AWS Data Engineer Associate vs Machine Learning Engineer Associate: Which AWS Cert Should You Take?

Take the AWS Certified Data Engineer Associate (DEA-C01) if your job is building and running data pipelines and data platforms, moving and transforming data with services like Glue, Redshift, Kinesis, and Athena. Take the AWS Certified Machine Learning Engineer, Associate (MLA-C01) if your job is building, deploying, and operating machine learning models, mostly with Amazon SageMaker. Both are associate-level exams, both cost US$150, and both have 65 questions. They sit next to each other in AWS's data and AI track, but they test different jobs. If you build the data foundation, take Data Engineer. If you build models on top of it, take ML Engineer.

These two get confused because data engineering and machine learning overlap in real teams, and AWS placed both exams at the associate tier around the same time. A data engineer often prepares the datasets a machine learning engineer trains on, so the skills touch. But the exams reward different depth, and picking the one that matches your day job saves weeks of studying the wrong material.

The quick comparison

What the Data Engineer Associate actually tests

DEA-C01 is about the data platform. It checks that you can ingest data in batch and streams, transform it, store it in the right place, operate the pipelines in production, and secure and govern it. The four domains are Data Ingestion and Transformation (34 percent), Data Store Management (26 percent), Data Operations and Support (22 percent), and Data Security and Governance (18 percent). More than a third of the exam is ingestion and transformation, so the heart of it is choosing between overlapping AWS services for a described pipeline: Glue versus EMR, Kinesis versus Managed Streaming for Kafka, Redshift versus Athena.

AWS refreshed the DEA-C01 exam guide to Version 1.1 in December 2025, adding modern data topics like Apache Iceberg open table formats and even integrating large language models into data processing. That reflects where data platforms are heading. This is the right exam for data engineers, analytics engineers, and ETL developers who own the pipeline. You can prepare by turning your notes into scenario questions with the AWS Data Engineer Associate practice exam generator, weighting ingestion and data stores.

What the ML Engineer Associate actually tests

MLA-C01 is about the machine learning lifecycle in production. It checks that you can prepare data for modeling, develop and train models, deploy and orchestrate them, and monitor and secure them once they are live. Its four domains are Data Preparation (28 percent), ML Model Development (26 percent), Deployment and Orchestration (22 percent), and Monitoring, Maintenance, and Security (24 percent). It leans heavily on Amazon SageMaker and expects you to understand MLOps: how a model gets from a notebook to a monitored endpoint that does not silently drift.

This is the right exam for machine learning engineers and MLOps practitioners. One scheduling note: the exam version is changing. AWS opens registration for MLA-C02 on September 1, 2026, and the last day to sit MLA-C01 in English is September 28, 2026. If you are studying now, confirm which version you will sit. You can drill either version's concepts with the AWS Machine Learning Engineer Associate practice exam generator.

The overlap: data preparation

The one place these exams genuinely meet is data preparation. Both expect you to clean, transform, and shape data. On the Data Engineer exam, that is the whole job: building robust pipelines that deliver quality data. On the ML Engineer exam, data preparation is the first 28 percent, framed around getting data ready for a specific model. If you already hold one, that shared ground makes the other easier, but the framing differs. A data engineer thinks about throughput, cost, and governance across the whole platform; a machine learning engineer thinks about feature quality for a model. Clean, well-governed pipelines are the backbone that both roles and downstream data quality tooling depend on, which is why teams increasingly pair their AWS platform with dedicated data lineage and observability to catch problems before they reach a model.

Which should you take first?

Match the exam to your current role, not your aspiration. If you spend your days writing Glue jobs, wiring up Kinesis streams, tuning Redshift, or managing a data lake, take Data Engineer first. It certifies what you already do and is the more broadly applicable credential, since almost every AWS shop needs data pipelines. If you spend your days in SageMaker training and deploying models, take ML Engineer first.

If you do both, or you want a deliberate path, Data Engineer first is the logical order. The data platform is the foundation machine learning runs on, and understanding ingestion, storage, and governance makes the ML Engineer material easier because you already know where the training data comes from and how it is secured. Many people end up holding both, which is a strong combination for anyone working on data-heavy AWS systems.

How to study for either exam

Both exams are scenario-based, so passive reading is the slow way to prepare. The questions describe a workload and a constraint and ask which AWS service or pattern fits best, where several options technically work. That is a judgment you build by doing many scenarios, not by rereading service documentation. Upload your study notes or the exam guide and generate practice questions that mirror the format, then retake fresh sets until the best service for a described situation comes back instantly. Aim to clear practice sets comfortably above the 720 scaled bar before you book, and for either exam, confirm the current exam guide on the AWS certification page so your practice reflects the latest scope.

The bottom line

Data Engineer Associate and Machine Learning Engineer Associate are not competitors; they are neighbors. Data Engineer builds and runs the data platform; ML Engineer builds and runs models on top of it. Both are US$150 associate exams with the same 65-question format and 720 passing score. Choose the one that matches your job today, take Data Engineer first if you want a foundation, and if you work across the full data-and-AI stack, plan to earn both.