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Free MLA-C01 study notes covering every exam domain — key concepts, mnemonics, and practice links.
Practice what you just read
10 free practice questions. No credit card.
MLA-C01 data questions usually describe a dataset problem and ask which AWS service or format solves it. Focus on where the data lives, how it is transformed, and how features stay consistent between training and inference.
| Task | Primary AWS Service | Why |
|---|---|---|
| Batch ETL and cataloging | AWS Glue (Crawler + ETL jobs) | Serverless Spark; populates the Glue Data Catalog |
| SQL exploration over S3 | Amazon Athena | Pay-per-query; works best with partitioned Parquet |
| Visual data prep | SageMaker Data Wrangler | Import, clean, and export a processing flow |
| Feature reuse and online serving | SageMaker Feature Store | One definition for training and real-time inference |
| Fine-grained lake permissions | AWS Lake Formation | Row/column-level access control over S3 data |
| Streaming ingestion | Amazon Kinesis | Near-real-time data into the processing pipeline |
Practice what you just read
10 free practice questions. No credit card.
Practice what you just read
10 free practice questions. No credit card.
Download the free MLA-C01 study notes PDF
Every domain in one printable file. No email address required.
Download the PDFAWS Machine Learning Engineer - Associate is a AWS certification. These facts come from the current exam guide and are the numbers to plan around.
| Domain | Weight | What to focus on |
|---|---|---|
| Data Preparation for Machine Learning (ML) | 28% | AWS Glue for ETL and data transformation, Amazon S3 data lakes and dataset management, SageMaker Data Wrangler for data preparation |
| ML Model Development | 26% | SageMaker built-in algorithms (XGBoost, Linear Learner, etc.), SageMaker Training Jobs and hyperparameter tuning, Distributed training strategies (data parallelism, model parallelism) |
| ML Solution Monitoring, Maintenance, and Security | 24% | SageMaker Model Monitor for data quality and drift detection, CloudWatch metrics and alarms for ML infrastructure, SageMaker Clarify for bias detection and explainability |
| Deployment and Orchestration of ML Workflows | 22% | SageMaker Endpoints for real-time inference, SageMaker Batch Transform for batch inference, SageMaker Serverless Inference for variable traffic |
Yes. Every MLA-C01 study note on this page is free to read, and the PDF download requires no email address. Practice questions start free as well.
They cover all 4 domains of the MLA-C01 exam: Data Preparation for Machine Learning (ML) (28%), ML Model Development (26%), ML Solution Monitoring, Maintenance, and Security (24%), Deployment and Orchestration of ML Workflows (22%). Each section is weighted in the same proportion as the real exam, so nothing high-value is buried.
The MLA-C01 exam has approximately 65 questions in 130 minutes, with a passing score of 720. Use the practice questions to confirm you are consistently above that line before booking the real exam.
Read a domain, then immediately answer practice questions on it — recall is what moves knowledge into long-term memory. Start with Data Preparation for Machine Learning (ML), and finish with a mixed set so you practise switching between topics.
They cover the content, but reading alone is not enough. Answer practice questions for every domain and take a timed practice exam; the notes are most useful when you return to them after a wrong answer.