MLA-C01 Real Exam Questions
AWS Certified Machine Learning Engineer - Associate MLA-C01 Exam. Everything you need to prepare, practice, and pass.
222
Questions
4
Exam Domains
Included
Explanations
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Certification Overview
The exam tests proficiency across the full SageMaker ML pipeline: preparing and engineering features with Data Wrangler and AWS Glue, training models with SageMaker's managed training and built-in algorithms, evaluating model quality and bias with Clarify, deploying models to production endpoints, and monitoring model performance and drift with Model Monitor. Security, cost optimization, and model governance are woven throughout.
What This Certification Proves
This certification validates the ability to design, build, and operationalize machine learning solutions on AWS using SageMaker and related services. It covers the complete ML lifecycle from data preparation through production deployment and monitoring, proving practical expertise in cloud-native ML engineering at an associate level.
Who Should Take This Exam
ML engineers and data scientists with 1-2 years of hands-on experience building models, transitioning to AWS; cloud engineers expanding into ML; anyone responsible for production ML workloads on AWS. Does not require prior AWS certification but benefits from familiarity with AWS core services.
Topic Breakdown
4 domains covering 222 questions
| Domain | Questions | Weight |
|---|---|---|
| Data Preparation For Machine Learning | 72 | 32% |
| Deployment And Orchestration Of Ml Workflows | 61 | 27% |
| Ml Model Development | 52 | 23% |
| Ml Solution Monitoring, Maintenance, And Security | 37 | 17% |
Study Plans
Choose a study plan that matches your schedule and experience level
30 Days
Intensive Sprint
Week 1-2
- Master fundamentals: Data Preparation For Machine Learning
- Read Amazon official documentation
- Complete 8 questions daily
Week 3
- Deep dive: Deployment And Orchestration Of Ml Workflows
- Review weak areas from results
- Take 2 full-length exams
Week 4
- Review all flagged questions
- Timed exams to build stamina
- Final revision of key concepts
60 Days
Balanced Approach
Week 1-2
- Survey all exam domains
- Set up study environment
- Begin with foundational topics
Week 3-4
- Focus: Data Preparation For Machine Learning
- Focus: Deployment And Orchestration Of Ml Workflows
- 4 questions daily
Week 5-6
- Focus: Ml Model Development
- Hands-on labs if applicable
- Review explanations for wrong answers
Week 7-8
- Complete all 222 questions
- Identify and eliminate weak areas
- Take 3 full-length timed tests
90 Days
Comprehensive Study
Month 1
- Learn all exam domains at a comfortable pace
- Build strong foundational knowledge
- 3 questions daily
Month 2
- Deep dive into each domain
- Hands-on practice and labs
- Take weekly timed exams
Month 3
- Work through all 222 questions
- Identify and eliminate weak areas
- Take 3 full-length timed exams
MLA-C01-Specific Tips
- Master SageMaker's end-to-end workflow: focus on Data Wrangler for feature engineering, built-in algorithms for training, and multi-model endpoints for deployment—these dominate the exam
- Practice data preprocessing at scale: understand how AWS Glue, SageMaker Processing, and Data Wrangler handle class imbalance, missing values, and feature engineering in real datasets
- Deep dive on SageMaker Model Monitor and Clarify: these cover both model performance tracking and bias detection—critical for the monitoring and security domains
- Hands-on with cost optimization: study SageMaker Autopilot for automated model selection, spot instances for training, and instance right-sizing—directly tested in the cost domain
- Learn deployment patterns: focus on SageMaker Endpoints (single/multi-variant), Batch Transform for offline inference, and A/B testing strategies covered in the operationalization domain
- Security and compliance: understand model explainability (SHAP), data encryption, IAM policies for ML workflows, and regulatory requirements specific to model deployment
- Take practice exams repeatedly: with 222 available questions at moderate difficulty (2.9/5), pattern recognition and practical troubleshooting matter more than memorization
Relevant Career Roles
Sample Questions
Try 5 free questions from the MLA-C01 question bank
An ML engineer needs to process thousands of existing CSV objects and new CSV objects that are uploaded. The CSV objects are stored in a central Amazon S3 bucket and have the same number of columns. One of the columns is a transaction date. The ML engineer must query the data based on the transaction date. Which solution will meet these requirements with the LEAST operational overhead?
A healthcare company uses an Amazon SageMaker AI endpoint to host a model that predicts patient readmission risk to hospitals. The company wants to predict patient readmissions with high accuracy and is willing to tolerate false positives. The current model performance has degraded over the previous year. The company trains and deploys a new model as a shadow variant for testing on live traffic from hospitals. The company monitors the performance of the new model for a month. During the month of testing, the shadow variant has a higher recall than the existing model but has a lower precision. What should the company do next?
An ML engineer needs to run intensive model training jobs each month that can take 48 to 72 hours to run. The training jobs can be interrupted and resumed without major issues. The ML engineer has a fixed budget and needs to optimize computing resources. Which solution will meet these requirements MOST cost-effectively?
A company needs to ingest data from data sources into Amazon SageMaker Data Wrangler. The data sources are Amazon S3, Amazon Redshift, and Snowflake. The ingested data must always be up to date with the latest changes in the source systems. Which solution will meet these requirements?
A company uses an Amazon QuickSight dashboard to track the sale prices of sneakers over time. The dashboard aggregates sale prices scraped from many retail websites. The company wants to determine which prices are unusually high outliers and to display the outliers visually. Which solution will meet these requirements?
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