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6.1.1. Exam Structure, Question Types, and Scoring

First Principle: A clear understanding of the exam's mechanics and cognitive objectives guides efficient study and tactical test-taking, maximizing your chances of success.

The AWS Certified Machine Learning - Specialty (MLS-C01) exam validates a candidate's advanced technical skills and experience in designing, implementing, deploying, and maintaining machine learning solutions using AWS.

Exam Format:
  • Primarily multiple-choice and multiple-response questions.
  • 65 questions in total.
  • 170 minutes to complete the exam.
  • Scaled score from 100-1,000, with a passing score of 750.
  • No penalty for incorrect answers.
Question Breakdown (as typically assessed by domain, based on official exam guide):
  • Domain 1: Data Engineering (20%): Ingesting, transforming, storing data for ML.
  • Domain 2: Exploratory Data Analysis (24%): Cleaning, visualizing, feature engineering.
  • Domain 3: Modeling (36%): Algorithm selection, training, tuning, evaluation.
  • Domain 4: ML Implementation & Operations (20%): Deployment, monitoring, MLOps, security, cost.

Questions often present complex, real-world ML scenarios. They assess your ability to:

  • Problem Framing: Select and justify the appropriate ML approach for a given business problem.
  • Data Handling: Identify and preprocess various data types for ML.
  • Model Development: Select, train, and deploy appropriate ML algorithms.
  • Solution Design: Design and implement scalable, secure, and cost-optimized ML solutions.
  • Troubleshooting: Troubleshoot ML models and workflows.
  • Operational Excellence: Address operational excellence in ML workflows (MLOps).
Key Exam Aspects:
  • Total Questions: 65.
  • Time: 170 minutes.
  • Format: Multiple-choice/response.
  • Passing Score: 750/1000.
  • No Penalty for Wrong Answers.
  • Focus: Designing, implementing, evaluating, and operating ML solutions in scenarios.

Scenario: You are taking a practice exam for the MLS-C01 certification. You notice questions are lengthy, complex scenarios involving multiple ML concepts and AWS services, often requiring you to select the best data engineering pipeline, algorithm, or deployment strategy.

Reflection Question: How does understanding the MLS-C01 exam's structure (number of questions, time limit, domain weighting) and its focus on "designing, implementing, evaluating, and operating" complex ML solutions fundamentally influence your preparation strategy, especially for pacing yourself through detailed ML scenarios?