AWS · DEA-C01
AWS data engineering practice across ingestion, transformation, storage, operations, support, security, and governance for DEA-C01.
Practice Questions
1,120
≈ 17 practice exams
Duration
130 minutes
Passing Score
720/1000
Difficulty
AssociateLast Updated
Oct 2026
DEA-C01 validates the ability to implement data pipelines and monitor, troubleshoot, secure, and optimize them on AWS. Its scored outline is Data Ingestion and Transformation (34%), Data Store Management (26%), Data Operations and Support (22%), and Data Security and Governance (18%).
The 130-minute exam contains 65 questions, with 50 scored and 15 unscored. AWS charges $150 USD and reports scores from 100 to 1,000, with 720 required to pass. There are no formal prerequisites; AWS targets candidates with two to three years in data engineering and one to two years of hands-on AWS experience.
Use these 1,120 questions to practise service selection and trade-offs across batch and streaming ingestion, transformation, orchestration, storage, catalogs, lifecycle management, monitoring, data quality, IAM, encryption, privacy, and governance. The official guide now explicitly includes LLM integration for data processing, so study the current guide rather than an early DEA-C01 summary.
AWS Certified Data Engineer - Associate DEA-C01 validates implementation of data pipelines and the ability to monitor, troubleshoot, secure, govern, and optimise them. The current guide covers batch and streaming systems, orchestration, data stores and models, operational support, quality, privacy, encryption, access control, logging, and LLM integration for data processing.
AWS targets candidates with two to three years of data engineering experience and one to two years of hands-on AWS work. They should understand ETL pipelines, source control, SQL, data lakes, networking, storage, compute, security, governance, data quality, and cost-performance trade-offs.
AWS enforces no certification prerequisite. Familiarity with programming concepts is required, but language-specific syntax and building machine-learning training or inference systems are outside the exam scope.
DEA-C01 contains 65 questions in 130 minutes: 50 scored and 15 unscored multiple-choice or multiple-response items. AWS reports scores from 100 to 1,000 and requires 720 to pass. The fee is $150 USD, delivery is online or at Pearson VUE test centers, and the certification is valid for three years.
DEA-C01 validates role-specific AWS data engineering ability for data engineer, analytics engineer, data platform engineer, and cloud data architecture work. It is strongest evidence when paired with production pipelines and concrete examples of operations, quality, security, and cost optimisation.
5 sample questions with answers and explanations. The full bank has 1,120 questions, enough for 17 full-length practice exams.
Preview — answers shown1. A data engineer needs to execute multiple tasks concurrently within an AWS Step Functions state machine to decrease overall processing time. Which state type enables concurrent execution?
Explanation
The Parallel state in AWS Step Functions executes multiple explicitly defined branches concurrently, starting each branch at its designated StartAt state. The Parallel state waits until all branches reach a terminal state before transitioning to the next state in the workflow, combining their outputs. This is the correct mechanism for running independent tasks simultaneously to reduce total processing time. The Fail state terminates the execution with a failure status and provides no execution or branching capability. The Choice state evaluates conditions against input data and routes execution to one of several possible next states, but it processes only one branch at a time rather than concurrently. The Succeed state terminates the execution successfully and has no capability to manage or monitor concurrent task execution.
2. A corporation stores large datasets on-premises and wants to periodically copy this data to Amazon S3 to keep the S3 bucket synchronized with the latest on-premises data. Which AWS service is most suitable?
Explanation
AWS DataSync is purpose-built for automating and accelerating data movement between on-premises storage systems and AWS storage services including S3, supporting both one-time migrations and periodic scheduled transfers. It handles scheduling, monitoring, and data integrity verification automatically. AWS Glue is an ETL service designed for data preparation and analytics workflows, not for periodic file synchronization between on-premises and S3. AWS Storage Gateway is optimized for hybrid cloud access patterns where on-premises applications need seamless access to cloud storage, not for periodic batch replication. AWS Snowball is a physical device for large one-time data transfers and does not support automated periodic replication.
3. An organization needs to ingest data from external SaaS applications into Amazon S3 and analyze it with Amazon Redshift. Which AWS service meets this requirement with the least operational overhead?
Explanation
Amazon AppFlow is a fully managed integration service built specifically to move data between SaaS applications such as Salesforce, ServiceNow, and Slack and AWS services including S3 and Redshift. It provides native connectors, built-in data transformation capabilities, and multiple trigger options without requiring custom code or pipeline infrastructure. The service handles authentication, API rate limiting, and data format normalization automatically. Amazon EventBridge is an event bus service for routing events between AWS services and applications; while it can trigger actions in response to SaaS events, it does not extract and deliver the underlying data to S3 or Redshift without additional custom development. MWAA is an orchestration service for Apache Airflow workflows that can coordinate data movement tasks but requires deploying and managing an Airflow environment, writing DAG code, and configuring SaaS connectors manually. AWS Step Functions orchestrates sequences of AWS service calls but does not include built-in SaaS connectors and would require Lambda functions and custom integration code to extract data from external applications.
4. A data engineering team analyzing Amazon Redshift performance wants to identify conditions that might indicate query failures or suboptimal query performance. Which system table view should they use?
Explanation
STL_ALERT_EVENT_LOG records alerts generated by the Redshift query optimizer when it detects conditions that could indicate performance issues, such as nested loops, missing statistics, or hash joins on large datasets. Querying this view surfaces actionable warnings that point directly to optimization opportunities. STL_USAGE_CONTROL logs changes to WLM configuration and records when queries are paused or canceled due to WLM rules, which is useful for WLM administration but not for identifying optimizer-generated performance alerts. STL_QUERY_METRICS provides a snapshot of current query queue activity and active query metrics, not historical alert events. STL_WLM_QUERY records query execution details like CPU time, rows processed, and disk spill within WLM service classes, which supports performance analysis but does not surface optimizer-generated alert events.
5. A company must migrate its on-premises PostgreSQL database to Amazon RDS for PostgreSQL with minimal downtime and wants to validate data consistency after the migration. Which AWS service is most suitable?
Explanation
AWS Database Migration Service is purpose-built for database migrations and supports keeping the source database fully operational during the migration process. It uses continuous data replication (change data capture) to minimize downtime and includes data validation capabilities to verify that the migrated data matches the source. AWS Snowball is a physical device for large-scale data transfers and does not support ongoing database replication or validation. AWS DataSync transfers files between storage systems and does not handle relational database migrations. AWS Glue is an ETL service for preparing and transforming data for analytics workloads, not for migrating transactional databases while keeping them live.
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.
The exam has 65 questions: 50 scored and 15 unscored. Unscored items are not identified.
DEA-C01 allows 130 minutes and requires a scaled score of 720 on a 100-1,000 scale.
Data Ingestion and Transformation is 34%, Data Store Management 26%, Data Operations and Support 22%, and Data Security and Governance 18%.
No. AWS targets candidates with two to three years of data engineering and one to two years of hands-on AWS experience.
AWS lists the exam fee as $150 USD.
Model training and inference are out of scope, but the current guide includes vector concepts and integrating LLMs for data processing.
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