Professional Machine Learning Engineer

Professional Machine Learning Engineer

The Google Cloud Certified - Professional Machine Learning Engineer certification is a professional-level certification for individuals who want to demonstrate their skills in designing, developing, and deploying machine learning models on Google Cloud AI Platform. This certification demonstrates expertise in building, deploying, and managing machine learning models.

Focus

The Professional Machine Learning Engineer certification focuses on Designing, developing, and deploying machine learning models on Google Cloud AI Platform.

Exam Duration

The Professional Machine Learning Engineer exam duration is 2 hours

Num of Question

The exam contains a total of 115 multiple-choice questions

Exam Fee

The exam fee is $269 USD.

Exam Validity

The CCRN-Pediatric certification is valid for No expiration date.

Prior Certification

• Google Cloud AI Engineer, Data Engineer, or Cloud Developer certifications are recommended but not required.

Exam Format

The Professional Machine Learning Engineer exam is a Multiple-choice questions only.

Passing Conditions

To pass Professional Machine Learning Engineer exam, applicants must achieve a minimum score of Passing score is 700 out of 1400, which is 50%

Who Should Take

Data scientists, data engineers, data analysts, and developers who have hands-on experience with Google Cloud AI Platform and want to demonstrate their skills in building, deploying, and managing machine learning models.

• Machine Learning Engineer
• Data Scientist
• Data Engineer
• Developers

Prerequisites

2+ years of practical experience in building and deploying machine learning models on Google Cloud AI Platform or related technologies.

Or can substitute with certifications:

• Google Cloud AI Engineer, Data Engineer, or Cloud Developer certifications

Certification Process

• Register for the exam on the Google Cloud Certification website.
• Download or stream the practice exam questions from the Google Cloud Certification website.
• Access the exam on a secure proctoring environment provided by the test administrator.

Domains with Weights

• Machine Learning Model Development (45%): Ability to design and develop scalable machine learning models to solve complex problems and drive business outcomes. This includes knowledge of TensorFlow, scikit-learn, and other machine learning frameworks and tools.
• Model Deployment and Management (30%): Ability to deploy and manage machine learning models on Google Cloud AI Platform. This includes knowledge of model deployment, model serving, and model monitoring and management.
• Data and Infrastructure (20%): Ability to prepare and preprocess data for machine learning models. This includes knowledge of data storage, data warehousing, and data processing on Google Cloud.
• Security, Ethics, and Compliance (5%): Ability to ensure security, ethics, and compliance when building, deploying, and managing machine learning models on Google Cloud AI Platform. This includes knowledge of data privacy, data security, and model interpretability.