MLS-CO1: AWS Certified Machine Learning - Specialty Hands-On Labs - IPSpecialist

MLS-CO1: AWS Certified Machine Learning – Specialty Hands-On Labs

Course Information

AWS Certified:

Machine Learning – Specialty : MLS-CO1 Hands-On Labs Based on Real World Case Studies : First Edition – 2022


791 Students Enrolled


US$27.99 $34.99

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:

  1. Select and justify the appropriate ML approach for a given business problem
  2. Identify appropriate AWS services to implement ML solutions
  3. 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:

  1. The ability to express the intuition behind basic ML algorithms
  2. Experience performing basic hyperparameter optimization
  3. Experience with ML and deep learning frameworks
  4. The ability to follow model-training best practices
  5. The ability to follow deployment best practices
  6. 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:

  1. Extensive or complex algorithm development
  2. Extensive hyperparameter optimization
  3. Complex mathematical proofs and computations
  4. Advanced networking and network design
  5. Advanced database, security, and DevOps concepts
  6. DevOps-related tasks for Amazon EMR


AWS Knowledge

  1. Minimum one year of hands-on experience with the AWS platform
  2. Professional experience managing/operating production systems on AWS
  3. A firm grasp of the seven AWS tenets – architecting for the cloud
  4. Hands-on experience with the AWS CLI and SDKs/API tools
  5. Understanding of network technologies as they relate to AWS
  6. Good grasp of fundamental Security concepts with hands-on inexperience in implementing Security controls and compliance requirements


General IT Knowledge

  1. 1-2 years’ experience as a system’s administrator in a systems operations role
  2. Experience in understanding virtualization technology
  3. Monitoring and auditing system’s experience
  4. Knowledge of networking concepts (DNS, TCP/IP, and Firewalls)
  5. Ability to collaborate with developers


Intended Audience

Eligible candidates for this exam must have:

  1. One or more years of hands-on experience in operating AWS-based applications
  2. Experience in provisioning, operating, and maintaining systems running on AWS
  3. Ability to identify and gather requirements to define a solution to be built and operated on AWS
  4. Capabilities to provide AWS operations and deployment guidance and best practices throughout the life cycle of a project


Recommended AWS Knowledge

  1. Create data repositories for machine learning.
  2. Identify and implement a data ingestion solution.
  3. Identify and implement a data transformation solution.
  4. Sanitize and prepare data for modeling.
  5. Perform feature engineering.
  6. Analyze and visualize data for machine learning.
  7. Frame business problems as machine learning problems.
  8. Select the appropriate model(s) for a given machine learning problem.
  9. Train machine learning models.
  10. Perform hyperparameter optimization.
  11. Evaluate machine learning models.
  12. Build machine learning solutions for performance, availability, scalability, resiliency, and fault tolerance.
  13. Recommend and implement the appropriate machine learning services and features for a given problem.
  14. Apply basic AWS security practices to machine learning solutions.
  15. Deploy and operationalize machine learning solutions.

The table below lists the main content domains and their weightings on the exam.

  Domain Percentage
Domain 1 Data Engineering 20%
Domain 2 Exploratory Data Analysis 24%
Domain 3 Modeling 36%
Domain 4 Machine Learning Implementation and Operations 20%
Total 100%

AWS Certified:

Machine Learning – Specialty : MLS-CO1 Hands-On Labs Based on Real World Case Studies : First Edition - 2022

No, there are no pre-requisites for this course

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