Lilly Tech Systems
AWS Machine Learning Specialty (MLS-C01)
Everything you need to pass the AWS Machine Learning Specialty certification exam. Comprehensive coverage of all 4 domains, 50+ practice questions with detailed explanations, proven study plans, and exam-day strategies — all free.
Your Study Path
Follow these lessons in order for complete exam preparation, or jump to any domain you need to review.
1. Exam Overview & Strategy
Exam format (65 questions, 180 min, 750/1000 pass score), domain weights, 4-week and 8-week study plans, registration process, and cost ($300).
2. Domain 1: Data Engineering (20%)
S3 data lakes, Kinesis streaming, AWS Glue ETL, Athena queries, EMR clusters for ML data pipelines. Includes practice questions with explanations.
3. Domain 2: Exploratory Data Analysis (24%)
Feature engineering, data visualization, descriptive statistics, handling missing data, and data transformation techniques. Practice questions included.
4. Domain 3: Modeling (36%)
SageMaker built-in algorithms, hyperparameter tuning, regularization, evaluation metrics, and model selection. The highest-weighted domain with practice questions.
5. Domain 4: ML Implementation & Operations (20%)
SageMaker deployment, A/B testing, model monitoring, security best practices, Auto Scaling, and CI/CD for ML. Practice questions included.
6. Practice Exam 1
25 exam-style questions covering all 4 domains with detailed explanations for each answer choice. Simulate real exam conditions.
7. Practice Exam 2
25 additional exam-style questions with comprehensive explanations. Different scenarios and service combinations to test your readiness.
8. Exam Day Tips & Resources
Last-minute review checklist, exam day strategy, time management tips, frequently asked questions, and additional study resources.
What You'll Learn
By the end of this course, you will be ready to:
Pass the MLS-C01 Exam
Achieve the 750/1000 score needed to earn your AWS Machine Learning Specialty certification on your first attempt.
Master AWS ML Services
Understand SageMaker, Comprehend, Rekognition, Polly, Lex, Translate, Forecast, Personalize, and how they fit together in real architectures.
Build ML Pipelines
Design end-to-end machine learning pipelines using S3, Glue, Kinesis, SageMaker, and other AWS services for production workloads.
Apply Exam Strategies
Use proven time management, question elimination, and domain-specific strategies to maximize your score on exam day.