Google Professional Machine Learning Engineer
Last Update May 5, 2024
Total Questions : 268
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Exam Name | Google Professional Machine Learning Engineer |
Exam Code | Professional-Machine-Learning-Engineer |
Actual Exam Duration | 120 minutes |
Exam Registration Price | $200 |
Official Information | https://cloud.google.com/certification/guides/machine-learning-engineer |
See Expected Questions | Google Professional-Machine-Learning-Engineer Expected Questions in Actual Exam |
Take Self-Assessment | Use Google Professional-Machine-Learning-Engineer Practice Test to Assess your preparation - Save Time and Reduce Chances of Failure |
Section | Objectives |
---|---|
Framing ML problems |
1.1 Translating business challenges into ML use cases. Considerations include:
1.2 Defining ML problems. Considerations include:
1.3 Defining business success criteria. Considerations include:
1.4 Identifying risks to feasibility of ML solutions. Considerations include:
|
Architecting ML solutions |
2.1 Designing reliable, scalable, and highly available ML solutions. Considerations include:
2.2 Choosing appropriate Google Cloud hardware components. Considerations include:
2.3 Designing architecture that complies with security concerns across sectors/industries. Considerations include:
|
Designing data preparation and processing systems |
3.1 Exploring data (EDA). Considerations include:
3.2 Building data pipelines. Considerations include:
3.3 Creating input features (feature engineering). Considerations include:
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Developing ML models |
4.1 Building models. Considerations include:
4.2 Training models. Considerations include:
4.3 Testing models. Considerations include:
4.4 Scaling model training and serving. Considerations include:
|
Automating and orchestrating ML pipelines |
5.1 Designing and implementing training pipelines. Considerations include:
5.2 Implementing serving pipelines. Considerations include:
5.3 Tracking and auditing metadata. Considerations include:
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Monitoring, optimizing, and maintaining ML solutions |
6.1 Monitoring and troubleshooting ML solutions. Considerations include:
6.2 Tuning performance of ML solutions for training and serving in production. Considerations include:
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