How to pass the AWS Machine Learning Associate certification with a 100% score

The AWS Certified Machine Learning Engineer Associate exam (MLA-C01) is designed for builders who turn data into working ML systems on AWS.

It validates that you can prepare data, choose sensible modeling approaches, train and tune efficiently, deploy with the right endpoint pattern, automate with CI/CD, monitor for drift, and keep solutions secure.

If you are plotting a full certification path, this exam pairs nicely with the AWS AI Practitioner, the advanced AWS Machine Learning Specialty, and platform credentials like Solutions Architect Associate or DevOps Engineer Professional.

For cross-cloud perspective, see GCP ML Engineer Professional and the GCP Generative AI Leader.

Exam Basics

You will answer multiple-choice and multiple-response items along with formats such as ordering, matching, and short case studies. Scores are reported from 100 to 1,000; the minimum passing score is 720. AWS uses a compensatory model, so strong performance in one domain can offset weaker areas. Expect a handful of unscored items that AWS is piloting for future releases. To get a feel for question style, skim adjacent blueprints like Cloud Practitioner and Data Engineer Associate, then watch this quick study primer: Exam Strategy Video.

Content Domains And Weights

Domain 1: Data Preparation For Machine Learning – 28%
Domain 2: ML Model Development – 26%
Domain 3: Deployment And Orchestration Of ML Workflows – 22%
Domain 4: ML Solution Monitoring, Maintenance, And Security – 24%

Domain 1: Data Preparation For Machine Learning

Get comfortable ingesting and shaping data with services you will reuse across roles. Land datasets in Amazon S3, mount high-throughput file systems with Amazon EFS or Amazon FSx, and stream events with Amazon Kinesis. Use AWS Glue, AWS Glue DataBrew, and SageMaker Data Wrangler to clean, encode, scale, and join. Register reusable features in the SageMaker Feature Store and manage labels with SageMaker Ground Truth.

Formats and access: Parquet, ORC, Avro, JSON, CSV stored on Amazon S3, queried or staged for SageMaker training
Quality and fairness: validate with AWS Glue Data Quality, probe bias and distributions with SageMaker Clarify
Security: encrypt with AWS KMS and control access using AWS IAM

Domain 2: ML Model Development

Pick approaches that match the business problem and data shape. Start from SageMaker built-in algorithms or fine-tune foundation models via SageMaker JumpStart and integrate managed AI where appropriate, such as Amazon Comprehend, Amazon Rekognition, Amazon Transcribe, Amazon Translate, and Amazon Q.

Training: use Script Mode with TensorFlow or PyTorch, distribute when needed, and log to Amazon CloudWatch
Tuning: run SageMaker Automatic Model Tuning to explore hyperparameters efficiently
Evaluation: choose metrics that fit the task (Accuracy, F1, RMSE, ROC/AUC) and interpret with SageMaker Clarify
Governance: track lineage and versions in the SageMaker Model Registry

Domain 3: Deployment And Orchestration Of ML Workflows

Choose the right endpoint pattern on SageMaker: real-time, asynchronous, batch transform, or serverless. Balance cost and latency with instance families and auto scaling. When you need containers, publish images to Amazon ECR and orchestrate with Amazon ECS or Amazon EKS, or bring your own container directly to SageMaker.

IaC: model infrastructure with AWS CloudFormation or the AWS CDK
CI/CD: automate train-evaluate-register-deploy using AWS CodePipeline, AWS CodeBuild, and AWS CodeDeploy with SageMaker Pipelines and Amazon EventBridge
Rollback and safety: use blue/green or canary strategies, which you will also see on DevOps Engineer Professional

Domain 4: ML Solution Monitoring, Maintenance, And Security

Watch the health of both your models and your platform. Detect drift with SageMaker Model Monitor, trace latency outliers with AWS X-Ray, and right-size with SageMaker Inference Recommender. Manage spend via AWS Budgets, AWS Cost Explorer, and purchasing options like On-Demand, Spot, Reserved capacity, and SageMaker Savings Plans. Protect data and endpoints using IAM, KMS, private VPC connectivity, and least-privilege roles.

Out Of Scope

You are not expected to design enterprise-wide cloud architectures, implement every third-party tool, write complex custom algorithms from scratch, or integrate with other clouds like Google Cloud during the exam. The focus is hands-on ML engineering on AWS.

How To Prepare

Download the blueprint and map it: use the domains above and cross-reference with related pages like AI Practitioner and ML Specialty to see how topics connect.
Baseline with practice: take a starter set of questions from adjacent tracks such as Cloud Practitioner or Developer Associate to learn AWS question patterns. If you like drills, try focused sets on platforms like Udemy.
Build a mini project: in SageMaker Studio, ingest with AWS Glue, engineer features in Data Wrangler, train and AMT-tune, register in the Model Registry, deploy a real-time endpoint, and set up Model Monitor.
Automate: wire CodePipeline, CodeBuild, and CodeDeploy with Amazon EventBridge to run train-evaluate-deploy on commit.
Iterate with timed mocks: document why the correct answer meets “lowest latency,” “least effort,” or “most cost-effective,” and why each distractor fails. Revisit weak spots with targeted reading across Architecture, Security, and Data Engineering.
Watch a quick refresher: Study Strategy Video to tune pacing and question triage.

Next Steps

After MLA-C01, deepen ML with the ML Specialty, broaden platform design with the Solutions Architect Associate and Professional, or sharpen delivery skills with DevOps Engineer Professional. If you want leadership-level Gen-AI breadth, compare with GCP Generative AI Leader.

Next Steps

The AWS Solutions Architect Book of Exam Questions by Cameron McKenzie

So what’s next?

A great way to secure your employment or even open the door to new opportunities is to get certified.

If you’re interested in AWS products, here are a few great resources to help you get Cloud Practitioner, Solution Architect, Machine Learning and DevOps certified from AWS:

Put your career on overdrive and get AWS certified today!