions that contribute to the final result and 15 unscored pilot questions that AWS uses to evaluate future content. Unscored questions are not identified, so candidates should treat all questions equally. Question types are multiple choice (one correct answer from four options) and multiple response (two or more correct answers from five or more options). The time limit is 130 minutes, and the exam is delivered via Pearson VUE at a testing center or through an online proctored session. The exam is available in English, Japanese, Korean, and Simplified Chinese, and costs $150 USD.
Scores are reported on a scaled range of 100–1,000, and the minimum passing score is 720. AWS uses a compensatory scoring model, meaning candidates do not need to achieve a passing threshold in each individual domain — only the overall scaled score matters. Unanswered questions are treated as incorrect; there is no penalty for guessing. The certification is valid for three years, after which recertification requires passing the current version of the exam.
Skills Measured
- 1.Domain 1 – Data Ingestion and Transformation (34%): Covers reading from and writing to diverse data sources (batch and streaming), using AWS Glue for ETL jobs and crawlers, ingesting data with Amazon Kinesis Data Streams and Kinesis Firehose, applying transformation logic with AWS Lambda and Amazon EMR, and orchestrating pipelines with AWS Step Functions and Amazon MWAA. Also includes applying programming concepts (language-agnostic) to automate pipeline tasks.
- 2.Domain 2 – Data Store Management (26%): Covers selecting appropriate storage services (Amazon S3, Amazon Redshift, Amazon DynamoDB, Amazon RDS, Amazon OpenSearch Service) based on access patterns, latency, cost, and scale. Includes designing data models, implementing partitioning and compression strategies, managing schema evolution with AWS Glue Data Catalog, and enforcing data lifecycle policies (S3 lifecycle rules, Redshift data sharing).
- 3.Domain 3 – Data Operations and Support (22%): Covers operationalizing and monitoring data pipelines using Amazon CloudWatch, AWS CloudTrail, and AWS Glue job metrics. Includes automating pipeline deployments with AWS CDK or CloudFormation, troubleshooting pipeline failures, performing data quality checks and validation, and analyzing data using Amazon Athena and Amazon Redshift queries.
- 4.Domain 4 – Data Security and Governance (18%): Covers implementing authentication and authorization using AWS IAM, resource-based policies, and AWS Lake Formation column- and row-level security. Includes encrypting data at rest and in transit with AWS KMS and SSL/TLS, managing data privacy and compliance requirements, configuring audit logging via AWS CloudTrail, and applying data governance frameworks using AWS Glue Data Catalog and Lake Formation.
Study Tips
- Start with the official AWS Exam Guide (PDF from awsstatic.com) and map every task statement to your existing knowledge gaps before studying anything else — prioritize Domain 1 (34%) and Domain 2 (26%) since they together account for 60% of the scored content.
- Use AWS Skill Builder's official 'AWS Certified Data Engineer – Associate' exam prep course, which includes video modules aligned to each domain and the Official Practice Question Set with rationales explaining both correct and incorrect answers.
- Build hands-on labs around the core data pipeline services: create an end-to-end pipeline using AWS Glue crawlers and ETL jobs, write to Amazon Redshift, and stream real-time data through Amazon Kinesis Data Firehose into S3 — this covers the three most heavily tested service categories.
- Take the AWS Certification Official Pretest on Skill Builder early in your study cycle to identify weak domains, then revisit it after studying to measure progress before scheduling the real exam.
- For Domain 4 (Security and Governance), specifically practice configuring AWS Lake Formation permissions, column-level security, and the difference between IAM-based vs. Lake Formation–based access control, as these are frequently tested and commonly misunderstood.
- Review the AWS Well-Architected Framework's Data Analytics Lens and the AWS whitepapers on data lake architecture and streaming data solutions — these provide the architectural reasoning behind best-practice questions on the exam.
- Practice with multiple-response question formats specifically, since these are harder to guess and require knowing all correct answers. Use timed practice sets to simulate the 130-minute constraint across 65 questions (roughly 2 minutes per question).
Career Benefits
The DEA-C01 certification targets one of the fastest-growing roles in cloud computing. AWS-certified data engineers in the US report average salaries around $141,000 per year according to Glassdoor data, with entry-level positions starting near $124,000–$130,000 and senior roles exceeding $175,000. Research from the Jefferson Frank Careers and Hiring Guide found that 73% of AWS professionals saw a salary increase after certification, averaging approximately 27%. Job roles accessible with this credential include Data Engineer, Cloud Data Architect, ETL/ELT Developer, Data Platform Engineer, and Analytics Engineer.
AWS certifications appear in cloud job postings more than any other vendor credential, and the DEA-C01 specifically validates the services — Glue, Redshift, Kinesis, S3 — that dominate real-world data engineering job requirements. For professionals transitioning from database administration, backend development, or traditional ETL roles, the certification provides a structured path into cloud-native data engineering. Many candidates report role transitions or salary increases within 3–6 months of earning the credential. Pairing DEA-C01 with the Databricks Data Engineer Associate certification is widely considered the most job-market-relevant two-certification combination in the data engineering space.
Sample Questions
5 sample questions with answers and explanations. Start a practice session to test yourself across all 1120 questions.
Preview — answers shown1. A data engineer at Contoso Pharmaceuticals has a Glue ETL job that processes clinical trial data from a JDBC source (PostgreSQL on RDS). The job runs daily and should only process new records added since the last successful run. The source table has an auto-incrementing trial_sequence_id column. Which Glue feature should the data engineer configure to achieve incremental processing? (Select one!)
AEnable job bookmarks with the trial_sequence_id column as the bookmark key
BSet the RecrawlPolicy to CRAWL_NEW_FOLDERS_ONLY on the JDBC source
CConfigure a Glue crawler with CRAWL_EVENT_MODE to detect new records
DUse pushdown predicates with a hardcoded date filter in the ETL script
Explanation
Glue job bookmarks track previously processed data to prevent reprocessing. For JDBC sources, bookmarks use bookmark key columns that must be monotonically increasing or decreasing. The auto-incrementing trial_sequence_id column is ideal as a bookmark key because each run will resume from the last processed sequence number. The data engineer must call job.init() at the start and job.commit() at the end of the ETL script, and include the transformation_ctx parameter to properly index the bookmark state. Glue crawlers with CRAWL_EVENT_MODE are designed for S3 event notifications, not JDBC sources. Hardcoded date filters would require manual updates and would miss any late-arriving records inserted with earlier dates. CRAWL_NEW_FOLDERS_ONLY is an S3-specific RecrawlPolicy that applies to folder-based partitioning and is not applicable to JDBC data sources.
2. Adatum Corp has an AWS Lambda function that processes messages from an Amazon SQS FIFO queue. The function has a timeout of 60 seconds. During load testing, the team discovers that some messages are being processed multiple times, causing duplicate records in the downstream DynamoDB table. What should the data engineer do to prevent duplicate processing? (Select one!)
AIncrease the SQS FIFO queue visibility timeout to at least 360 seconds to prevent messages from becoming visible again before processing completes
BConfigure the Lambda function with provisioned concurrency to eliminate cold starts that may cause processing delays
CEnable content-based deduplication on the SQS FIFO queue to automatically reject duplicate messages
DAdd a DynamoDB conditional write with a deduplication ID to prevent duplicate records regardless of message reprocessing
Explanation
When the SQS visibility timeout is shorter than the Lambda function processing time, messages become visible again and are processed by another invocation before the original processing completes. AWS recommends setting the visibility timeout to at least 6 times the function timeout plus the MaximumBatchingWindowInSeconds, which in this case means at least 360 seconds for a 60-second function timeout. This prevents messages from reappearing during processing. Content-based deduplication prevents duplicate messages from being sent to the queue within a 5-minute window, not duplicate processing of the same message. Provisioned concurrency reduces cold starts but does not address the visibility timeout issue. While DynamoDB conditional writes provide application-level idempotency, the root cause of the visibility timeout misconfiguration should be fixed directly.
3. Adatum Analytics runs Amazon Redshift with RA3 nodes. The team has a large fact table (orders) that is frequently joined with a small dimension table (product_categories, 500 rows) and a medium-sized dimension table (customers, 50 million rows) on their respective ID columns. The team needs to optimize query performance for these joins. Which combination of distribution styles should the team use? (Select two!)
Multiple correct answersAApply KEY distribution on the orders table using customer_id as the distribution key
BApply ALL distribution on the product_categories table to replicate it across all nodes
CApply EVEN distribution on the orders table for balanced data placement
DApply KEY distribution on the customers table using customer_id as the distribution key
EApply ALL distribution on the customers table to replicate it across all nodes
Explanation
KEY distribution on the orders fact table using customer_id collocates rows with matching customer_id values on the same node slices, enabling local joins with the customers table and eliminating data redistribution during queries. ALL distribution on the small product_categories table (500 rows) replicates the entire table to every node, ensuring joins with the fact table never require network data movement. EVEN distribution on orders would scatter data randomly without considering join patterns, causing expensive data redistribution. KEY distribution on customers alone without matching the fact table's distribution key would not collocate the data. ALL distribution on customers (50 million rows) would wastefully replicate a large table across every node, multiplying storage requirements and slowing inserts and updates.
4. A multinational corporation needs a system for managing large organizational datasets with granular access control at the database, table, column, row, and cell level, implemented with minimal operational overhead. Which solution meets this requirement?
AImplement Amazon RDS with IAM database authentication for fine-grained access at various levels
BDeploy a Hive metastore on Amazon EMR and manage user access through HiveQL DDL statements
CEstablish a data lake using AWS Lake Formation and control access via Lake Formation data filters
DImplement an Amazon Aurora database as a catalog and control access using SQL GRANTs
Explanation
AWS Lake Formation data filters provide granular access control at the database, table, column, and row levels from a centralized governance layer that integrates with the AWS Glue Data Catalog. Administrators can grant and revoke permissions through a simple interface without modifying application code or writing custom access logic. This covers all required access granularity levels including cell-level security through combined column and row filter policies. A Hive metastore on EMR provides metadata management for Hadoop-based workloads but does not natively support row-level or cell-level security, and managing access through HiveQL adds operational complexity. Amazon Aurora with SQL GRANTs enables column and table-level access control but does not provide native row-level or cell-level security without additional stored procedure logic or views, and it lacks the centralized governance capabilities of Lake Formation. RDS with IAM database authentication manages who connects to the database but does not provide row-level or cell-level security through IAM alone.
5. Adatum Digital is building a serverless event-driven pipeline. When new files arrive in S3, a Lambda function must validate the file format, a Glue ETL job must transform the data, and a second Lambda function must send a notification upon completion. The Glue job takes between 10 and 45 minutes depending on file size. The data engineer needs to orchestrate these steps with proper error handling and retry logic. Which orchestration approach meets these requirements with the LEAST operational overhead? (Select one!)
AUse AWS Step Functions Express Workflow with a Wait state polling the Glue job status
BUse AWS Step Functions Standard Workflow with the Run a Job (.sync) integration pattern for the Glue ETL job
CUse S3 Event Notifications to trigger Lambda functions chained via SNS topics with Glue job polling
DDeploy Amazon MWAA with an Airflow DAG using GlueJobOperator and LambdaInvokeOperator
Explanation
AWS Step Functions Standard Workflows support execution durations up to one year and provide the Run a Job (.sync) integration pattern that automatically waits for Glue ETL jobs to complete before proceeding to the next state. This eliminates the need for polling logic and provides built-in retry and catch error handling with minimal operational overhead. The serverless nature requires no infrastructure management. Chaining Lambda functions via SNS requires custom polling logic for the long-running Glue job and lacks centralized error handling. MWAA provides powerful orchestration but requires managing an Airflow environment with VPC, NAT gateway, and DAG deployment, adding significant operational overhead compared to Step Functions. Express Workflows have a maximum duration of 5 minutes, which is insufficient for Glue jobs lasting up to 45 minutes.
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