The AWS Certified Machine Learning – Specialty (MLS-C01) exam is intended for individuals who perform an artificial intelligence/machine learning (AI/ML) development or data science role. The exam validates a candidate’s ability to design, build, deploy, optimize, train, tune, and maintain ML solutions for given business problems by using the AWS Cloud. The exam also validates a candidate’s ability to complete the following tasks:
- Select and justify the appropriate ML approach for a given business problem
- Identify appropriate AWS services to implement ML solutions
- Design and implement scalable, cost-optimized, reliable, and secure ML solutions
65 questions; either multiple choice or multiple response
180 mins to complete the exam
The target candidate is expected to have 2 or more years of hands-on experience developing, architecting, and running ML or deep learning workloads in the AWS Cloud.
Recommended AWS knowledge
The target candidate should have the following knowledge:
- The ability to express the intuition behind basic ML algorithms
- Experience performing basic hyperparameter optimization
- Experience with ML and deep learning frameworks
- The ability to follow model-training best practices
- The ability to follow deployment best practices
- The ability to follow operational best practices
AWS Certified Machine Learning Specialty exam is for:
- Candidates who are interested in Data Science.
- Business Decision Makers
- Data Platform Engineers
- One who is pursuing to build a career in ML
Domain 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
- 2.1 Sanitize and prepare data for modeling.
- 2.2 Perform feature engineering.
- 2.3 Analyze and visualize data for machine learning.
Domain 3: Modeling
- 3.1 Frame business problems as machine learning problems
- 3.2 Select the appropriate model(s) for a given machine learning problem.
- 3.3 Train machine learning models
- 3.4 Perform hyperparameter optimization.
- 3.5 Evaluate machine learning models.
Domain 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.