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Google Cloud Certified Generative AI Leader Syllabus

Fundamentals of gen AI

Core AI, ML and gen AI concepts, data types, the layers of the gen AI landscape and Google's foundation models.

30%
Weight
18
Questions
30
Marks

Core Gen AI Concepts and Use Cases

  • AI, ML, NLP and generative AI definitions
  • Foundation, multimodal and diffusion models; LLMs
  • Supervised, unsupervised and reinforcement learning
  • ML lifecycle stages and supporting Google Cloud tools
  • Choosing a foundation model for a business use case

Data in Gen AI

  • Data quality and accessibility
  • Structured vs. unstructured data
  • Labeled vs. unlabeled data
  • Business implications of data types

Gen AI Landscape and Google Models

  • Layers: infrastructure, models, platforms, agents, applications
  • Gemini use cases and strengths
  • Gemma open models
  • Imagen for images and Veo for video

Google Cloud's gen AI offerings

Google Cloud's strengths and its prebuilt, customer-experience, developer and agent tooling offerings for gen AI.

35%
Weight
21
Questions
35
Marks

Google Cloud Strengths

  • AI-first, enterprise-ready platform and open approach
  • AI-optimized infrastructure: AI Hypercomputer, TPUs, GPUs
  • Data control, privacy and governance
  • Low-code/no-code development, pre-trained models and APIs

Prebuilt and Customer Experience Offerings

  • Gemini app, Gemini Advanced and Gems
  • Gemini Enterprise: custom agents and multimodal search
  • Gemini for Google Workspace
  • Agent Search and Google Search for external search
  • Customer Engagement Suite: Conversational Agents, Agent Assist, Conversational Insights

Developer and Agent Tooling

  • Gemini Enterprise Agent Platform: Model Garden, Agent Search, AutoML
  • Prebuilt RAG and RAG APIs
  • Building custom agents with extensions, functions, data stores and plugins
  • Google Cloud AI APIs: Speech-to-Text, Translation, Document AI, Vision, Natural Language
  • Agent Studio vs. Google AI Studio

Techniques to improve gen AI model output

Overcoming model limitations with prompt engineering, grounding, RAG, tuning and monitoring.

20%
Weight
12
Questions
20
Marks

Model Limitations and Mitigations

  • Hallucinations, knowledge cutoff, bias and edge cases
  • Grounding, RAG, fine-tuning and human-in-the-loop
  • Monitoring: versioning, KPIs, performance and drift
  • Security patches and automatic model upgrades

Prompt Engineering

  • Zero-shot, one-shot and few-shot prompting
  • Role prompting and prompt chaining
  • Chain-of-thought prompting
  • ReAct prompting

Grounding Techniques

  • First-party, third-party and world data
  • RAG's effect on output quality
  • Grounding with Google Search and prebuilt RAG
  • Sampling settings: temperature, top-p, token limits, safety settings

Business strategies for a successful gen AI solution

Selecting, integrating and measuring gen AI solutions and adopting them securely and responsibly.

15%
Weight
9
Questions
15
Marks

Implementing a Transformational Solution

  • Types of gen AI solutions
  • Factors that shape business needs
  • Choosing the right solution and integration steps
  • Measuring business impact

Secure and Responsible AI

  • Security across the ML lifecycle
  • Secure AI Framework (SAIF)
  • IAM, Security Command Center and workload monitoring
  • Privacy, anonymization and pseudonymization
  • Fairness, accountability, transparency and explainability

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Timed Exams

Simulate real exam conditions with timed practice tests

  • Exam-like environment
  • Time management skills

Practice Tests

Comprehensive question bank with detailed explanations

  • Latest exam patterns
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  • Interactive diagrams
  • Topic relationships

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Learning Progress

Fundamentals of gen AI 85%
Google Cloud's gen AI offerings 92%

Practice Test Scores

95%
Latest Score
Above passing threshold

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