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MLS-C01 exam structure, score distribution by domain, time management strategy, answer elimination techniques, and final study plan.

AWS Certified Machine Learning - Specialty Exam Prep

Part 4: Review & Exam Strategy

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1. MLS-C01 Exam Structure

Item	Details
Total Questions	65 questions
Scored Questions	50 questions (15 unscored/experimental)
Time Limit	180 minutes (3 hours)
Passing Score	750/1000
Question Format	Multiple choice + Multiple response
Languages	Available in Japanese, Korean (not Vietnamese)
Validity	3 years

2. Domain Weights — Score Distribution

Domain	Weight	Questions (~)
Domain 1: Data Engineering	20%	~13 questions
Domain 2: Exploratory Data Analysis	24%	~16 questions
Domain 3: Modeling	36%	~24 questions
Domain 4: ML Implementation & Operations	20%	~13 questions

Exam tip: Domain 3 (Modeling) accounts for 36% — this is the most important domain. Prioritize studying: SageMaker built-in algorithms, evaluation metrics, hyperparameter tuning, and underfitting/overfitting.

3. Time Management Strategy

Time Budget (180 minutes, 65 questions):

Average: 2.5 minutes per question

Strategy:
Round 1 (120 min): Attempt all 65 questions
  - Answer confident questions immediately
  - Flag uncertain questions (don't spend > 3 min each)
  
Round 2 (45 min): Review flagged questions
  - Apply elimination technique
  - Check scenario keywords

Round 3 (15 min): Final review
  - Check unmarked questions
  - Don't change answers unless certain

4. Answer Elimination Techniques

Keyword in Question	Eliminate	Keep / Prefer
"no labels / unlabeled data"	Supervised algorithms	K-Means, RCF, PCA
"quickest / easiest / no code"	Custom SageMaker models	Managed AI services
"real-time predictions"	Batch Transform	Real-time Endpoint
"low traffic, cost-efficient inference"	Real-time Endpoint	Serverless Inference
"no internet, secure"	Public endpoint	VPC + Network Isolation
"imbalanced dataset"	Accuracy metric	F1, Precision, Recall, AUC
"multiple models, save cost"	Separate endpoints	Multi-Model Endpoint
"structured tabular data"	Image/NLP algorithms	XGBoost, Linear Learner

5. Final Study Plan

Day	Focus
Day 1	Review Domain 3 (Modeling): algorithms + metrics + HPO
Day 2	Review Domain 2 (EDA): data preparation + feature engineering
Day 3	Review Domain 1+4 (Data Engineering + Operations)
Day 4	Practice exam 1 (65 questions): identify weak areas
Day 5	Review weak areas from Practice Exam 1
Day 6	Practice exam 2 (65 questions)
Day 7	Cheat sheet review only — rest, no new learning

6. Exam Day Tips

Arrive 30 minutes early (Pearson VUE test center) or thoroughly check your environment (online proctored)
Read the entire question — don't just skim the first 2-3 lines
Highlight keywords: "MOST cost-effective", "LEAST operational overhead", "FASTEST to deploy"
When two answers seem correct: pick the one that best matches the constraints in the question
Never leave a question blank — guess if you don't know (no penalty for wrong answers)