The AWS Certified Data Analytics – Specialty exam (DAS-C01) is retired. AWS ended the exam’s availability on April 9, 2024, after a final testing date of April 8. It cannot be scheduled or renewed through another DAS-C01 sitting in 2026. That distinction belongs at the beginning of any discussion of the credential: the historical syllabus still teaches useful analytics architecture, but it is no longer an active certification preparation target.
AWS launched the Data Engineer – Associate certification in 2024 as part of a broader shift toward role-aligned credentials. It is not simply DAS-C01 with a new code or identical level of difficulty. The current AWS Data Engineer – Associate (DEA-C01) examines pipeline implementation, data-store management, operational support and governance. A professional choosing their next credential should evaluate the work they actually perform before treating a retired specialty title as their only roadmap.
What DAS-C01 originally assessed
The historical specialty examination organized its scope around Collection (18%), Storage and Data Management (22%), Processing (24%), Analysis and Visualization (18%) and Security (18%). These percentages describe the retired DAS-C01 blueprint, not the distribution of scored questions for the current data engineer exam. They remain a useful record of how AWS once grouped analytics responsibilities across a single specialty assessment.
The underlying technical problems have not vanished. Teams still ingest streams and batches, choose durable storage, transform data, make datasets queryable, and protect access to sensitive information. What changed is the certification lifecycle, the emphasis on particular job responsibilities, and the versions and service names in active AWS documentation.
A historical book that teaches the difference between a streaming buffer and an S3 archive may still be valuable. Its dated exam-booking instructions, domain weights and references to discontinued services are not reliable for a new candidate. Separate the enduring engineering concept from the claim about what an active exam tests.
Understand collection as a reliability problem
Suppose a utility company collects readings from remote meters. Some devices send events continuously; others upload files once daily after connectivity returns. A useful collection design must identify source time, device identity, unique reading IDs and expected late-arrival patterns. Capturing the event is only the first step; the system must know how to detect gaps and avoid counting the same reading twice.
Amazon Kinesis Data Streams can provide a bounded-replay stream for suitable real-time events. Amazon Data Firehose can deliver supported records into destinations such as S3. Batch uploads may be sufficient for daily meter extracts. Mixing the two is reasonable when the business needs both near-real-time alerts and durable historical reporting.
The historical Kinesis family also demonstrates why a retired exam’s study notes cannot be reused without review. Kinesis Data Firehose was renamed Amazon Data Firehose in 2024. Kinesis Data Analytics for SQL was discontinued in January 2026; maintained stream processing has different current product names. A candidate should never build a new lab from instructions requiring an unavailable AWS application.
Choose storage to support the question, not the diagram
Object storage, a searchable index and a warehouse can serve different workloads. Amazon S3 suits durable raw and curated files, while Amazon Redshift serves many analytical warehouse needs. Amazon OpenSearch Service may make selected documents searchable for operational investigation. No one destination is best for every latency, query, update or retention requirement.
For the utility example, preserve source meter records in an S3 landing zone with appropriate retention. A cleaned dataset might be partitioned by processing date or region for analytical access, while a limited operational search index provides recent device-error diagnostics. Decide which system is authoritative for corrections and how a derived table or index can be rebuilt from the retained source.
Before selecting formats and partitions, inspect query patterns. Frequent time-bounded analysis can benefit from well-designed partitioning and columnar files. Very high-cardinality partitions can create overhead, and thousands of small files can be expensive to process. S3 data-lake architecture connects these storage choices with a practical learning exercise.
Processing and cataloging should preserve meaning
The transformed meter dataset might normalize units, identify missing identifiers, mark implausible readings and join devices to an approved location reference. AWS Glue can catalog and transform suitable inputs, but it cannot determine the business meaning of a field without a defined contract. A technically successful ETL job can still make every aggregate wrong if a join duplicates rows or a temperature value is interpreted in the wrong unit.
Test the transformation using examples with known answers: one duplicated event, one late file, one changed schema, one missing device reference and one partial upstream batch. Show how rejected records are retained for diagnosis and how retries avoid double-counting. A pipeline is operationally reliable when a repeat run produces the intended logical result rather than an additional copy of every record.
Historical specialty preparation sometimes encouraged memorizing service feature lists. A stronger contemporary approach is to explain why one design meets the consumer’s accuracy and freshness constraints while another might fail. This reasoning is useful both to data engineers and to analysts who must trust the resulting dataset.
Security and governance cross the entire data lifecycle
Data protection begins before storage. Collection endpoints need appropriate credentials, streams and objects need least-privilege permissions, and processing jobs need controlled access to secrets and keys. An encrypted dataset is not secure if a broad IAM policy lets every employee download it. Likewise, a carefully permissioned catalog table does not protect an unrestricted temporary copy left in an auxiliary bucket.
For the utility company, an engineer might require raw readings for debugging while a regional analyst should see approved aggregates without detailed customer identifiers. Lake Formation can help govern supported data-lake access, while IAM and storage-level controls retain independent responsibilities. Test access using the actual analyst role, not a privileged developer session.
Retention, lineage and privacy are also governance decisions. Record which source batches and transformation versions produced a published metric. A disputed monthly report should be traceable back to actual input data, not defended only by pointing to a successful scheduler run.
How DEA-C01 differs from the retired specialty
As of October 2026, the active AWS Certified Data Engineer – Associate exam code is DEA-C01. Its official content domains are Data Ingestion and Transformation (34%), Data Store Management (26%), Data Operations and Support (22%) and Data Security and Governance (18%). This four-domain arrangement is different from the five-domain historical DAS-C01 distribution.
AWS currently describes DEA-C01 as a 65-question exam with a 130-minute testing window. It targets professionals with practical data engineering experience and tests implementing, monitoring and improving pipelines. The exam is associate-level, not a direct one-to-one transfer of the old specialty’s credential status or an automatic recertification route for retired holders.
A candidate preparing for DEA-C01 should use the current official AWS exam guide to decide which services and task statements deserve attention. A retired DAS-C01 guide may provide secondary background on storage or analytics systems, but its domain percentages, question emphasis and registration guidance cannot be presented as the modern exam map.
Build a current hands-on learning sequence
Start with a small source file and a stream of synthetic events. Preserve raw data in S3, define a schema, create a catalog entry and write a transformation that normalizes, validates and partitions a curated output. Then query it with Athena or another justified engine. Record every assumption about identifiers, event time, late data and output correctness.
Next, introduce failure: corrupt one record, repeat a batch, change a column type or revoke a job role’s permission. Watch the actual logs and error metrics and determine whether data was partially written. Document the recovery procedure and show that a second execution does not duplicate business records. This tests a real pipeline competency rather than memorized product marketing.
Finally, add controlled access and operational monitoring. Confirm that the intended analyst can query the correct dataset, an unauthorized identity cannot, and an alert fires when the source-to-curated data freshness falls behind. Explain the resulting trade-offs in terms of cost, reliability, correctness and security. Each requirement should be tied to a concrete test that another engineer could reproduce.
Choose your next credential with the dates made clear
DAS-C01 is a retired historical assessment. Existing holders may still have valid credentials within their original validity periods, but no new DAS-C01 exam appointments or renewals are available. DEA-C01 is a current role-aligned option for data engineers; it should be selected because its assessed tasks match the candidate’s work, not because a blog implies every former specialty exam has an identical successor.
The strongest preparation evidence is a pipeline whose data can be traced, checked, protected, replayed and recovered—not an outdated exam badge or a copied service list. Preserve the historical five-domain syllabus for context, follow AWS’s current certification guides for new scheduling decisions, and study the engineering responsibilities that remain useful regardless of exam version.