About This Study Plan
This 30-day study plan breaks the MLA-C01 (AWS Machine Learning Engineer Associate) exam preparation into 5 focused study sessions with a total of 21 actionable tasks. The plan covers all 4 exam domains — Data Preparation for Machine Learning, ML Model Development, Deployment and Orchestration of ML Workflows, ML Solution Monitoring, Maintenance, and Security — ensuring complete coverage of the exam blueprint. A steady month-long plan covering the current four-domain MLA-C01 outline.
Prerequisites
- Basic Python and ML concepts
- Beginner AWS knowledge
- 1-2 hours of study time per day
Study Schedule
- Days 1-2: S3, data formats, partitioning, lifecycle, encryption, and dataset organization
- Days 3-4: Glue, Athena, DataBrew, processing jobs, and transformation workflows
- Day 5: Feature engineering, Feature Store, leakage prevention, and data quality
- Days 6-7: Domain 1 practice quiz, cheat sheet review, and flashcards
- Days 8-9: Algorithm selection and use cases
- Days 10-11: SageMaker training jobs, built-in algorithms, containers, and distributed training
- Day 12: Hyperparameter tuning and cost-aware training
- Days 13-14: Evaluation metrics, model registry, and Domain 2 practice
- Days 15-16: Inference options and endpoint deployment patterns
- Day 17: Endpoint autoscaling, variants, rollback, and production release controls
- Days 18-19: SageMaker Pipelines, workflow steps, CI/CD, and model approval
- Days 20-21: Domain 3 practice and deployment flashcards
- Days 22-23: Model Monitor, baselines, drift types, and CloudWatch observability
- Days 24-25: IAM, KMS, VPC endpoints, private training, private inference, and audit controls
- Day 26: Full mock exam #1
- Day 27: Review mock exam #1 and rebuild weak notes
- Day 28: Full mock exam #2
- Review all four official MLA-C01 domains and weights
- Rerun weak-domain flashcards
- Review inference choices, monitoring types, and metric selection
- Rest and keep notes concise
Study Tips
Build comparison tables for inference options, model metrics, and monitoring types.
Write down why wrong answers are wrong; MLA-C01 distractors are often plausible service choices.
Spend extra review time on SageMaker Pipelines, Model Registry, Model Monitor, and Feature Store.
Aim for consistent 80%+ on mixed practice before scheduling.
Ready to Practice?
Put your study plan into action with AWS Machine Learning Engineer Associate practice questions.