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AWS Machine Learning Engineer - Associate Exam Guide

A domain-by-domain AWS Certified Machine Learning Engineer – Associate (MLA-C01) study plan covering data preparation, model development, deployment, and ML monitoring.

Exam Information

130 minutes

Duration

65 questions (approx.)

Questions

720/1000

Passing Score

$150 USD

Exam Cost

Recommended Study Plan

Week 1

Data Preparation for ML

28% of exam

10-12 hours

Focus: Parquet + partitioning + Athena is the default answer for cost-effective S3 analytics.
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

Recommended Resources:

AWS official objectives

/certifications/aws/ml-engineer-associate/notes#data-preparation-ml

/certifications/aws/ml-engineer-associate/practice

/certifications/aws/ml-engineer-associate/study-guide

Week 2

ML Model Development

26% of exam

10-12 hours

Focus: For tabular data, start with XGBoost; for images/text, use a framework container or JumpStart.
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

Recommended Resources:

AWS official objectives

/certifications/aws/ml-engineer-associate/notes#ml-model-development

/certifications/aws/ml-engineer-associate/practice

/certifications/aws/ml-engineer-associate/study-guide

Week 3

Deployment and Orchestration

22% of exam

6-8 hours

Focus: Batch Transform is the answer when there is no need for a persistent endpoint.
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

Recommended Resources:

AWS official objectives

/certifications/aws/ml-engineer-associate/notes#deployment-orchestration-ml

/certifications/aws/ml-engineer-associate/practice

/certifications/aws/ml-engineer-associate/study-guide

Week 4

Monitoring, Maintenance, and Security

24% of exam

6-8 hours

Focus: Data drift ≠ model quality drift; quality drift requires ground-truth labels.
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

Recommended Resources:

AWS official objectives

/certifications/aws/ml-engineer-associate/notes#ml-monitoring-security

/certifications/aws/ml-engineer-associate/practice

/certifications/aws/ml-engineer-associate/study-guide

Week 5

Final Review & Practice Exams

All domains

8-10 hours

Complete all three full-length practice exams
Review incorrect answers and weak domains
Revisit flashcards for key terms
Timed review of highest-weight domains

Recommended Resources:

Practice exams

Flashcards

Study notes PDF

Exam Tips & Strategies

Know the SageMaker Workflow

Most scenarios map to a SageMaker step — Data Wrangler, Feature Store, Training, Model Registry, Endpoints, and Pipelines. Learn where each belongs.

Practise Under Real Timing

You get 130 minutes for 65 questions, and several are long scenario stems. Rehearse so you can triage and skip before the clock runs down.

Separate Training From Inference

Distinguish data-prep and training services (Glue, SageMaker Training, Feature Store) from serving and monitoring (Endpoints, Model Monitor, Clarify).

Watch the Cost and Security Constraints

Questions often hinge on cost (Spot training, Serverless inference) or security (IAM roles, VPC endpoints, KMS) — read the constraint before choosing.

Key Topics to Master

AWS Glue for ETL and data transformation

Amazon S3 data lakes and dataset management

SageMaker built-in algorithms (XGBoost, Linear Learner, etc.)

SageMaker Training Jobs and hyperparameter tuning

SageMaker Endpoints for real-time inference

SageMaker Batch Transform for batch inference

SageMaker Model Monitor for data quality and drift detection

CloudWatch metrics and alarms for ML infrastructure

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

View Official Exam Guide

Back to ML Engineer Associate

AWS Machine Learning Engineer - Associate Exam Guide

A domain-by-domain AWS Certified Machine Learning Engineer – Associate (MLA-C01) study plan covering data preparation, model development, deployment, and ML monitoring.

Exam Information

130 minutes

Duration

65 questions (approx.)

Questions

720/1000

Passing Score

$150 USD

Exam Cost

Recommended Study Plan

Week 1

Data Preparation for ML

28% of exam

10-12 hours

Focus: Parquet + partitioning + Athena is the default answer for cost-effective S3 analytics.
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

Recommended Resources:

AWS official objectives

/certifications/aws/ml-engineer-associate/notes#data-preparation-ml

/certifications/aws/ml-engineer-associate/practice

/certifications/aws/ml-engineer-associate/study-guide

Week 2

ML Model Development

26% of exam

10-12 hours

Focus: For tabular data, start with XGBoost; for images/text, use a framework container or JumpStart.
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

Recommended Resources:

AWS official objectives

/certifications/aws/ml-engineer-associate/notes#ml-model-development

/certifications/aws/ml-engineer-associate/practice

/certifications/aws/ml-engineer-associate/study-guide

Week 3

Deployment and Orchestration

22% of exam

6-8 hours

Focus: Batch Transform is the answer when there is no need for a persistent endpoint.
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

Recommended Resources:

AWS official objectives

/certifications/aws/ml-engineer-associate/notes#deployment-orchestration-ml

/certifications/aws/ml-engineer-associate/practice

/certifications/aws/ml-engineer-associate/study-guide

Week 4

Monitoring, Maintenance, and Security

24% of exam

6-8 hours

Focus: Data drift ≠ model quality drift; quality drift requires ground-truth labels.
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

Recommended Resources:

AWS official objectives

/certifications/aws/ml-engineer-associate/notes#ml-monitoring-security

/certifications/aws/ml-engineer-associate/practice

/certifications/aws/ml-engineer-associate/study-guide

Week 5

Final Review & Practice Exams

All domains

8-10 hours

Complete all three full-length practice exams
Review incorrect answers and weak domains
Revisit flashcards for key terms
Timed review of highest-weight domains

Recommended Resources:

Practice exams

Flashcards

Study notes PDF

Exam Tips & Strategies

Know the SageMaker Workflow

Most scenarios map to a SageMaker step — Data Wrangler, Feature Store, Training, Model Registry, Endpoints, and Pipelines. Learn where each belongs.

Practise Under Real Timing

You get 130 minutes for 65 questions, and several are long scenario stems. Rehearse so you can triage and skip before the clock runs down.

Separate Training From Inference

Distinguish data-prep and training services (Glue, SageMaker Training, Feature Store) from serving and monitoring (Endpoints, Model Monitor, Clarify).

Watch the Cost and Security Constraints

Questions often hinge on cost (Spot training, Serverless inference) or security (IAM roles, VPC endpoints, KMS) — read the constraint before choosing.

Key Topics to Master

AWS Glue for ETL and data transformation

Amazon S3 data lakes and dataset management

SageMaker built-in algorithms (XGBoost, Linear Learner, etc.)

SageMaker Training Jobs and hyperparameter tuning

SageMaker Endpoints for real-time inference

SageMaker Batch Transform for batch inference

SageMaker Model Monitor for data quality and drift detection

CloudWatch metrics and alarms for ML infrastructure

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

View Official Exam Guide