Google Generative AI Leader
Generative-AI-Leader
Money Back Guarantee
98% Success Rate
Real Questions
1
Fundamentals of gen AI 30%
1.1
Describe core generative AI (gen AI) concepts and use cases
5 subtopics1.1.1Core gen AI concepts
1.1.2Machine learning approaches
1.1.3Machine learning lifecycle
1.1.4Foundation model selection
1.1.5Gen AI business use cases
Learning Objectives
- Define core gen AI concepts and terminology
- Describe machine learning approaches
- Identify stages of the ML lifecycle and Google Cloud tools
- Choose appropriate foundation models for business use cases
- Identify business use cases where gen AI can create value
1.2
Describe how various data types are used in gen AI and the business implications
2 subtopics1.2.1Data quality and accessibility
1.2.2Data types
Learning Objectives
- Explain characteristics and importance of data quality in AI
- Identify differences between structured and unstructured data
- Identify differences between labeled and unlabeled data
1.3
Identify the core layers of the gen AI landscape and the business implications
1 subtopics1.3.1Gen AI landscape layers
Learning Objectives
- Identify the core layers of the gen AI landscape
- Understand business implications of each layer
1.4
Identify the use cases and strengths of Google's foundation models
1 subtopics1.4.1Google foundation models
Learning Objectives
- Identify use cases for Google's foundation models
- Understand strengths of each model family
Domain Hands-on Skills
Identifying appropriate gen AI concepts for business scenariosEvaluating data quality for AI projectsSelecting foundation models based on requirements
Common Mistakes to Avoid
- Confusing different AI/ML concepts
- Not considering data quality implications
- Mismatching models to use cases
2
Google Cloud's gen AI offerings 35%
3
Techniques to improve gen AI model output 20%
4