AWS Machine Learning Specialist Practice Exams

1st of 10 AWS ML Specialty Exams | Over 500 ML Specialist Exam Questions

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AWS ML Specialist Exam Facts

  • 50 scored questions plus 15 unscored questions
  • Question types are multiple choice and multiple response
  • Exam time is about 180 minutes
  • Passing score is 750 out of 1000

AWS Machine Learning Specialty Exam Objectives

  • Domain 1: Data Engineering – 20%
  • Domain 2: Exploratory Data Analysis – 24%
  • Domain 3: Modeling – 36%
  • Domain 4: ML Implementation and Operations – 20%

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AWS Certified Machine Learning Specialty Exam Topics

Exam Basics

  • Format: 50 scored questions and 15 unscored questions
  • Question types: multiple choice and multiple response
  • Exam time: about 180 minutes
  • Passing score: 750 out of 1000
  • Target candidate: 2+ years of ML or deep learning on AWS
  • Scaled scoring ensures fairness across exam versions

Domain 1: Data Engineering (20%)

  • Create data repositories for ML with S3, EFS, and EBS
  • Implement data ingestion solutions using Glue, Kinesis, EMR, and Flink
  • Schedule and orchestrate batch and streaming pipelines
  • Transform and preprocess data with ETL and MapReduce tools

Domain 2: Exploratory Data Analysis (24%)

  • Sanitize and prepare datasets: handle missing and corrupt data
  • Perform feature engineering from text, speech, image, and public datasets
  • Apply binning, tokenization, one-hot encoding, and dimensionality reduction
  • Visualize and interpret data with scatter plots, histograms, and clustering

Domain 3: Modeling (36%)

  • Frame business problems as ML problems
  • Select models such as regression, XGBoost, decision trees, CNNs, RNNs, and LLMs
  • Train ML models with cross-validation, gradient descent, GPUs, and distributed training
  • Perform hyperparameter optimization with dropout, L1/L2, and learning rate tuning
  • Evaluate models using accuracy, precision, recall, F1, RMSE, ROC/AUC, and confusion matrices
  • Conduct A/B testing and cross-validation to compare models

Domain 4: Implementation & Operations (20%)

  • Deploy scalable, resilient, and fault-tolerant ML solutions
  • Use CloudTrail, CloudWatch, Auto Scaling, and multi-Region deployments
  • Choose ML services such as SageMaker, Polly, Lex, Transcribe, and Q
  • Apply AWS security practices with IAM, S3 policies, VPCs, and encryption
  • Expose endpoints, retrain pipelines, and monitor model performance

Out of Scope

  • Extensive algorithm development and complex mathematical proofs
  • Advanced hyperparameter optimization
  • Deep networking, security, or DevOps design
  • DevOps tasks for EMR and unrelated AWS services
  • Architecture and troubleshooting outside ML scope

How to Prepare

  • Study the official exam guide and review domain task statements
  • Practice data engineering, feature engineering, and modeling in AWS
  • Use SageMaker and AWS ML services in hands-on projects
  • Take practice exams to get familiar with multiple-choice and multiple-response formats
  • Review unscored practice questions as learning opportunities