AWS Machine Learning Engineer Associate Exam Prep
The AWS Certified Machine Learning Engineer - Associate (MLA-C01) validates your ability to build, train, tune, and deploy machine learning models using AWS services. This certification bridges the gap between foundational AI knowledge and hands-on ML engineering.
This exam is being replaced
The last day to take MLA-C01 is 28 September 2026. It is replaced by MLA-C02.
The last day to take MLA-C01 in English is 28 September 2026. Registration for the updated MLA-C02 opens 1 September 2026. MLA-C02 expands into generative AI — fine-tuning foundation models, RAG architectures, agentic workflows and Amazon Bedrock — on top of the existing ML engineering content. If you can sit MLA-C01 before the cutoff, do; otherwise prepare for MLA-C02.
Confirm on the official AWS certification page130 minutes
65 questions
720/1000
$150 USD
Exam Domain Breakdown
MLA-C01 Practice Exam Questions
The MLA-C01 exam gives you 130 minutes for 65 questions, and you need 720/1000 to pass. Reading study notes does not tell you whether you would clear that bar today — only answering exam-style questions under the same conditions does.
Practice is most useful when it follows the exam blueprint rather than a random question bank. Data Engineering for ML alone accounts for 28% of your MLA-C01 score, so a weak spot there costs you far more than an equivalent gap in a lighter domain. Practising by domain shows you where your points are actually leaking.
Start with a free MLA-C01 practice exam
Answer 15 exam-style MLA-C01 questions in about 10 minutes. You will see your result broken down by domain and where your gaps are. No card required — create an account so your answers are saved. Check the deadline above before you book — MLA-C01 is not available indefinitely.
Start free MLA-C01 practice examWho Should Take This Exam
- ML Engineers
- Data Scientists transitioning to engineering
- Software Engineers building ML pipelines
- AI/ML practitioners on AWS
What to Expect on the MLA-C01 Exam
The ML Engineer Associate exam covers the complete ML lifecycle: data preparation, model training, hyperparameter tuning, deployment, and monitoring. Expect questions on SageMaker (training jobs, endpoints, processing jobs, pipelines, feature store, model registry), data transformation with AWS Glue, and feature engineering best practices.
A significant portion of the exam focuses on ML operations (22%) — deploying models to production endpoints, setting up A/B testing with traffic splitting, configuring auto-scaling for inference endpoints, and monitoring model performance for drift. The security domain (24%) covers data encryption, VPC configurations for SageMaker, IAM roles, and ML governance with SageMaker Model Cards and Model Dashboard.
Study Strategy for ML Engineer Associate
This exam requires both ML knowledge and AWS service expertise. Start by understanding the SageMaker ecosystem thoroughly — it's the core of most exam questions. Know the differences between SageMaker built-in algorithms (XGBoost, Linear Learner, Image Classification), how to bring your own container, and when to use SageMaker vs managed AI services like Comprehend or Rekognition.
For the data engineering domain (28%), understand data pipelines using Glue, Kinesis, and S3, and how to handle common data quality issues. Practice with questions about choosing the right instance type for training vs inference, and know how to optimize costs with Spot instances for training jobs.
Career Value and Prerequisites
The ML Engineer Associate fills a critical gap in the AWS certification portfolio — it validates practical ML engineering skills that are in extreme demand. As companies move from ML experimentation to production deployment, the need for engineers who can build reliable ML pipelines on AWS is growing rapidly. Salaries for certified ML engineers typically range from $140,000 to $180,000.
Recommended prerequisites include 1-2 years of experience with ML/data science concepts and familiarity with AWS services. The AI Practitioner certification provides useful foundational knowledge. This is also a natural stepping stone toward the Machine Learning Specialty for those wanting deeper expertise.
Ready to start preparing?
Take a free 10-minute AI assessment to identify your knowledge gaps for the AWS Machine Learning Engineer Associate exam.
Start Free AssessmentFrequently Asked Questions
What is the AWS Machine Learning Engineer Associate exam?
The MLA-C01 is an associate-level certification that validates your ability to build, train, and deploy ML models on AWS. It covers data engineering for ML (28%), model development (26%), ML operations and deployment (22%), and security/governance for ML (24%). It's 170 minutes with 65 questions.
Do I need a data science background for MLA-C01?
You need basic ML knowledge (understanding of training, inference, common algorithms, evaluation metrics) but you don't need to be a data scientist. The exam focuses more on the engineering aspects: building data pipelines, training models with SageMaker, deploying endpoints, and monitoring model performance.
What AWS services should I know for ML Engineer Associate?
SageMaker is the most important service — know training jobs, endpoints, processing jobs, pipelines, feature store, and model registry. Also study AWS Glue for data preparation, Kinesis for streaming data, Bedrock for generative AI, and S3 for data storage. Understand IAM roles and VPC configurations for ML workloads.
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