About AWS – Certified Machine Learning – Specialty Exam
|Exam Questions||Case study, short answer, repeated answer, MCQs|
|Number of Questions||100-120|
|Time to Complete||180 minutes|
|Exam Fee||300 USD|
Overview of AWS Machine Learning – Specialty Certification
Individuals who work in artificial intelligence/machine learning (AI/ML) development or data science should take the AWS Certified Machine Learning – Specialty (MLS-C01) exam. The exam verifies a candidate’s competence to use the AWS Cloud to design, construct, deploy, optimize, train, tune, and manage machine learning solutions for specific business challenges.
A candidate’s ability to accomplish the following tasks is also validated by the exam:
- 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
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
What is considered out of scope for the target candidate?
The following is a non-exhaustive list of related job tasks that the target candidate is not expected to be able to perform. These items are considered out of scope for the exam:
- Extensive or complex algorithm development
- Extensive hyperparameter optimization
- Complex mathematical proofs and computations
- Advanced networking and network design
- Advanced database, security, and DevOps concepts
- DevOps-related tasks for Amazon EMR
- Minimum one year of hands-on experience with the AWS platform
- Professional experience managing/operating production systems on AWS
- A firm grasp of the seven AWS tenets – architecting for the cloud
- Hands-on experience with the AWS CLI and SDKs/API tools
- Understanding of network technologies as they relate to AWS
- Good grasp of fundamental Security concepts with hands-on inexperience in implementing Security controls and compliance requirements
General IT Knowledge
- 1-2 years’ experience as a system’s administrator in a systems operations role
- Experience in understanding virtualization technology
- Monitoring and auditing system’s experience
- Knowledge of networking concepts (DNS, TCP/IP, and Firewalls)
- Ability to collaborate with developers
Eligible candidates for this exam must have:
- One or more years of hands-on experience in operating AWS-based applications
- Experience in provisioning, operating, and maintaining systems running on AWS
- Ability to identify and gather requirements to define a solution to be built and operated on AWS
- Capabilities to provide AWS operations and deployment guidance and best practices throughout the life cycle of a project
Recommended AWS Knowledge
- Create data repositories for machine learning.
- Identify and implement a data ingestion solution.
- Identify and implement a data transformation solution.
- Sanitize and prepare data for modeling.
- Perform feature engineering.
- Analyze and visualize data for machine learning.
- Frame business problems as machine learning problems.
- Select the appropriate model(s) for a given machine learning problem.
- Train machine learning models.
- Perform hyperparameter optimization.
- Evaluate machine learning models.
- Build machine learning solutions for performance, availability, scalability, resiliency, and fault tolerance.
- Recommend and implement the appropriate machine learning services and features for a given problem.
- Apply basic AWS security practices to machine learning solutions.
- Deploy and operationalize machine learning solutions.
The table below lists the main content domains and their weightings on the exam.
|Domain 1||Data Engineering||20%|
|Domain 2||Exploratory Data Analysis||24%|
|Domain 4||Machine Learning Implementation and Operations||20%|