MLA-C01 FAQ — Common Questions (AWS Machine Learning Engineer Associate)

What is AWS Certified Machine Learning Engineer — Associate (MLA-C01)?

MLA-C01 is an associate-level AWS certification focused on building, deploying, and operating ML solutions and pipelines on AWS, with a strong emphasis on Amazon SageMaker and practical MLOps.

If you want the fastest orientation, start with the section overview and keep the official exam guide from Resources open while you study.

What score do you need to pass MLA-C01?

AWS uses a scaled score (100–1000). The minimum passing score is 720.

How many questions and how much time?

• 65 questions
• 130 minutes
• Multiple-choice and multiple-response

Do you need to code for MLA-C01?

You don’t need to write production code during the exam, but you should be comfortable with:

• The ML lifecycle (data prep → training → evaluation → deployment → monitoring)
• Common MLOps concepts (versioning, CI/CD, monitoring, retraining triggers)
• Choosing the right AWS services and endpoint types for a scenario

What AWS services should you know for MLA-C01?

At a high level, expect to see:

• Amazon SageMaker (training, endpoints, pipelines, model registry, monitoring)
• Data prep and ETL tools (for example, AWS Glue, SageMaker Data Wrangler)
• Storage and data sources (Amazon S3, plus common data stores)
• Observability and governance (CloudWatch, CloudTrail, cost tooling)
• Security primitives (IAM, encryption, VPC basics)

Use the Cheat Sheet for a service-by-use-case map.

How long should you study for MLA-C01?

Typical ranges (varies with hands-on experience):

• Strong SageMaker + ML background: 40–60 hours
• Some AWS and some ML, but not both deeply: 60–90 hours
• New to ML engineering on AWS: 90–120+ hours

Pick a schedule you can sustain, then cycle between the Cheat Sheet and Resources so your study plan stays tied to real deployment decisions.

Is MLA-C01 closer to “data science” or “engineering”?

More engineering. The emphasis is on operationalizing ML: data pipelines, training and tuning workflows, deployment endpoints, CI/CD, monitoring, cost management, and security.

How do you practice effectively for MLA-C01?

Follow a loop:

1. Read one objective area from the official exam guide in Resources
2. Review the matching deployment or monitoring pattern in the Cheat Sheet
3. Write 3–5 “miss rules” from what you got wrong
4. Re-drill weak tasks 48–72 hours later (spaced repetition)