AWSAssociate Level2026 Updated

AWS Data Engineer Associate DEA-C01 Exam Guide 2026: Everything You Need to Pass

Updated May 1, 202612 min readWritten by Certsqill experts

Quick facts — DEA-C01

Exam cost

$300 USD

Questions

65 items

Time limit

130 minutes

Passing score

720 / 1000

Valid for

3 years

Testing

Pearson VUE

Who this exam is for

The AWS Data Engineer Associate DEA-C01 certification is designed for professionals who work with or want to work with AWS technologies in a professional capacity. It is taken by cloud engineers, DevOps practitioners, IT administrators, and technical professionals looking to validate their expertise.

You do not need extensive prior experience to attempt it, but you will benefit from hands-on familiarity with the subject matter. The exam tests applied knowledge and architectural judgment, not just memorization. If you can reason about trade-offs and real-world scenarios, structured practice will handle the rest.

Domain breakdown

The DEA-C01 exam is built around official domains, each with a fixed percentage of the question pool. This distribution should directly inform how you allocate your study time.

Domain

Weight

Focus areas

Data Ingestion & Transformation

34%

Kinesis Data Streams vs Kinesis Data Firehose, AWS Glue ETL jobs (Spark-based), Glue Data Catalog, AWS DMS, AWS DataSync, and designing batch vs streaming ingestion pipelines.

Data Store Management

26%

S3 data lake design with prefixes and partitioning, Amazon Redshift cluster vs Serverless, Aurora for transactional workloads, DynamoDB for operational data, and OpenSearch Service.

Data Operations & Support

22%

AWS Glue workflows and triggers, Step Functions for pipeline orchestration, EventBridge Scheduler, CloudWatch metrics for data pipeline health, and troubleshooting Glue and EMR job failures.

Data Security & Governance

18%

AWS Lake Formation column-level and row-level security, data filtering, Glue catalog encryption, S3 encryption in data lake contexts, and IAM data lake access patterns.

Note the domain with the highest weight — many candidates under-invest here because it feels conceptual. In practice, this is where the exam is most precise, with scenario-based questions that test specifics.

What the exam actually tests

This is not a memorization exam. Questions require applied judgment under constraints. Almost every question includes a scenario with explicit requirements and asks you to select the most appropriate solution.

Here are examples of the question types you will encounter:

Pipeline service selection

"A company needs to continuously deliver clickstream data from its website to an S3 data lake with automatic format conversion to Parquet and no custom code. Which AWS service should the data engineer use?"

Tests the distinction between Kinesis Data Firehose (managed, no code, built-in format conversion to Parquet/ORC via Glue) and Kinesis Data Streams (developer-managed consumers, custom processing). Firehose is the answer for low-code delivery to S3/Redshift/OpenSearch.

Glue vs EMR trade-off

"A data engineering team needs to run complex custom Spark transformations on 5 TB of data daily. The team has strong PySpark experience and requires fine-grained control over Spark configurations and cluster sizing. Which service is MOST appropriate?"

Tests when to choose EMR (custom Spark, full cluster control, complex transformations) over Glue (managed, wizard-based, best for simpler ETL). EMR is correct when Spark expertise and configuration control are requirements.

Lake Formation security design

"A data lake contains a table with customer financial records. The compliance team requires that analysts can query all columns except SSN and account number. Which Lake Formation feature implements this with the LEAST operational overhead?"

Tests Lake Formation column-level security (data filtering), which restricts specific columns in the Glue Data Catalog without modifying the underlying S3 data. This is distinct from S3 bucket policies, which cannot provide column-level access control.

How to prepare — 4-week study plan

This plan assumes one hour per weekday and roughly 30 minutes of lighter review on weekends. It is calibrated for someone with some relevant experience. If you are starting from zero, add an extra week before Week 1 to familiarise yourself with the basics.

Week 1: Streaming Ingestion & Kinesis

Study Kinesis Data Streams: shards, partition keys, sequence numbers, retention period (1-365 days), enhanced fan-out, and consumer types
Learn Kinesis Data Firehose: delivery stream destinations (S3, Redshift, OpenSearch, Splunk), dynamic partitioning, and Glue schema-based format conversion to Parquet/ORC
Understand Kinesis Data Analytics (Managed Apache Flink): streaming SQL vs Apache Flink applications, sliding vs tumbling windows
Compare MSK (Managed Kafka) with Kinesis: when each is appropriate, connector types, and MSK Connect for S3 sink

Week 2: Glue, EMR & Batch Processing

Study AWS Glue: crawlers, Data Catalog, ETL jobs (Python Shell vs Spark), job bookmarks for incremental processing, Glue Studio, and Glue DataBrew
Learn Glue workflows: triggers (scheduled, conditional, on-demand), workflow graphs, and monitoring with CloudWatch metrics
Understand EMR: cluster types (on-demand, spot, reserved), instance fleets, EMRFS for S3 integration, and when EMR beats Glue for complex Spark workloads
Study AWS DMS: supported source/target combinations, full load vs full load + CDC, replication instance sizing, and SCT for schema conversion

Week 3: Data Stores & Governance

Master Redshift: distribution styles (EVEN, KEY, ALL), sort keys, Redshift Spectrum for S3 querying, Redshift Serverless vs provisioned, and COPY command optimisation
Study Lake Formation: blueprint workflows, granting table/column permissions, tag-based access control, and cross-account data sharing
Learn S3 data lake design: partitioning strategies for Athena query optimisation, S3 Intelligent-Tiering, Object Lock for compliance, and S3 Select vs Athena
Understand Step Functions for data pipeline orchestration: state types (Task, Choice, Parallel, Map), error handling, and integration with Glue/EMR/Lambda

Week 4: Operations, Security & Mock Exams

Study CloudWatch for data pipeline monitoring: Glue job metrics (bytes read/written, errors), EMR step metrics, and creating alarms for pipeline failures
Learn S3 and Glue encryption: SSE-S3 vs SSE-KMS for data lake objects, Glue Data Catalog encryption at rest, and connection password encryption
Complete two full 65-question mock exams under 130-minute timed conditions and review all incorrect answers
Drill Kinesis vs Firehose vs DMS selection scenarios — the most commonly confused service combinations on this exam

Common mistakes candidates make

These patterns appear repeatedly among candidates who resit this exam. Knowing them in advance is worth several percentage points.

Confusing Glue ETL and Amazon EMR use cases

AWS Glue is a managed, serverless ETL service optimised for straightforward data transformations without cluster management. Amazon EMR is appropriate when teams need full Apache Spark/Hadoop control, custom Spark configurations, or run very large and complex jobs. The exam tests this boundary — know the specific signals (custom Spark configs, fine-grained cluster control) that indicate EMR over Glue.

Not understanding Kinesis Data Streams vs Firehose

Kinesis Data Streams requires you to write consumer code (Lambda, KCL applications) and manage shard capacity. Kinesis Data Firehose is fully managed with no consumer code needed and direct delivery to S3, Redshift, OpenSearch, and Splunk with built-in format conversion. Questions requiring "no custom code" or "automatic Parquet conversion" point to Firehose.

Weak on Lake Formation column-level security

Lake Formation data filtering (column and row-level security) is tested in the Data Security & Governance domain. Many candidates only know IAM-based S3 access and cannot answer questions about restricting specific columns from specific IAM principals. Understand how Lake Formation permissions layer on top of IAM and how tag-based access control works.

Not knowing Redshift distribution and sort key optimisation

Redshift performance questions require understanding distribution styles (EVEN distributes evenly, KEY co-locates join data, ALL replicates to all nodes) and sort key types (compound vs interleaved). Choosing the wrong distribution style causes data skew and slow queries. This is tested in scenario questions about Redshift query performance problems.

Is Certsqill right for you?

Honestly: Certsqill is built for candidates who have already done some studying and want to convert knowledge into exam performance. If you have never touched the subject, start with a foundational course first — then come to Certsqill when you are ready to practice.

Where Certsqill is strong: question depth, AI-powered explanations, and domain analytics. Every question is mapped to the exam blueprint. When you get something wrong, the AI tutor explains why the right answer is right and why each wrong answer fails under the specific constraints in the question.

Where Certsqill is not a replacement: video courses and hands-on labs. Use Certsqill to test and sharpen — not as your first exposure to a topic you have never encountered.

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