Tutorials Dojo Study Guide and Cheat Sheets - AWS Certified Machine Learning Engineer Associate MLA-C01 - Study Guide

Sep 20, 2026

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Comprehensive study guide for the AWS Certified Machine Learning Engineer Associate (MLA-C01) exam. Covers exam logistics, domains, scoring, and benefits, then provides structured preparation for machine learning data workflows: ingesting and storing data, transforming it with cleaning and feature engineering, ensuring integrity with bias and security controls, and optimizing datasets for model training. Includes sample practice question sections and references mapped to each preparation domain using AWS services and tools such as SageMaker, Glue, EMR, and related data quality and security capabilities.

LanguageEnglish

CategoryExam

FormatPDF

Pages39

Reading Time1 h 38 min

AccessFree to download

Introduction

AWS Certified Machine Learning Engineer Associate (MLA-C01) Overview

Exam Details

Exam Domains

Exam-Related AWS Topics and Services

Exam Scoring System

Exam Benefits

AWS Certified Machine Learning Associate MLA-C01 Exam Study Guide

What to review

How to review

Common Exam Scenarios

Validate Your Knowledge

Sample Practice Test Questions: What to expect from the exam

Data Preparation for Machine Learning (ML)

Ingest and Store Data

Transform Data and Perform Feature Engineering

Ensure Data Integrity and Prepare Data for Modeling

Optimizing Data for Machine Learning Models

ML Model Development

S A M A N T H A S E RV O , KA Y N E R O D R I G O , & J O N B O N S O AW S C E R T I F I E D MACHINE LEARNING ASSOCIATE MLA-C01 EXAM Tutorials Dojo Study Guide Tutorials Dojo Study Guide and Cheat Sheets-AWS Certiﬁed Machine Learning Engineer Associate by Jon Bonso, Kayne Rodrigo, Samantha Servo TABLE OF CONTENTS INTRODUCTION 11 AWS CERTIFIED MACHINE LEARNING ENGINEER ASSOCIATE (MLA-C01) OVERVIEW 12 Exam Details 13 Exam Domains 14 Exam-Related AWS Topics and Services 15 Exam Scoring System 18 Exam Beneﬁts 19 AWS CERTIFIED MACHINE LEARNING ASSOCIATE MLA-C01 EXAM STUDY GUIDE 20 What to review 20 How to review 23 Common Exam Scenarios 24 Validate Your Knowledge 29 Sample Practice Test Questions: 30 What to expect from the exam 34 DATA PREPARATION FOR MACHINE LEARNING (ML) 36 A. Ingest and Store Data 37 Chapter 1.1 Data Formats and Ingestion Mechanisms 37 Chapter 1.2 AWS Data Sources for Data Storage 42 Chapter 1.3 AWS Services Involved in Data Ingestion 45 Key Features for ML Workﬂows 46 Key Features for ML Workﬂows 46 Key Features for ML Workﬂows 47 Chapter 1.4 AWS Storage Options: Use Cases and Tradeoffs 48 Chapter 1.5 Data Ingestion Using AWS Tools 49 Extracting data 49 AWS services for acceleration and IOPS optimization 50 Ingesting data 51 Chapter 1.6 Merging and Processing Data from Multiple Sources 52 Techniques for Data Merging 52 AWS Glue: ETL ( Extract, Transform, Load) Operations 52 Using Apache Spark for Large-Scale Data Processing 53 Chapter 1.7 Troubleshooting Data Ingestion and Storage Issues 54 [https://portal.tutorialsdojo.com/](https://portal.tutorialsdojo.com/ 1)[ 1](https://portal.tutorialsdojo.com/ 1) Tutorials Dojo Study Guide and Cheat Sheets-AWS Certiﬁed Machine Learning Engineer Associate by Jon Bonso, Kayne Rodrigo, Samantha Servo Diagnosing Capacity and Scalability Problems 54 Tools and Techniques for Debugging Storage and Ingestion Workﬂows 55 B. Transform Data and Perform Feature Engineering 56 Chapter 1.8 Data Cleaning and Transformation Techniques 56 Outlier Detection and Removal 56 Imputation of Missing Data 57 Combining Datasets and Deduplication Methods 58 Chapter 1.9 Feature Engineering Techniques 58 Data scaling and standardization methods 58 Feature splitting, binning, and log transformation 60 Chapter 1.10 Encoding Techniques for ML Data 60 One-hot encoding, binary encoding, and label encoding 60 Tokenization for text data 62 Chapter 1.11 Tools for Data Exploration, Transformation, and Feature Engineering 63 Using SageMaker Data Wrangler for Data Exploration and Transformation 63 AWS Glue and AWS Glue DataBrew for ETL ( Extract, Transform, Load) 64 Chapter 1.12 Transforming Streaming Data 64 Real-time Data Processing Using AWS Lambda 64 Spark-based Transformations in Amazon EMR 64 Chapter 1.13 Data Annotation and Labeling for High-Quality Datasets 65 Using SageMaker Ground Truth for Labeled Data Generation 65 Amazon Mechanical Turk for Crowd-Sourced Data Annotation 65 C. Ensure Data Integrity and Prepare Data for Modeling 66 Chapter 1.14 Understanding Pre-Training Bias Metrics 66 Class Imbalance (CI) and Difference in Proportions of Labels ( DPL) 66 Bias Detection in Numeric, Text, and Image Data 66 Using SageMaker Clarify for bias metrics analysis 67 Chapter 1.15 Data Encryption and Security 67 Techniques for Securing Data during Ingestion, Storage, and Processing 67 AWS Encryption Services and Best Practices 67 Chapter 1.16 Data Classiﬁcation, Anonymization, and Masking 68 Types of information 68 Approaches for Handling Sensitive Data (e.g., PII, PHI) 68 Best Practices for Data Masking in AWS 69 [https://portal.tutorialsdojo.com/](https://portal.tutorialsdojo.com/ 2)[ 2](https://portal.tutorialsdojo.com/ 2) Tutorials Dojo Study Guide and Cheat Sheets-AWS Certiﬁed Machine Learning Engineer Associate by Jon Bonso, Kayne Rodrigo, Samantha Se

FAQs

What does the guide cover for the MLA-C01 exam besides study topics?

It includes an overview of exam details such as domains, related AWS topics and services, scoring system, benefits, and common exam scenarios, along with sections for validating knowledge and sample practice question expectations.

How does the guide structure data preparation for machine learning?

It organizes preparation into ingesting and storing data, transforming data and performing feature engineering, ensuring data integrity and preparing data for modeling, and optimizing data for model training performance.

Which AWS tools and services are highlighted for data work and governance?

The guide references tools and services such as SageMaker, AWS Glue and Glue DataBrew, Amazon EMR, and services for exploration, quality checks, encryption/security, annotation, and bias metrics analysis.

Sep 20, 2026

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