Earn AWS ML Specialty Certification
10 weeks · 0 milestones
Pass the AWS Certified Machine Learning Specialty exam on first or second attempt.
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Assess baseline knowledge, identify gaps, and build the study plan
1–2 weeks to assess and plan
The AWS Machine Learning Specialty certification tests applied ML knowledge across four domains: Data Engineering, Exploratory Data Analysis, Modelling, and ML Implementation & Operations. Before committing to a study plan, take the AWS 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 the AWS official sample questions or a paid practice exam provider) 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 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 — the AWS ML Specialty exam typically requires 150–300 hours of preparation for candidates without prior AWS or deep ML background; the plan must account for current background knowledge honestly
Complete the study program and validate readiness with practice exams
Pass the AWS Machine Learning Specialty exam
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