AWS Certified Machine Learning Exam Topics & Free Simulator

This is one of 10 Free AWS AI & Machine Learning Certification Practice Test

AWS AI & ML Exam Details

  • The AWS Machine Learning exam hits you with 65 questions
  • Every question is multiple choice or multiple response, no free-form AI essays here
  • You have 180 minutes, because apparently machine learning is complicated
  • The magic passing score is 750 out of 1000
  • Passing it proves you can design, implement, and tune models without praying to the ML gods

Exam Topics for AWS Machine Learning Cert

  • Domain One: Data Engineering Basics 20%
  • Domain Two: Exploratory Data Analysis Work 24%
  • Domain Three: AI, ML & LLM Modeling 36%
  • Domain Four: Machine Learning Implementation, Optimization and Ops 20%

AWS Machine Learning Associate Exam Objectives

Exam Overview

  • 65 questions, each carefully crafted to test your patience and knowledge
  • Three full hours to show you know the difference between supervised and unsupervised without Googling it
  • Passing score: 750, because AWS likes to keep the bar just high enough
  • Intended for practitioners who can wrangle data, build models, and avoid overfitting disasters
  • The exam makes sure you can design scalable ML solutions instead of just throwing everything into a Jupyter notebook

Domain 1: Data Engineering 20%

  • Design data ingestion pipelines with S3, Kinesis, and Glue
  • Choose storage formats that won’t drive analysts crazy
  • Build scalable solutions that actually deliver clean data

Domain 2: Exploratory Data Analysis 24%

  • Spot trends, anomalies, and “oops” moments in your datasets
  • Understand how feature engineering is basically magic plus statistics
  • Visualize data to convince others that your ML model might actually work

Domain 3: Modeling 36%

  • Select the right algorithms without flipping a coin
  • Tune hyperparameters like a wizard with grid search or SageMaker magic
  • Evaluate models using metrics beyond just “accuracy is good enough”
  • Balance performance and efficiency without burning through your AWS credits

Domain 4: ML Implementation and Operations 20%

  • Deploy ML models on SageMaker without crying over container errors
  • Automate retraining so your models don’t age like milk
  • Monitor performance drift and avoid embarrassing production failures

What is Not Included

  • You will not be asked to prove P=NP or invent a new algorithm
  • Deep theoretical math proofs are not part of the test
  • Debugging TensorFlow errors at 2 a.m. is thankfully not included
  • Questions focus on AWS ML solutions, not on writing neural nets from scratch

Benefits of Getting Certified

  • Instant credibility: people suddenly trust your ML buzzwords
  • Better job prospects in the growing field of AI and data science
  • AWS street cred, proving you know SageMaker is more than just a cool name
  • Shows you can translate ML concepts into practical cloud solutions
  • And yes, you get a shiny badge to flex on LinkedIn

Preparation Tips

  • Study the official exam guide and domain breakdown carefully
  • Take practice exams until acronyms like EDA and MLOps feel like old friends
  • Spin up AWS services like SageMaker, Glue, and Rekognition in a sandbox account
  • Review ML algorithms and metrics without overthinking the math
  • Practice explaining models to non-technical friends to prove you really understand
  • And finally, keep your cool. This exam tests skill, not your ability to memorize Wikipedia