What is the duration, language, and format of Professional Machine Learning Engineer - Google
- Language of Exam: English, Japanese, Korean
- Duration of Exam: 120 minutes
- Type of Questions: Multiple choice (MCQs), multiple answers
- No negative marking for wrong answers
Understanding functional and technical aspects of Professional Machine Learning Engineer - Google ML Problem Framing
The following will be discussed in Google Professional-Machine-Learning-Engineer exam dumps:
- Assessing data readiness
- Identifying data sources
- Aligning with Google AI principles and practices (e.g. different biases)
- Defining the input (features) and predicted output format
- Managing incorrect results
- Success metrics
- Define ML problem
- Defining output use
- Defining outcome of model predictions
- Assessing and communicating business impact
- Defining business problems
- Defining problem type (classification, regression, clustering, etc.)
- Assessing ML solution readiness
- Identify risks to feasibility and implementation of ML solution. Considerations include:
- Determination of when a model is deemed unsuccessful
- Key results
- Identifying nonML solutions
- Define business success criteria
The Google Professional Machine Learning Engineer certification is developed to validate the ability of the specialists to design, build, and productionize the Machine Learning models to solve business challenges with the help of Google Cloud technologies as well as their knowledge of the proven Machine Learning models & techniques. Specifically, this certificate equips the candidates with an understanding of all the aspects related to data pipeline interaction, model architecture, as well as metrics interpretation. It also provides the target individuals with the comprehension of the basic concepts of application development, data engineering, infrastructure management, and data governance. To get certified, the individuals need to take one qualifying exam.
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Professional Machine Learning Engineer - Google Certification Path
The associate level certification is focused on the fundamental skills of deploying, monitoring, and maintaining projects on Google Cloud. This certification is a good starting point for those new to cloud and can be used as a path to professional level certifications.
Professional certifications span key technical job functions and assess advanced skills in design, implementation, and management. These certifications are recommended for individuals with industry experience and familiarity with Google Cloud products and solutions.
Reference: https://cloud.google.com/certification/guides/machine-learning-engineer
Google Professional-Machine-Learning-Engineer Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Train and deploy models | 20% | - Use Vertex AI deployment features and infrastructure - Configure training jobs and environments - Deploy models for online, batch, and streaming prediction - Implement generative AI deployment patterns |
| Automate and orchestrate ML pipelines | 18% | - Implement CI/CD for ML systems - Use Vertex AI Pipelines, TFX, and other orchestration tools - Design end-to-end ML workflows - Automate retraining and model updates |
| Architect low-code AI solutions | 12% | - Design solutions using Vertex AI Studio, Model Garden, and Agent Builder - Apply responsible AI principles to low-code designs - Identify use cases for low-code/no-code AI tools |
| Monitor and optimize AI solutions | 16% | - Monitor model performance, fairness, and drift - Troubleshoot and maintain production systems - Monitor data quality and pipeline health - Optimize cost, latency, and resource usage |
| Collaborate to manage data and models | 16% | - Address data privacy, compliance, and governance - Organize and prepare enterprise data
|
| Scale prototypes into AI models | 18% | - Optimize model performance and generalization - Select appropriate model architectures and frameworks - Work with foundation models and generative AI techniques - Design and run experiments |
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