MLS-C01 Real Exam Questions
AWS Certified Machine Learning - Specialty (MLS-C01) Exam. Everything you need to prepare, practice, and pass.
388
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5
Exam Domains
Included
Explanations
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Certification Overview
This exam focuses on the complete ML lifecycle on AWS: ingesting and transforming data at scale (Data Preprocessing, Data Ingestion, Data Transformation via AWS Glue), performing exploratory analysis, building and tuning models with SageMaker (Hyperparameter Tuning, Overfitting prevention), and operationalizing ML systems (MLOps, monitoring, cost optimization). Feature Engineering and cost-conscious architecture are critical throughout.
What This Certification Proves
This specialty certification validates expertise in designing, building, and deploying machine learning solutions on AWS at scale. It demonstrates proficiency with AWS ML services, data engineering pipelines, and operational best practices—proving you can architect end-to-end ML workflows in production environments.
Who Should Take This Exam
ML engineers and data scientists with AWS experience (or AWS-certified professionals transitioning to ML), professionals building data pipelines with AWS services, and DevOps engineers implementing ML operations. Requires hands-on experience with ML concepts and familiarity with AWS services.
Topic Breakdown
5 domains covering 388 questions
| Domain | Questions | Weight |
|---|---|---|
| Modeling | 154 | 40% |
| Machine Learning Implementation And Operations | 96 | 25% |
| Data Engineering | 66 | 17% |
| Ml Implementation And Operations | 53 | 14% |
| Exploratory Data Analysis | 19 | 5% |
Study Plans
Choose a study plan that matches your schedule and experience level
30 Days
Intensive Sprint
Week 1-2
- Master fundamentals: Modeling
- Read Amazon official documentation
- Complete 13 questions daily
Week 3
- Deep dive: Machine Learning Implementation And Operations
- 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: Modeling
- Focus: Machine Learning Implementation And Operations
- 7 questions daily
Week 5-6
- Focus: Data Engineering
- Hands-on labs if applicable
- Review explanations for wrong answers
Week 7-8
- Complete all 388 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
- 5 questions daily
Month 2
- Deep dive into each domain
- Hands-on practice and labs
- Take weekly timed exams
Month 3
- Work through all 388 questions
- Identify and eliminate weak areas
- Take 3 full-length timed exams
MLS-C01-Specific Tips
- Master Amazon SageMaker deeply—it covers training, tuning, deployment, and monitoring. Practice the full workflow from data ingestion to model hosting.
- Focus on data preprocessing and feature engineering techniques; the exam heavily weights data quality and transformation challenges using AWS Glue and SageMaker processing.
- Study hyperparameter tuning strategies and cost optimization patterns—use SageMaker Automatic Model Tuning (Hyperparameter Optimization) and spot instances extensively in labs.
- Understand MLOps workflows: experiment tracking, model versioning, CI/CD pipelines, and monitoring deployed models for drift and performance degradation.
- Practice overfitting prevention techniques and regularization methods; the exam tests your ability to diagnose and solve model quality issues.
- Get hands-on with AWS Glue for ETL, SageMaker Feature Store for feature management, and model registry for production governance.
- Review cost optimization patterns—spot instances, on-demand vs. reserved capacity, and efficient data formats for training and inference at scale.
Relevant Career Roles
Sample Questions
Try 5 free questions from the MLS-C01 question bank
A company will use Amazon SageMaker to train and host a machine learning (ML) model for a marketing campaign. The majority of data is sensitive customer data. The data must be encrypted at rest. The company wants AWS to maintain the root of trust for the master keys and wants encryption key usage to be logged. Which implementation will meet these requirements?
Which of the following metrics should a Machine Learning Specialist generally use to compare/evaluate machine learning classification models against each other?
A Machine Learning Specialist wants to determine the appropriate setting for an endpoint automatic scaling SageMakerVariantInvocationsPerInstance configuration. The Specialist has performed a load test on a single instance and determined that peak requests per second (RPS) without service degradation is about 20 RPS. As this is the first deployment, the Specialist intends to set the invocation safety factor to 0.5. Based on the stated parameters and given that the invocations per instance setting is measured on a per-minute basis, what should the Specialist set as the setting? SageMakerVariantInvocationsPerInstance
A machine learning engineer is building a bird classification model. The engineer randomly separates a dataset into a training dataset and a validation dataset. During the training phase, the model achieves very high accuracy. However, the model did not generalize well during validation of the validation dataset. The engineer realizes that the original dataset was imbalanced. What should the engineer do to improve the validation accuracy of the model?
A manufacturing company asks its machine learning specialist to develop a model that classifies defective parts into one of eight defect types. The company has provided roughly 100,000 images per defect type for training. During the initial training of the image classification model, the specialist notices that the validation accuracy is 80%, while the training accuracy is 90%. It is known that human-level performance for this type of image classification is around 90%. What should the specialist consider to fix this issue?
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