AWS Certifications
AWS Certified Data Engineer Associate
Most candidates assume DEA-C01 is a shortcut to a data engineering job. It isn't. Roughly 62% pass on the first attempt, and the ones who land DE roles afterward usually had production AWS experience already. The cert validates what you know. It doesn't install the knowledge. 65 questions, 170 minutes, $150, and a fair amount of cognitive dissonance about what the exam is actually worth.
Updated April 2026·By The DataDriven Team
Exam at a glance
The headline numbers. Cost, time, and difficulty distilled into four tiles.
65
Questions
170 min
Duration
$150
Cost
3 years
Validity
By the numbers
Source: DataDriven analysis of 1,042 verified data engineering interview rounds.
62%
First-attempt pass
$150
Exam fee
65
Questions
170m
Time limit
Exam Overview
Key numbers for the AWS Certified Data Engineer Associate (DEA-C01).
65
Multiple choice + multi-select
170 min
Proctored
~720
Out of 1000 (scaled)
$150
USD per attempt
3 years
Then recertify
Remote
Pearson VUE or PSI
What the Exam Tests
Candidates obsess over security and governance because it sounds important. The exam doesn't. Ingestion and transformation is a third of the test all by itself, and most of the failures we see come from people who skipped Kinesis and Glue to memorize IAM policies. Follow the weights. Ignore the vibes.
| Domain | Weight | Approx questions |
|---|---|---|
| Data Ingestion and Transformation | 34% | ~22 |
| Data Store Management | 26% | ~17 |
| Data Operations and Support | 22% | ~14 |
| Data Security and Governance | 18% | ~12 |
34%~22 questions
Data Ingestion and Transformation
This is the heaviest section and it's where most candidates either pass or fail. Covers AWS Glue (ETL jobs, crawlers, data catalog), Kinesis (Data Streams, Firehose, Analytics), EMR, and Step Functions for orchestration. You need to know when to use Glue vs EMR vs Kinesis for a given scenario. The exam loves questions where two services could technically work but one is clearly better. Glue is the answer roughly 40% of the time in this domain. Know Glue bookmarks for incremental processing, Glue job types (Spark, Python Shell, Ray), and how the Data Catalog integrates with Athena and Redshift Spectrum.
26%~17 questions
Data Store Management
Covers S3 (storage classes, lifecycle policies, partitioning), Redshift (distribution styles, sort keys, WLM, Redshift Spectrum), DynamoDB (partition keys, GSIs, streams), and RDS/Aurora. The exam tests you on choosing the right data store for a workload. Columnar analytics? Redshift. Sub-millisecond key-value lookups? DynamoDB. Cheap long-term storage with ad hoc queries? S3 + Athena. Know Redshift distribution styles cold: KEY, EVEN, ALL, and AUTO. Expect 2-3 questions on S3 partitioning strategies for Athena performance.
22%~14 questions
Data Operations and Support
Monitoring, alerting, troubleshooting, and automation. CloudWatch metrics and alarms, CloudTrail for audit, EventBridge for event-driven pipelines, and SNS/SQS for notifications and decoupling. The exam tests whether you can diagnose pipeline failures: a Glue job OOM error, a Kinesis shard hot key, a Redshift query queue bottleneck. This domain also covers CI/CD for data pipelines using CodePipeline and CodeBuild. Know how to set up CloudWatch alarms on Glue job metrics and Kinesis iterator age.
18%~12 questions
Data Security and Governance
IAM policies, KMS encryption (at rest and in transit), Lake Formation permissions, VPC endpoints, and data masking. The exam expects you to know the difference between S3 bucket policies and IAM policies, when to use Lake Formation vs raw IAM for data access control, and how KMS key policies work with cross-account access. Expect questions on encryption configuration for each service: S3 SSE-KMS, Redshift encryption, DynamoDB encryption. Lake Formation appears heavily because it's AWS's answer to centralized data governance.
Services You Must Know Cold
The exam covers 20+ AWS services, but these six account for roughly 70% of the questions. Deep knowledge here is non-negotiable.
AWS Glue
The Swiss Army knife of AWS data engineering. Glue handles ETL (Spark-based jobs), data cataloging (crawlers that populate a Hive-compatible metastore), and schema management. The exam tests Glue more than any other service. You need to know: job bookmarks for incremental loads, dynamic frames vs DataFrames, PushDown Predicates for partition pruning, and when to use Spark jobs vs Python Shell jobs. Glue crawlers can auto-detect schema changes, but they can also create duplicate tables if partitioning changes. This is a common exam trap.
Amazon Kinesis
Three services under one name. Kinesis Data Streams: manually managed shards, 1MB/s write per shard, 2MB/s read per shard, 24-hour to 365-day retention. Kinesis Data Firehose: fully managed delivery to S3, Redshift, or OpenSearch with automatic batching and compression. Kinesis Data Analytics: real-time SQL or Flink processing on streaming data. The exam tests shard math. If you're ingesting 5MB/s, you need at least 5 shards. Hot shards (uneven partition key distribution) cause throttling. Know when to use Streams vs Firehose: Streams for custom consumers and sub-second processing, Firehose for fire-and-forget delivery to storage.
Amazon Redshift
Columnar MPP warehouse. The exam covers: distribution styles (KEY distributes rows by a column, EVEN distributes round-robin, ALL copies entire table to every node), sort keys (compound vs interleaved), WLM (workload management queues), concurrency scaling, and Redshift Spectrum for querying S3 directly. Common exam scenario: you have a fact table and a dimension table. Distribute both by the join key for co-located joins. Sort the fact table by the most-filtered column (usually date). Spectrum lets you keep cold data on S3 while hot data stays in Redshift.
Amazon S3 + Athena
S3 is the backbone of every AWS data lake. The exam tests partitioning strategies: Hive-style partitions (s3://bucket/year=2026/month=04/) reduce scan costs in Athena by 10-100x. File format matters: Parquet and ORC give columnar compression and predicate pushdown. Athena is serverless SQL over S3. Know that Athena pricing is per TB scanned, so partition pruning and columnar formats directly reduce cost. Athena also integrates with the Glue Data Catalog for schema management.
AWS Lake Formation
Centralized permissions layer for data lakes. Instead of managing S3 bucket policies, IAM roles, and Glue Data Catalog permissions separately, Lake Formation provides a single place to grant table-level and column-level access. The exam tests: creating a data lake with Lake Formation, granting/revoking permissions, tag-based access control, and cross-account data sharing. Lake Formation is the governance answer for most exam questions about data access control.
AWS Step Functions
Orchestrate multi-step data pipelines using state machines. Step Functions coordinate Glue jobs, Lambda functions, EMR steps, and ECS tasks with retry logic, error handling, and parallel execution. The exam tests when to use Step Functions vs Glue Workflows vs MWAA (managed Airflow). Step Functions is the default answer for orchestrating heterogeneous AWS services. MWAA is the answer when the team already uses Airflow or needs complex scheduling logic.
6-8 Week Study Plan
Structured for data engineers with some AWS exposure. Allocate 1 to 2 hours daily. If you're starting from zero AWS experience, add 2 weeks for cloud fundamentals.
Weeks 1-2: Foundations: S3, Glue, and the Data Catalog
- Set up an AWS free-tier account and create an S3 data lake with Hive-style partitions
- Run Glue crawlers against sample data, inspect the Data Catalog entries
- Write a Glue ETL job that reads CSV from S3, transforms to Parquet, and writes partitioned output
- Understand Glue job bookmarks by running an incremental load twice
- Practice Athena queries against your partitioned data, note the data scanned
- Read the AWS Glue developer guide sections on DynamicFrames and PushDown Predicates
Weeks 3-4: Streaming, Redshift, and Data Stores
- Create a Kinesis Data Stream and produce/consume records with the AWS CLI
- Set up a Kinesis Firehose delivery stream to S3 with transformation via Lambda
- Deploy a Redshift cluster, load sample data, and experiment with distribution styles
- Run EXPLAIN on Redshift queries to see distribution and sort key effects
- Set up a DynamoDB table, understand partition key design and GSI projections
- Compare costs: Redshift on-demand vs reserved, Athena vs Redshift Spectrum for the same query
Weeks 5-6: Security, Governance, and Operations
- Configure Lake Formation with table-level and column-level permissions
- Set up KMS encryption for S3, Redshift, and DynamoDB, understand key policies
- Build a Step Functions state machine that orchestrates a Glue job and a Lambda function
- Configure CloudWatch alarms for Glue job failures and Kinesis iterator age
- Practice IAM policy writing: least-privilege policies for Glue, Redshift, and S3
- Study VPC endpoints for S3 and DynamoDB, understand when they're required
Weeks 7-8: Practice Exams and Gap Filling
- Take 3 to 4 full-length practice exams (AWS Skill Builder has official ones)
- Review every wrong answer and trace it to the relevant AWS documentation page
- Focus on your weakest domain, most candidates underperform on security/governance
- Re-read the exam guide and ensure you can explain every listed topic in one sentence
- Practice scenario-based reasoning: given a requirement, pick the right service and justify it
- Schedule your exam for the end of this week while the material is fresh
Is It Worth It? An Honest Assessment
The answer depends on where you are in your career and what companies you're targeting. Here's a direct breakdown.
Yes, get it if...
- You're targeting roles at companies running on AWS (which is ~60% of the market). Recruiters at AWS-heavy shops filter for the cert in their ATS.
- You're transitioning from a different cloud or from on-premise. The cert gives you a structured way to learn AWS data services and a credential that validates the transition.
- You're early-career (L3-L4) and don't have production AWS experience on your resume yet. The cert fills that gap and gives you talking points for behavioral rounds.
- Your current company will pay for the exam and study time. $150 and 6-8 weeks of part-time study is a low-risk investment.
Skip it if...
- You already have 2+ years of production AWS data engineering experience. Interviewers care about what you've built, not whether you passed a multiple-choice test.
- You're applying exclusively to companies that use GCP or Azure. The cert has no cross-cloud credibility.
- You're at the L5-L6 level. At senior levels, system design and leadership experience matter infinitely more than certifications. No hiring manager is checking cert boxes for Staff DE candidates.
- You're choosing between cert study and interview prep. If you can only do one, interview prep has higher ROI. The cert tests breadth of service knowledge; interviews test depth of problem-solving.
Watch out
The cert tests breadth of service knowledge. Interviews test depth of problem-solving. If you can only invest time in one, interview prep beats cert prep on ROI for landing a DE role.
How It Compares to Databricks and GCP
Three cloud DE certs exist. They test different things and signal different specializations. Most candidates should pick one based on their target company's stack.
| Cert | Focus | Difficulty | Best for | Time |
|---|---|---|---|---|
| AWS DEA-C01 | AWS-specific services (Glue, Kinesis, Redshift, Lake Formation) | Moderate | DEs working in AWS-heavy environments | 6-8 weeks |
| Databricks DE Associate | Delta Lake, Spark SQL, Structured Streaming, Unity Catalog | Moderate | DEs using the Databricks Lakehouse platform | 3-4 weeks |
| GCP Professional DE | BigQuery, Dataflow, Pub/Sub, Cloud Composer, ML integration | Hard | DEs working in GCP environments or targeting Google | 8-10 weeks |
Frequently Asked Questions
How hard is the AWS Data Engineer certification exam?+
It's moderate difficulty. Easier than the GCP Professional DE cert (which includes case studies and ML), harder than the Databricks Associate (which is narrower in scope). Most candidates with 6 months of AWS experience and 6-8 weeks of study pass on the first attempt. The challenge isn't any single question; it's the breadth of services you need to know. Expect questions on 15+ AWS services.
What's the difference between DEA-C01 and the old AWS Data Analytics Specialty?+
The Data Analytics Specialty (DAS-C01) was retired and replaced by DEA-C01 in 2024. DEA-C01 is an Associate-level exam (easier than the old Specialty) with updated coverage including Lake Formation, modern Glue features, and Step Functions orchestration. If you held the old DAS-C01, it doesn't auto-convert. You'll need to take DEA-C01 separately.
Can I pass with just free-tier AWS resources?+
For 80% of the material, yes. S3, Glue (limited), Athena, DynamoDB, Lambda, Step Functions, and IAM all have free-tier usage. Redshift has a 2-month free trial. Kinesis and EMR don't have meaningful free tiers, so study those from documentation and practice questions rather than hands-on. AWS Skill Builder offers free practice exams.
Do I need the Solutions Architect or Cloud Practitioner cert first?+
No. DEA-C01 is an Associate-level exam with no prerequisites. That said, if you're completely new to AWS, spending a week on Cloud Practitioner fundamentals (VPC, IAM basics, S3, EC2) will give you context that makes the DE-specific material easier to absorb. Don't take the exam though; just study the concepts.
How does this cert affect salary?+
The cert itself doesn't change your comp band. But it can move you from 'no interview' to 'phone screen' at AWS-heavy companies, which is where salary is actually determined. Once you're in the interview loop, performance matters far more than credentials. Think of the cert as a door-opener, not a salary multiplier.
The Cert Gets You The Interview. It Doesn't Pass It.
Hiring managers want someone who can actually write a window function, not someone who can name the Kinesis shard limit. Build the skill the cert only claims to measure.