Study plan
How to Pass the AWS Certified Machine Learning Engineer – Associate Exam
A study plan for the AWS Certified Machine Learning Engineer – Associate certification exam, built from the official domain weights instead of a generic template. Combine hands‑on labs (SageMaker, Lambda, Step Functions) with official AWS training (e.g., Exam Readiness, SageMaker developer guides). Focus on MLOps pipelines, model monitoring, and cost optimization. Practice with sample questions provided by AWS and build end‑to‑end ML projects using real AWS services. Use flashcards for terminology and service limits. Review AWS whitepapers on Well‑Architected ML and security.
- 5 domains
- 65 questions
- 130 min
- 72% to pass
certilumStudy in weight order, not topic order
The plan below follows the official blueprint weights, so the order of study is the order that returns the most points.
- 1
Deployment and Orchestration
28%- Deploy models to endpoints for real‑time inference (e.g., SageMaker real‑time endpoints, Serverless Inference)
- Set up batch inference workflows (e.g., SageMaker batch transform, AWS Lambda triggers)
- Automate model deployment using CI/CD pipelines (e.g., SageMaker Pipelines, AWS CodePipeline)
- Monitor deployed model performance and detect drift (e.g., SageMaker Model Monitor, CloudWatch metrics)
18 practice questions tagged to this domain.
- 2
Model Development
26%- Select appropriate algorithm or model architecture based on business requirements
- Train and fine‑tune models using Amazon SageMaker (e.g., automatic model tuning, hyperparameter optimization)
- Evaluate model performance using appropriate metrics (e.g., accuracy, precision, recall, AUC‑ROC)
- Perform error analysis and address model bias
- +1 more topics in the blueprint
17 practice questions tagged to this domain.
- 3
Data Preparation for Machine Learning
18%- Ingest data from AWS data stores and streams (e.g., S3, Kinesis, Redshift)
- Identify and remediate data quality issues (e.g., missing values, duplicates, outliers)
- Transform and normalize data for ML model training (e.g., using SageMaker Processing jobs, feature engineering)
- Split data into training, validation, and test sets
12 practice questions tagged to this domain.
- 4
ML Operations (MLOps)
16%- Automate model retraining pipelines (e.g., SageMaker Pipelines, Step Functions)
- Manage model registry and approve models for production (e.g., SageMaker Model Registry)
- Implement logging and auditing of ML workflows (e.g., CloudTrail, CloudWatch Logs)
- Design for scalable and cost‑optimized ML infrastructure (e.g., spot instances, multi‑AZ)
10 practice questions tagged to this domain.
- 5
ML Security and Compliance
12%- Implement data encryption at rest and in transit (e.g., KMS, S3 SSE)
- Apply least‑privilege IAM policies for ML resources
- Use private network connectivity (e.g., VPC endpoints, SageMaker in VPC)
- Ensure compliance with AWS shared responsibility model for ML workloads
8 practice questions tagged to this domain.
Three phases, in order
Most failures are process failures, not knowledge failures.
Phase 1 — Learn the high-weight domains
Start where the points are. The first domains on this plan carry the most weight, so a gap there costs the most. Cover the objectives, then close each topic with practice questions you actually get wrong.
Phase 2 — Close the mid-weight domains
The middle of the blueprint is where most candidates plateau. These domains are worth less per topic than the first phase, but they add up fast, and they are the usual difference between a near miss and a clear pass.
Phase 3 — Timed full mocks
Stop reading new material at this point. Run full-length exams under the real time limit and the real format, and use the per-domain breakdown to find which domain is still costing you the pass.
Where we put the emphasis
Certilum provides structured learning paths for AWS Machine Learning Engineer – Associate. We break down MLOps, deployment strategies, and security best practices into interactive quizzes and scenario‑based questions. Our diagnostic engine identifies weak areas so you can focus your study time effectively.
The order matters less than the loop. Every domain you finish should end with questions you got wrong, because the mistake is the only thing you actually needed to study. On the AWS Certified Machine Learning Engineer – Associate that loop is worth 26% of the exam in the first block alone.
More about the AWS Certified Machine Learning Engineer – Associate exam
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Frequently asked questions
Everything you need to know about the AWS Certified Machine Learning Engineer – Associate exam.