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.
First-attempt pass
Exam fee
Questions
Time limit
Source: DataDriven analysis of 1,042 verified data engineering interview rounds.
Key numbers for the AWS Certified Data Engineer Associate (DEA-C01).
65
Questions
Multiple choice + multi-select
170 min
Duration
Proctored
~720
Passing Score
Out of 1000 (scaled)
$150
Cost
USD per attempt
3 years
Validity
Then recertify
Remote
Format
Pearson VUE or PSI
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.
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.
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.
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.
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.
The exam covers 20+ AWS services, but these six account for roughly 70% of the questions. Deep knowledge here is non-negotiable.
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.
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.
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.
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.
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.
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.
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.
The answer depends on where you are in your career and what companies you're targeting. Here's a direct breakdown.
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.
Focus: AWS-specific services (Glue, Kinesis, Redshift, Lake Formation)
Best for: DEs working in AWS-heavy environments
Focus: Delta Lake, Spark SQL, Structured Streaming, Unity Catalog
Best for: DEs using the Databricks Lakehouse platform
Focus: BigQuery, Dataflow, Pub/Sub, Cloud Composer, ML integration
Best for: DEs working in GCP environments or targeting Google
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.
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