MLS-C01 test index
For most people effective learning is active learning. Frequent, targeted, testing can help to keep interest, measure achievements and identify weaknesses requiring further study. There are over 25 tests are embedded in the Study Guides. This test index has links to take you to each test.
Domain 1 Data Engineering
- 1. Data Engineering
- 1.1 Create data repositories for machine learning.
- 1.2 Identify and implement a data-ingestion solution.
- 1.3 Identify and implement a data-transformation solution.
Domain 2 Exploratory Data Analysis
- 2 Exploratory data analysis
- 2.1 Sanitize and prepare data for modeling
- 2.2 Perform feature engineering
- 2.3 Analyze and visualize data for machine learning
Domain 3 Modeling
- 3 Modeling
- 3.1 Frame the business problem
- 3.2 Select the appropriate models
- How to select a model for a given machine learning problem
- 35 Q & A for SageMaker built-in algorithms
- 3.3 Train the models
- 3.4 Tune the models
- 3.5 Evaluate the models
Domain 4 Machine Learning Implementation and Operations
- 4 Machine Learning Implementation and Operations
- 4.1 Build machine learning solutions for performance, availability, scalability, resiliency, and fault tolerance.
- 4.2 Recommend and implement the appropriate machine learning services and features for a given problem.
- 4.3 Apply basic AWS security practices to machine learning solutions.
- 4.4 Deploy and operationalize machine learning solutions.
Whizlab’s AWS Certified Machine Learning Specialty practice exams
Whizlab’s AWS Certified Machine Learning Specialty Practice tests are designed by experts to simulate the real exam scenario. The questions are based on the exam syllabus outlined by official documentation. These practice tests are provided to the candidates to gain more confidence in exam preparation and self-evaluate them against the exam content.
Practice test content
- Free Practice test – 15 questions
- Practice test 1 – 65 questions
- Practice test 2 – 65 questions
- Practice test 3 – 65 questions
Section test content
- Core ML Concepts – 10 questions
- Data Engineering – 11 questions
- Exploratory Data Analysis – 13 questions
- Modeling – 15 questions
- Machine Learning Implementation and Operations – 12 questions