AWS Certified Machine Learning - Specialty (MLS-C01)

AWS Certified Machine Learning - Specialty (MLS-C01)

Demonstrates expertise in designing, building, and deploying scalable machine learning models on the AWS cloud platform.

Focus

The AWS Certified Machine Learning - Specialty (MLS-C01) certification focuses on Machine Learning

Exam Duration

The AWS Certified Machine Learning - Specialty (MLS-C01) exam duration is 170 minutes (2 hours 50 minutes)

Num of Question

The exam contains a total of 85 questions

Exam Fee

The exam fee is $300 USD.

Exam Validity

The CCRN-Pediatric certification is valid for 3 years from the date of the exam pass.

Prior Certification

• AWS Certified Developer - Associate or equivalent experience

Exam Format

The AWS Certified Machine Learning - Specialty (MLS-C01) exam is a Multiple-choice, multiple-response, fill-in-the-blank, and drag-and-drop questions

Passing Conditions

To pass AWS Certified Machine Learning - Specialty (MLS-C01) exam, applicants must achieve a minimum score of Passing score is 720 on a scale of 100-1000

Who Should Take

Data Scientists, Machine Learning Engineers, Data Engineers, and IT Professionals who design, build, and deploy machine learning workloads on AWS.

• Machine Learning Engineer
• Data Scientist
• Data Engineer
• Cloud Architect

Prerequisites

1-2 years of experience with machine learning on AWS.

Or can substitute with certifications:

• AWS Certified Developer - Associate
• Equivalent experience

Certification Process

• Step 1: Prepare for the Exam - Study AWS Machine Learning Services and obtain the necessary experience.
• Step 2: Register for the Exam - Schedule the exam through the AWS Training and Certification website.
• Step 3: Pass the Exam - Pass the AWS Certified Machine Learning - Specialty (MLS-C01) exam.

Domains with Weights

• Domain 1: Data Ingestion, Engineering, and Processing (21%): Choose the appropriate data ingestion, processing, and preparation strategy for machine learning workloads on AWS.
• Domain 2: Training, Model Tuning, and Deployment (25%): Design, build, and deploy scalable machine learning models on the AWS cloud platform.
• Domain 3: Model Operations, Model Maintenance, and Model Explanation (29%): Operate, maintain, and explain machine learning models on AWS.
• Domain 4: ML ROI, Security, and Governance (25%): Determine the return on investment (ROI) of a machine learning project, and ensure that the project meets security and governance requirements on AWS.