MLA-C01 Mock Exams & Practice Exam Questions | AWS Certified Machine Learning Engineer - Associate

Tip: MLA-C01 is 65 questions / 130 minutes. Start with task drills (learn patterns fast), then do mixed sets to build transfer across domains.

Suggested progression

1. Task drills (daily): 20–25 questions focused on one task.
2. Mixed sets (2×/week): 30–40 questions spanning 2–3 domains.
3. Timed runs (final week): 2–3 full-length runs (65 questions / 130 minutes); review every miss and re-drill weak tasks.

What to pair with practice

• Study Plan: pick a 30/60/90 day timeline → view
• Syllabus: objective-by-domain outline → view
• Cheatsheet: high-yield patterns + service pickers → open
• Overview: format, domains, and study strategy → read

Exam at a glance

• Exam name: AWS Certified Machine Learning Engineer — Associate (MLA-C01)
• Level: Associate
• Questions: 65 total (multiple-choice and multiple-response)
• Time: 130 minutes
• Delivery: Pearson VUE testing center or online proctored exam
• Result: Scaled score (100–1000); minimum passing score: 720
• Cost: 150 USD
• Languages offered: English, Japanese, Korean, Simplified Chinese

Note: AWS includes scored and unscored questions; the exam guide indicates 50 scored + 15 unscored (total 65).

Domain breakdown (weights)

• Domain 1: Data Preparation for Machine Learning (ML) — 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%

What the exam emphasizes (high level)

Expect scenario-driven items where you choose the best answer for:

• Ingesting, transforming, validating, and preparing data for modeling
• Selecting modeling approaches, training and tuning models, and analyzing performance
• Choosing deployment endpoints and infrastructure, plus orchestration and CI/CD for ML workflows
• Monitoring model inference and infrastructure, managing costs, and securing ML systems

The exam is very SageMaker-forward (Feature Store, Data Wrangler, Model Registry, monitoring and deployment patterns), with a strong MLOps and ops-reliability flavor.

Who should take MLA-C01

This exam is a strong fit for:

• ML engineers and MLOps engineers working with Amazon SageMaker
• Data engineers and backend engineers who deploy ML features into production systems
• DevOps engineers supporting ML pipelines and model delivery

AWS’s intended candidate guidance: at least 1 year of experience using Amazon SageMaker and other AWS services for ML engineering, plus experience in a related role (for example, backend developer, DevOps engineer, data engineer, MLOps engineer, or data scientist).

Study plan (efficient)

1. Pick a timeline: 30/60/90-day Study Plan →
2. Work the Syllabus task-by-task; drill immediately after each task.
3. Keep a miss log: convert misses into one-liner rules (“RAG isn’t the answer here—this is model monitoring”, “Choose serverless vs real-time endpoints based on latency and traffic shape”).
4. Final 1–2 weeks: mixed sets + at least a couple timed runs; review every miss.