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AWS Machine Learning Specialty Exam Guide

Everything you need to know about the AWS Machine Learning Specialty certification exam, from study strategies to exam day tips.

Exam Information

180 minutes

Duration

65 questions

Questions

750/1000

Passing Score

$300 USD

Exam Cost

Recommended Study Plan

Week 1-2

Data Engineering

Data Engineering (20%)

15-20 hours

Data collection and storage strategies
AWS Glue, Data Pipeline, and Lake Formation
Feature engineering and data transformation
Data labeling with SageMaker Ground Truth

Recommended Resources:

AWS ML Documentation

SageMaker Workshop

Week 3-4

Exploratory Data Analysis

Exploratory Data Analysis (24%)

15-20 hours

Data visualization and statistical analysis
Feature selection and engineering techniques
Handling missing data and outliers
Data preparation for ML algorithms

Recommended Resources:

Data Analysis Guides

Practice Questions

Week 5-6

Modeling

Modeling (36%)

20-25 hours

Algorithm selection and hyperparameter tuning
SageMaker built-in algorithms
Deep learning frameworks on AWS
Model training, evaluation, and optimization

Recommended Resources:

SageMaker Developer Guide

Algorithm Documentation

Week 7-8

ML Implementation & Operations

ML Implementation and Operations (20%)

15-20 hours

Model deployment and inference
SageMaker endpoints and batch transform
Model monitoring and retraining strategies
A/B testing and multi-variant endpoints

Recommended Resources:

MLOps Best Practices

Practice Exams

Exam Tips & Strategies

Know SageMaker Inside Out

SageMaker is central to this exam. Understand all features: training, tuning, endpoints, Ground Truth, and built-in algorithms.

Manage Your Time

You have about 2.8 minutes per question. Some questions have long scenarios—read them carefully.

Understand When to Use Which Algorithm

Know when to use XGBoost vs Linear Learner vs DeepAR vs BlazingText based on data type and problem.

Focus on Modeling Domain

36% of the exam is on modeling. Master hyperparameter tuning, regularization, and evaluation metrics.

Key Topics to Master

Amazon SageMaker

SageMaker Built-in Algorithms

Hyperparameter Tuning

SageMaker Ground Truth

AWS Glue & Data Pipeline

Feature Engineering

Model Deployment

SageMaker Endpoints

Batch Transform

Model Monitoring

XGBoost

DeepAR

BlazingText

Amazon Rekognition & Comprehend

For the most up-to-date exam information, visit the official exam guide.

View Official Exam Guide

Back to ML Specialty

AWS Machine Learning Specialty Exam Guide

Everything you need to know about the AWS Machine Learning Specialty certification exam, from study strategies to exam day tips.

Exam Information

180 minutes

Duration

65 questions

Questions

750/1000

Passing Score

$300 USD

Exam Cost

Recommended Study Plan

Week 1-2

Data Engineering

Data Engineering (20%)

15-20 hours

Data collection and storage strategies
AWS Glue, Data Pipeline, and Lake Formation
Feature engineering and data transformation
Data labeling with SageMaker Ground Truth

Recommended Resources:

AWS ML Documentation

SageMaker Workshop

Week 3-4

Exploratory Data Analysis

Exploratory Data Analysis (24%)

15-20 hours

Data visualization and statistical analysis
Feature selection and engineering techniques
Handling missing data and outliers
Data preparation for ML algorithms

Recommended Resources:

Data Analysis Guides

Practice Questions

Week 5-6

Modeling

Modeling (36%)

20-25 hours

Algorithm selection and hyperparameter tuning
SageMaker built-in algorithms
Deep learning frameworks on AWS
Model training, evaluation, and optimization

Recommended Resources:

SageMaker Developer Guide

Algorithm Documentation

Week 7-8

ML Implementation & Operations

ML Implementation and Operations (20%)

15-20 hours

Model deployment and inference
SageMaker endpoints and batch transform
Model monitoring and retraining strategies
A/B testing and multi-variant endpoints

Recommended Resources:

MLOps Best Practices

Practice Exams

Exam Tips & Strategies

Know SageMaker Inside Out

SageMaker is central to this exam. Understand all features: training, tuning, endpoints, Ground Truth, and built-in algorithms.

Manage Your Time

You have about 2.8 minutes per question. Some questions have long scenarios—read them carefully.

Understand When to Use Which Algorithm

Know when to use XGBoost vs Linear Learner vs DeepAR vs BlazingText based on data type and problem.

Focus on Modeling Domain

36% of the exam is on modeling. Master hyperparameter tuning, regularization, and evaluation metrics.

Key Topics to Master

Amazon SageMaker

SageMaker Built-in Algorithms

Hyperparameter Tuning

SageMaker Ground Truth

AWS Glue & Data Pipeline

Feature Engineering

Model Deployment

SageMaker Endpoints

Batch Transform

Model Monitoring

XGBoost

DeepAR

BlazingText

Amazon Rekognition & Comprehend

For the most up-to-date exam information, visit the official exam guide.

View Official Exam Guide