Generative-AI-Leader Clear Explanations and Topic Coverage
3.8 (1)
Google Generative-AI-Leader Exam Specifications
- Exam Code:
- Generative-AI-Leader
- Exam Name:
- Google Cloud Certified - Generative AI Leader Exam
- Provider:
- Time:
- 90 min
- Passing Percentage:
- 70%
- Version:
- 6.1
Question Types with Explanations
- 74 Single Select
74 Questions with Answers
- PDF Printable (Q & A)
- 3 Months
Key Features to Key Benefits
Real Exam-like Questions and Answers (74 total)
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Includes Detailed Explanations
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Free Demo Questions Available
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Google Cloud Certified - Generative AI Leader Exam Syllabus Topics
In the Google Cloud Certified - Generative AI Leader Exam, practitioners learn to govern enterprise AI programs by aligning strategy with measurable outcomes while implementing ethics, privacy, security, and data provenance controls across data, models, and deployments. They also develop the ability to compare platforms, design scalable prompt-driven architectures with data pipelines and model serving, manage the full lifecycle with versioning, testing, evaluation metrics and continuous improvement loops, and lead cross-functional initiatives that demonstrate ROI while enforcing governance, bias mitigation, explainability, monitoring, and incident response.
Major Topics Covered
- Drive AI strategy and governance for enterprise-grade generative solutions, aligning vision with measurable business outcomes.
- Assess risk and implement robust ethics, privacy, and security controls across data, models, and deployments.
- Evaluate and compare generative AI platforms, APIs, and tooling to meet performance, scalability, and cost targets.
- Design scalable architectures for prompt-driven apps, including data pipelines, feature stores, and model serving patterns.
- Establish model lifecycle management with versioning, testing, evaluation metrics, and continuous improvement loops.
- Implement prompt engineering and system design techniques to maximize reliability, controllability, and user satisfaction.
- Define governance policies for data provenance, access control, auditing, and compliance with regulatory requirements.
- Lead cross-functional programs, measure impact, and communicate ROI, risk, and progress to executives.
- Build responsible AI practice including bias mitigation, explainability, monitoring, and incident response planning.
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