AWS Machine Learning Practice Exam

The AWS Machine Learning certification helps organizations find and nurture people with important skills for using cloud technology. Getting certified in AWS Certified Machine Learning - Specialty shows that you're skilled in creating, training, adjusting, and launching machine learning models on AWS. This certification exam is for people who work in artificial intelligence (AI) or data science. It proves you can plan, create, deploy, improve, train, adjust, and manage machine learning solutions for different business needs using AWS. This exam also validates the candidate's skills in the:

• Choosing the best machine learning method for a specific business issue.
• Picking the right AWS services for setting up machine learning solutions.
• Creating and setting up machine learning solutions that are big, cost-effective, reliable, and safe.

Who should take this exam?

The AWS Certified Machine Learning - Specialty certification is for people who work in development or data science roles and have over a year of experience developing, designing, or managing machine learning and deep learning tasks on the AWS Cloud. Before you take this exam, it's best if you:

• Have at least two years of practical experience working on machine learning or deep learning tasks on the AWS Cloud.
• Can explain the basic ideas behind machine learning algorithms.
• Experience in basic hyperparameter optimization.
• Are familiar with machine learning and deep learning frameworks.
• Can follow the best practices for training, launching, and managing models.

Knowledge Requirement:

The ideal candidate should have a minimum of 2 years of hands-on experience in developing, architecting, and managing machine learning or deep learning workloads within the AWS Cloud environment. Further, the recommended AWS Knowledge includes:

• Demonstrated capability to articulate the underlying principles of fundamental ML algorithms.
• Proficiency in executing basic hyperparameter optimization.
• Familiarity with various ML and deep learning frameworks.
• Understanding and adherence to best practices in model training.
• Knowledge and application of best practices in deployment.
• Understanding and implementation of operational best practices.

Exam Details 

• Exam Code: MLS-C01
• Exam Name: AWS Certified Machine Learning - Specialty exam
• Exam Languages: English, Japanese, Korean, and Simplified Chinese
• Exam Questions: 65 Questions
• Time: 180 minutes
• Passing Score: 750

AWS Machine Learning Exam Topics

The AWS Machine Learning exam covers the following topics - 

Domain 1: Understand Data Engineering (20%)

• Create data repositories for ML.
• Identify and implement a data ingestion solution.
• Identify and implement a data transformation solution.

Domain 2: Learn Exploratory Data Analysis (24%)

• Sanitize and prepare data for modeling.
• Perform feature engineering.
• Analyze and visualize data for ML. 

Domain 3: Understand Modeling (36%)

• Frame business problems as ML problems.
• Select the appropriate model(s) for a given ML problem.
• Train ML models.
• Perform hyperparameter optimization. 

Domain 4: Explore Machine Learning Implementation and Operations (20%)

• Build ML solutions for performance, availability, scalability, resiliency, and fault tolerance.
• Recommend and implement the appropriate ML services and
• features for a given problem.
• Apply basic AWS security practices to ML solutions.
• Deploy and operationalize ML solutions.