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AWS Machine Learning Engineer - Associate Study Guide

Domain-by-domain study guide for AWS Certified Machine Learning Engineer - Associate (MLA-C01). Covers all 4 exam objectives with topic checklists, quick notes, and practice links.

Download PDF Notes

Exam Overview

Key facts before you start studying

Duration

130 min

Questions

Exam Code

MLA-C01

Domains

About this exam

Citation-ready facts for AWS Machine Learning Engineer - Associate (MLA-C01)

AWS Machine Learning Engineer - Associate (MLA-C01) is a AWS certification exam with about 65 questions over 130 minutes, typically requiring a scaled score of 720 to pass. Official domain weights on CertStud: Data Preparation for Machine Learning (ML) (28%); ML Model Development (26%); Deployment and Orchestration of ML Workflows (22%); ML Solution Monitoring, Maintenance, and Security (24%).

Use this study guide with free practice questions, the week-by-week study plan, and detailed notes. Confirm current objectives and scoring on the official AWS exam page before booking.

Quick Navigation

Jump to a specific exam domain

1. Data Preparation for Machine Learning (ML) (28%)
2. ML Model Development (26%)
3. Deployment and Orchestration of ML Workflows (22%)
4. ML Solution Monitoring, Maintenance, and Security (24%)

Domain 1

28%

Data Preparation for Machine Learning (ML)

Ingest, transform, and prepare data for machine learning workflows

Topics to Master

AWS Glue for ETL and data transformation
Amazon S3 data lakes and dataset management
SageMaker Data Wrangler for data preparation
Feature engineering and SageMaker Feature Store
Data quality validation and schema enforcement
Amazon Athena for data exploration and analysis
AWS Lake Formation for data governance
DynamoDB and RDS as data sources for ML
Data formatting (CSV, Parquet, TFRecord, RecordIO)
Handling missing data, outliers, and data imbalance

Domain 2

26%

ML Model Development

Select algorithms, train models, and optimize performance

Topics to Master

SageMaker built-in algorithms (XGBoost, Linear Learner, etc.)
SageMaker Training Jobs and hyperparameter tuning
Distributed training strategies (data parallelism, model parallelism)
Custom training scripts (TensorFlow, PyTorch, scikit-learn)
Model evaluation metrics (accuracy, precision, recall, F1, AUC)
Cross-validation and train-test-validation splits
Handling overfitting and underfitting
Transfer learning and fine-tuning pre-trained models
SageMaker Autopilot for AutoML
SageMaker JumpStart for foundation models

Domain 3

22%

Deployment and Orchestration of ML Workflows

Deploy models and orchestrate end-to-end ML workflows

Topics to Master

SageMaker Endpoints for real-time inference
SageMaker Batch Transform for batch inference
SageMaker Serverless Inference for variable traffic
SageMaker Multi-Model Endpoints (MME)
SageMaker Pipelines for ML workflow orchestration
MLOps best practices and CI/CD for ML
A/B testing and canary deployments
Model versioning and SageMaker Model Registry
Lambda and Step Functions for ML orchestration
Amazon ECR for custom container images

Domain 4

24%

ML Solution Monitoring, Maintenance, and Security

Monitor ML systems, detect drift, and secure ML workflows

Topics to Master

SageMaker Model Monitor for data quality and drift detection
CloudWatch metrics and alarms for ML infrastructure
SageMaker Clarify for bias detection and explainability
Model retraining strategies and triggers
IAM roles and policies for SageMaker access control
VPC configuration for secure ML workloads
KMS encryption for data and models at rest
SageMaker Studio security and governance
Cost optimization for SageMaker training and inference
Compliance and auditing with CloudTrail

Continue Your Prep

Free CertStud resources for MLA-C01

Study Notes Exam Study Plan Start Practice Test

Back to AWS Machine Learning Engineer - Associate

AWS Machine Learning Engineer - Associate Study Guide

Domain-by-domain study guide for AWS Certified Machine Learning Engineer - Associate (MLA-C01). Covers all 4 exam objectives with topic checklists, quick notes, and practice links.

Download PDF Notes

Exam Overview

Key facts before you start studying

Duration

130 min

Questions

Exam Code

MLA-C01

Domains

About this exam

Citation-ready facts for AWS Machine Learning Engineer - Associate (MLA-C01)

Use this study guide with free practice questions, the week-by-week study plan, and detailed notes. Confirm current objectives and scoring on the official AWS exam page before booking.

Quick Navigation

Jump to a specific exam domain

1. Data Preparation for Machine Learning (ML) (28%)
2. ML Model Development (26%)
3. Deployment and Orchestration of ML Workflows (22%)
4. ML Solution Monitoring, Maintenance, and Security (24%)

Domain 1

28%

Data Preparation for Machine Learning (ML)

Ingest, transform, and prepare data for machine learning workflows

Topics to Master

AWS Glue for ETL and data transformation
Amazon S3 data lakes and dataset management
SageMaker Data Wrangler for data preparation
Feature engineering and SageMaker Feature Store
Data quality validation and schema enforcement
Amazon Athena for data exploration and analysis
AWS Lake Formation for data governance
DynamoDB and RDS as data sources for ML
Data formatting (CSV, Parquet, TFRecord, RecordIO)
Handling missing data, outliers, and data imbalance

Domain 2

26%

ML Model Development

Select algorithms, train models, and optimize performance

Topics to Master

SageMaker built-in algorithms (XGBoost, Linear Learner, etc.)
SageMaker Training Jobs and hyperparameter tuning
Distributed training strategies (data parallelism, model parallelism)
Custom training scripts (TensorFlow, PyTorch, scikit-learn)
Model evaluation metrics (accuracy, precision, recall, F1, AUC)
Cross-validation and train-test-validation splits
Handling overfitting and underfitting
Transfer learning and fine-tuning pre-trained models
SageMaker Autopilot for AutoML
SageMaker JumpStart for foundation models

Domain 3

22%

Deployment and Orchestration of ML Workflows

Deploy models and orchestrate end-to-end ML workflows

Topics to Master

SageMaker Endpoints for real-time inference
SageMaker Batch Transform for batch inference
SageMaker Serverless Inference for variable traffic
SageMaker Multi-Model Endpoints (MME)
SageMaker Pipelines for ML workflow orchestration
MLOps best practices and CI/CD for ML
A/B testing and canary deployments
Model versioning and SageMaker Model Registry
Lambda and Step Functions for ML orchestration
Amazon ECR for custom container images

Domain 4

24%

ML Solution Monitoring, Maintenance, and Security

Monitor ML systems, detect drift, and secure ML workflows

Topics to Master

SageMaker Model Monitor for data quality and drift detection
CloudWatch metrics and alarms for ML infrastructure
SageMaker Clarify for bias detection and explainability
Model retraining strategies and triggers
IAM roles and policies for SageMaker access control
VPC configuration for secure ML workloads
KMS encryption for data and models at rest
SageMaker Studio security and governance
Cost optimization for SageMaker training and inference
Compliance and auditing with CloudTrail

Continue Your Prep

Free CertStud resources for MLA-C01

Study Notes Exam Study Plan Start Practice Test