Pass the Aws Certified Machine Learning Engineer Associate With Structured Practice
Access free AWS certification courses, course quizzes, and realistic mock exams. Master key concepts, track your progress, and prepare with confidence.
Free exam course
14hrs of study material to get you prepared for the exam
intermediate level
No experience required
Free practice tests
over 1000 exam style questions available
Aws Certified Machine Learning Engineer Associate
What you'll learn
✓Data Preparation for Machine Learning (ML)
Learn how high-quality datasets are prepared, transformed, and validated before model training begins. Strong data preparation skills are essential for reliable machine learning outcomes.
✓ML Model Development
Understand how models are selected, trained, and improved to solve real prediction problems. This domain develops the practical thinking needed for effective ML design.
✓Deployment and Orchestration of ML Workflows
Discover how machine learning solutions move from development into production using scalable AWS workflows. This is where ML becomes operational and business-ready.
✓ML Solution Monitoring, Maintenance, and Security
Learn how production ML systems are monitored, secured, and kept reliable over time. This domain teaches the long-term thinking required for real-world ML ownership.
Skills you'll gain
Machine Learning
Model Development
Data Preparation
Feature Engineering
Model Deployment
ML Operations
Course outline
Learn the skills required to pass your AWS certification exam with a structured, exam-focused course. Each course breaks down complex topics into easy-to-understand lessons designed specifically for certification success.
Explore a free preview today and see how our courses help you build knowledge, reinforce concepts, and prepare for exam day.
1. Data Preparation for Machine Learning (ML)
1.1.2 - Choosing appropriate data formats based on data access patterns
1.1.3 - Ingesting data into Amazon SageMaker Data Wrangler and SageMaker Feature Store
1.1.4 - Merging data from multiple sources
1.1.5 - Troubleshooting and debugging data ingestion and storage issues that involve capacity and scalability
1.1.6 - Making initial storage decisions based on cost, performance, and data structure
1.2.1 - Transforming data by using Aws tools
1.2.2 - Creating and managing features by using Aws tools
1.2.3 - Validating and labeling data by using Aws services
1.3.1 - Validating data quality using Aws Glue DataBrew and Aws Glue Data Quality
1.3.2 - Identifying and mitigating sources of bias in data by using Aws tools
1.3.3 - Preparing data to reduce prediction bias
1.3.4 - Configuring data to load into the model training resource
2. ML Model Development
2.1.1 - Assessing available data and problem complexity to determine the feasibility of an ML solution
2.1.2 - Comparing and selecting appropriate ML models or algorithms to solve specific problems
2.1.3 - Choosing built-in algorithms, foundation models, and solution templates
2.1.4 - Selecting models or algorithms based on costs
2.1.5 - Selecting AI services to solve common business needs
2.2.1 - Using SageMaker built-in algorithms and common ML libraries to develop ML models
2.2.2 - Using SageMaker script mode with SageMaker supported frameworks to train models
2.2.3 - Using custom datasets to fine-tune pre-trained models
2.2.4 - Performing hyperparameter tuning
2.2.5 - Integrating automated hyperparameter optimization capabilities
2.2.6 - Preventing model overfitting, underfitting, and catastrophic forgetting
2.2.7 - Combining multiple training models to improve performance
2.2.8 - Reducing model size
2.2.9 - Managing model versions for repeatability and audits
2.3.1 - Selecting and interpreting evaluation metrics and detecting model bias
2.3.2 - Assessing tradeoffs between model performance, training time, and cost
2.3.3 - Performing reproducible experiments by using Aws services
2.3.4 - Comparing the performance of a shadow variant to the performance of a production variant
2.3.5 - Using SageMaker Clarify to interpret model outputs
2.3.6 - Using SageMaker Model Debugger to debug model convergence
This free course includes:
224 Chapters
14 hours to complete
300 Quiz questions
Daily practice tests
Practice Questions
Reinforce key AWS concepts with exam-style quiz questions.
Identify knowledge gaps before they impact your exam performance.
Build confidence through consistent practice and instant feedback.
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Each PDF offers a full mock exam supported by detailed answers and explanations
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