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AWS Certified Machine Learning Machine Learning Specialty

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AWS Certified Machine Learning Specialty Practice Exam

Format – Practice Exam

No. of Questions – 262 Questions

MCQ and Answers with Explanations

Last Update – 25th July of 2021

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AWS Certified Machine Learning Specialty Practice Exam

Format – Practice Exam

No. of Questions – 262 Questions

MCQ and Answers with Explanations

Last Update – 25th July 2021

AWS Certified Machine Learning – Specialty

About AWS Certified Machine Learning – Specialty Practice Exam

AWS Certified Machine Learning – Specialty certification exam is intended for individuals who perform a development or data science role. It validates a candidate’s ability to design, implement, deploy, and maintain machine learning (ML) solutions for given business problems.

Skills Validated by the Certification

  • 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

Recommended AWS Knowledge

Successful candidate likely has one to two years of hands-on experience developing, architecting, or running ML/deep learning workloads on the AWS Cloud, along with –

  • Ability to express the intuition behind basic ML algorithms
  • Experience performing basic hyperparameter optimization
  • Experience with ML and deep learning frameworks
  • Ability to follow model-training best practices
  • Ability to follow deployment and operational best practices

Course outline for AWS Certified Machine Learning – Specialty Practice Exam

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.

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