Earn AWS Certified Machine Learning Engineer – Associate
10 weeks · 0 milestones
Pass the AWS Certified Machine Learning Engineer – Associate exam (MLA-C01). The exam covers four domains: Data Preparation for Machine Learning, ML Model Development, Deployment and Orchestration of ML Workflows, and ML Solution Monitoring, Maintenance, and Security. Passing score: 720/1000.
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Milestone map
3 milestones
The AWS Certified Machine Learning Engineer – Associate (MLA-C01) tests applied ML knowledge across four domains: Data Preparation for Machine Learning (28%), ML Model Development (26%), Deployment and Orchestration of ML Workflows (22%), and ML Solution Monitoring, Maintenance, and Security (24%). Before committing to a study plan, take AWS Skill Builder's official practice exam to establish a baseline score and identify which domains need the most attention. The study plan must be specific: hours per domain, resources to complete, and a target date for the exam based on realistic study time.
Proof required
Submit your baseline practice exam score (from AWS Skill Builder official sample questions or equivalent) with a breakdown of performance by domain, your gap analysis (which domains are below your target threshold and which specific knowledge areas within those domains you need to strengthen), and your study plan showing at minimum: total planned study hours, weekly time allocation, resources assigned to each domain, and target exam date.
What gets checked
- Baseline assessment used a structured practice question set — not self-assessment ('I think I know ML') but an actual score from practice questions that map to the four MLA-C01 exam domains
- Study plan allocates more time to lower-scoring domains, not proportionally by domain size — a study plan that spends equal time on all domains ignores the diagnostic information from the baseline
- Target exam date is realistic — MLA-C01 typically requires 100–200 hours of preparation for candidates with prior Python and ML fundamentals background; candidates without hands-on AWS experience should allocate extra time for the deployment and monitoring domains