How to Pass the AWS Certified Machine Learning Engineer Associate (MLA-C01) Exam in 2026: An 8-Week Study Plan

The AWS Certified Machine Learning Engineer Associate (MLA-C01) is one of the most valuable cloud certifications you can hold in 2026, and a clear AWS MLA-C01 study plan is the difference between passing first time and burning a $150 exam fee. This guide gives you the exam facts you need and a realistic, week-by-week schedule that fits around a full-time job.

If you have already passed the AWS AI Practitioner or worked with Amazon SageMaker, you are closer than you think. MLA-C01 rewards people who can build and operate real machine learning pipelines, not people who memorise service names. This 8-week study plan is built around that reality.

What Is the AWS MLA-C01 Certification?

MLA-C01 is AWS's associate-level credential for machine learning engineers. It validates your ability to build, deploy, operationalise and maintain machine learning solutions on the AWS Cloud. It launched as the natural successor to the older AWS Certified Machine Learning Specialty (MLS-C01), which AWS retired on 31 March 2026.

The shift matters. The retired Specialty exam leaned heavily on data science theory and algorithm selection. MLA-C01 is an engineering exam. It tests whether you can take a model and run it in production: data pipelines, deployment, CI/CD, monitoring and security. That is exactly the skill set employers are short of in 2026.

Exam Tip: Because MLA-C01 is an associate exam that replaced a specialty one, the page-one search results are still thin. Study from the official exam guide and recent material rather than older MLS-C01 content, which covers different objectives.

AWS MLA-C01 Exam at a Glance

Here are the core facts every candidate should know before booking. These are the exam logistics you will be planning your eight weeks around.

The MLA-C01 exam costs $150 USD and contains 65 questions, of which 50 are scored and 15 are unscored research questions that do not affect your result. You have 130 minutes, and you need a scaled score of 720 out of 1000 to pass.

There are no formal prerequisites, but AWS recommends roughly one year of machine learning engineering experience and one year of hands-on experience with AWS services such as SageMaker. You can pass without hitting those exact numbers, but the more console time you have, the easier the scenario questions become.

The Four MLA-C01 Exam Domains

The MLA-C01 exam is split into four weighted domains. Your AWS MLA-C01 study plan should allocate time in proportion to these weights, because the exam does the same.

Domain 1: Data Preparation for Machine Learning (28%)

The largest domain. You need to ingest, transform, validate and prepare data for modelling. Expect questions on feature engineering, handling missing values, data formats, and the AWS services that do this work, including SageMaker Data Wrangler, AWS Glue, Amazon S3 and Feature Store.

Domain 2: ML Model Development (26%)

This covers choosing a modelling approach, training models, tuning hyperparameters, analysing performance and managing model versions. You will see questions on built-in SageMaker algorithms, evaluation metrics such as precision, recall and F1, and how to spot overfitting or bias.

Domain 3: Deployment and Orchestration of ML Workflows (22%)

The engineering heart of the exam. You choose deployment infrastructure and endpoints, provision compute, configure auto scaling, and build CI/CD pipelines to automate ML workflows. Know your SageMaker endpoint types (real-time, serverless, asynchronous and batch transform) cold.

Domain 4: ML Solution Monitoring, Maintenance and Security (24%)

This domain checks that you can keep a deployed model healthy and secure. Expect SageMaker Model Monitor, data and model drift detection, CloudWatch, and security topics such as IAM roles, encryption and VPC configuration for ML workloads.

Exam Tip: Domains 1 and 2 together make up 54% of the exam, but Domains 3 and 4 are where most candidates lose marks. If your background is data science rather than DevOps, weight your revision towards deployment, orchestration and monitoring.

Is the AWS MLA-C01 Worth It in 2026?

Short answer: yes, if you want to work in production machine learning. AWS remains the dominant cloud provider, and the bottleneck in ML hiring has shifted from building models to deploying and maintaining them reliably. MLA-C01 signals exactly that production readiness.

The associate level also makes it more accessible than the old specialty track, so you can earn a respected AI and ML credential without years of data science theory. For anyone who has already passed the AWS AI Practitioner, MLA-C01 is the logical next step up and a stronger signal to employers.

If you are still choosing between cloud AI tracks, our comparison of the AWS AI Practitioner and Azure AI Fundamentals is a useful starting point before you commit to the engineer-level exam.

The 8-Week AWS MLA-C01 Study Plan

This plan assumes around 8 to 10 hours of study per week. If you have less time, stretch it to 12 weeks. If you already work with SageMaker daily, you can compress it to 6. The structure stays the same: build foundations, then deployment, then monitoring, then relentless practice.

Week 1: Foundations and Exam Strategy

• Download and read the official AWS MLA-C01 exam guide end to end. Note the four domain weights.
• Set up an AWS free tier account and confirm billing alarms so practice does not cost you.
• Refresh core ML concepts: supervised versus unsupervised learning, training and inference, common metrics.
• Take a short diagnostic practice test to find your weak domains early.

Week 2: Data Preparation (Domain 1)

• Work through SageMaker Data Wrangler, AWS Glue and Amazon S3 for data ingestion and transformation.
• Practise feature engineering: encoding categorical data, scaling, handling imbalanced datasets.
• Build one hands-on lab: load a public dataset into S3, clean it in Data Wrangler, store features in Feature Store.
• Review data validation and how to detect data quality issues before training.

Week 3: ML Model Development Part 1 (Domain 2)

• Study SageMaker built-in algorithms and when to use each.
• Learn the training workflow: training jobs, hyperparameter tuning jobs, and automatic model tuning.
• Practise reading evaluation metrics and confusion matrices.
• Lab: train a model in SageMaker and run a hyperparameter tuning job.

Week 4: ML Model Development Part 2 and Versioning

• Cover model evaluation, bias detection with SageMaker Clarify, and explainability.
• Learn the SageMaker Model Registry and how to manage model versions.
• Revisit overfitting, underfitting and regularisation.
• Take a domain-focused practice quiz on Domains 1 and 2 combined.

Week 5: Deployment and Orchestration (Domain 3)

• Master the four SageMaker endpoint types: real-time, serverless, asynchronous and batch transform.
• Study auto scaling, multi-model endpoints and inference cost trade-offs.
• Learn ML pipeline orchestration with SageMaker Pipelines and AWS Step Functions.
• Lab: deploy a trained model to a real-time endpoint and invoke it.

Week 6: CI/CD and Automation (Domain 3)

• Build a CI/CD understanding for ML: CodePipeline, CodeBuild and how they automate retraining and deployment.
• Study infrastructure as code basics with CloudFormation for ML resources.
• Lab: wire a simple pipeline that retrains and redeploys a model when new data lands.
• Review everything in Domain 3 and take a focused practice set.

Week 7: Monitoring, Maintenance and Security (Domain 4)

• Learn SageMaker Model Monitor and how to detect data drift and model drift.
• Cover CloudWatch metrics, logs and alarms for ML workloads.
• Study security: IAM roles for SageMaker, encryption at rest and in transit, and VPC configuration.
• Take a full domain quiz on Domain 4.

Week 8: Full Mock Exams and Review

• Sit at least three full-length, timed mock exams on different days.
• After each, review every wrong answer and the reasoning, not just the right option.
• Target a consistent 80% or higher across mocks before you book the real exam.
• Do a final light pass over your weakest domain the day before. Do not cram new material.

Exam Tip: Do not book your exam until you are scoring 80% or above on at least three separate full-length mocks taken on different days. One lucky high score is not readiness.

Common MLA-C01 Mistakes to Avoid

A few patterns sink otherwise well-prepared candidates. Learn from them now.

• Studying theory, ignoring the console. This is an engineering exam. Candidates who have actually deployed an endpoint score far higher on scenario questions than those who only read.
• Underweighting deployment and monitoring. Domains 3 and 4 are 46% of the exam combined. Data scientists especially tend to neglect them.
• Using old MLS-C01 material. The retired specialty exam had different objectives. Make sure every resource you use is mapped to MLA-C01.
• Memorising every service. You do not need to know every AWS service. You need to know which SageMaker feature solves a given ML engineering problem.

How CertCrush Helps You Pass MLA-C01

Reading documentation will only take you so far. The fastest way to find your gaps is to answer exam-style questions under timed conditions, then review the explanations until the reasoning sticks. That is exactly what CertCrush is built for.

Our practice questions mirror the real MLA-C01 format, including the scenario, ordering and matching styles AWS uses, so there are no surprises on exam day. Browse the full catalogue on our courses page and build the question-bank habit into Weeks 4 through 8 of the plan above.

If you are weighing the cost of structured prep against another failed attempt, our pricing page lays out the options.

Ready to Start Practising?

The AWS MLA-C01 exam rewards engineers who practise, not people who only read. Eight weeks is enough time to pass if you put hands on the console and test yourself relentlessly with realistic questions.

Create your free CertCrush account today, start your MLA-C01 practice questions, and walk into the exam knowing you have already seen questions like the ones in front of you. Your first pass starts now.