AWS · DEA-C01
Validates ability to implement data pipelines and to monitor, troubleshoot, and optimize cost and performance issues in accordance with best practices.
Practice Questions
1,120
≈ 17 practice exams
Duration
130 minutes
Passing Score
720/1000
Difficulty
AssociateLast Updated
Jan 2026
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.
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.
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.
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.
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.
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.
5 sample questions with answers and explanations. The full bank has 1,120 questions, enough for 17 full-length practice exams.
Preview — answers shown1. Multiple ECS tasks poll messages from an Amazon SQS queue, and each message must be processed once before deletion. Which three events can result in a message being removed from the SQS queue? (Select three!)
Multiple correct answersExplanation
A DeleteMessage API call is the standard mechanism for permanently removing a processed message from the queue. Consumer applications call this API after successfully processing a message to confirm completion. Exceeding the maxReceiveCount means the message has been received and returned to the queue the specified number of times without successful deletion, indicating a persistent processing failure. SQS automatically moves such messages to the configured dead-letter queue, effectively removing them from the main queue. A purge operation immediately deletes all messages currently in the queue, used for resetting or troubleshooting. Receiving a message without deleting it causes the message to become invisible to other consumers for the duration of the visibility timeout, but the message remains in the queue and becomes visible again once the timeout expires. The visibility timeout expiring makes the message available to other consumers again rather than deleting it. Editing queue configuration changes operational parameters but has no effect on existing messages.
2. Adatum Insurance processes claim events through Amazon MSK. The team wants to ingest these events directly into Amazon Redshift for analytics without staging data in S3. The MSK cluster uses IAM authentication, and the Redshift Serverless workgroup is in a different VPC than the MSK cluster. Which two steps are required to enable Redshift streaming ingestion from MSK? (Select two!)
Multiple correct answersExplanation
Redshift streaming ingestion from MSK requires two critical configurations. First, enhanced VPC routing must be enabled on the Redshift workgroup, and since the clusters are in different VPCs, network connectivity must be established via VPC peering or Transit Gateway, with security groups configured to allow traffic on MSK broker ports. Second, an IAM role with appropriate MSK permissions must be attached to the Redshift namespace to authenticate with the MSK cluster. Firehose is not needed because streaming ingestion provides direct connectivity. Redshift streaming ingestion does support IAM authentication with MSK on Kafka version 2.7.1 and above, so converting to SASL/SCRAM is unnecessary. Kafka Connect S3 sink connector would stage data in S3, defeating the purpose of direct streaming ingestion.
3. A data engineer at Tailspin Corp is building a near-real-time analytics dashboard using Amazon Redshift Serverless with streaming ingestion from an Amazon MSK topic. The MSK topic contains JSON records with nested fields. The engineer notices that query performance degrades significantly as more columns are extracted from the JSON payload. Which approach should the engineer use to optimize JSON parsing performance in the streaming materialized view? (Select one!)
Explanation
JSON_PARSE converts the entire JSON payload to Redshift's SUPER data type in a single operation, and subsequent field access uses PartiQL dot notation without reparsing the JSON. This is far more efficient than JSON_EXTRACT_PATH_TEXT, which reparses the entire JSON string for every column extracted, meaning 10 columns would result in 10 separate parse operations per record. Creating multiple materialized views from the same MSK topic is explicitly not recommended because each view creates a separate consumer per partition, causing throttling risk, duplicate ingestion costs, and higher infrastructure costs. Enabling compression on the MSK topic does not address JSON parsing performance within Redshift, and Redshift streaming ingestion does not support compressed data.
4. Contoso Manufacturing operates an Amazon Redshift Serverless data warehouse. The marketing team needs to run complex analytical queries against both the Redshift tables and a 20 TB historical dataset stored as Parquet files in S3. Some of the Parquet files were recently transitioned to Glacier Flexible Retrieval to save costs. The data engineer needs to enable queries that join Redshift tables with the S3 data. Which actions should the data engineer take? (Select two!)
Multiple correct answersExplanation
Creating external tables in the Glue Data Catalog defines the schema and location of the S3 Parquet data. Redshift Spectrum can then query these external tables using its dedicated compute layer and join the results with local Redshift tables. Spectrum pushes filters, aggregations, and joins to its processing layer, reducing data transfer. However, the data engineer must be aware that Redshift Spectrum cannot query any Glacier storage class — the files in Glacier Flexible Retrieval must be restored before they can be queried. Restoring Glacier files is necessary only for the archived subset, not as a primary strategy for all 20 TB. Querying Glacier data directly through Spectrum will fail with an error. Loading all 20 TB into Redshift eliminates the cost advantage of keeping historical data in S3 and would significantly increase storage costs.
5. A data engineer at Adatum Technologies is building an ETL pipeline using AWS Glue. The source data in S3 contains a column that sometimes appears as a string and sometimes as an integer across different files. The Glue DynamicFrame detects this as an ambiguous type. The engineer needs to cast all values to string type. Which DynamicFrame transformation should be used? (Select one!)
Explanation
ResolveChoice is specifically designed to handle ambiguous or conflicting data types in DynamicFrames. Using the cast:string action resolves the choice type by casting all values to string, regardless of whether the original value was a string or integer. ApplyMapping can rename and cast columns but is not the appropriate transformation for resolving choice types — it works on columns with already-determined types. Relationalize flattens nested structures into relational tables but does not resolve type ambiguities. Filter would remove records rather than converting them, resulting in data loss.
AWS runs the same data-forensics review on DEA-C01 as on every certification in its program, comparing results against historical patterns for statistical anomalies. A flagged result means invalidation, possible permanent revocation, and being locked out of AWS's online testing program until AWS's security team grants written approval, with no refund either way.
DEA-C01 is a deep exam, 1,120 questions in our bank, which is exactly why shortcuts do not hold up once you are actually building pipelines on the job. 30 questions are free to start, each explained around the data engineering reasoning AWS is testing, not just the correct letter.
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