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Google Generative-AI-Leader Exam Syllabus Topics:

SectionWeightObjectives
Techniques to improve gen AI model output20%- Describe prompt engineering techniques and their purpose.
  • 1. Few-shot
  • 2. One-shot
  • 3. Chain of thought
  • 4. Zero-shot
- Describe how grounding can be used to improve model output.
  • 1. Grounding with enterprise data
  • 2. Grounding with Google Search
- Describe the process of fine-tuning gen AI models.
  • 1. Reinforcement learning from human feedback (RLHF)
  • 2. Supervised tuning
Google Cloud's generative AI offerings35%- Describe Google Cloud's gen AI product and service portfolio.
  • 1. Vertex AI
  • 2. Model Garden
  • 3. Google Workspace
  • 4. Vertex AI Studio
  • 5. Gemini for Google Cloud
- Identify the use cases and strengths of Google's foundation models.
  • 1. Imagen
  • 2. Gemini
  • 3. Veo
  • 4. Gemma
Fundamentals of generative AI30%- Describe how various data types are used in gen AI and the business implications.
  • 1. Identifying the differences between labeled and unlabeled data
  • 2. Explaining the characteristics and importance of data quality and data accessibility in AI (e.g., completeness, consistency, relevance, availability, cost, format)
  • 3. Identifying the differences between structured and unstructured data, and identifying real world examples of each type
- Describe core generative AI (gen AI) concepts and use cases.
  • 1. Defining core gen AI concepts (e.g., artificial intelligence, natural language processing, machine learning, generative AI, foundation models, multimodal foundation models, diffusion models, prompt tuning, prompt engineering, large language models)
  • 2. Describing the machine learning approaches (e.g., supervised, unsupervised, reinforcement)
  • 3. Identifying how to choose the appropriate foundation model for a business use case (e.g., modality, context window, security, availability and reliability, cost)
  • 4. Identifying the stages of the machine learning lifecycle (e.g., data ingestion, data preparation, model training, model deployment, model management) and the Google Cloud tools for each stage
- Identify the core layers of the gen AI landscape and the business implications.
  • 1. Applications
  • 2. Platforms
  • 3. Agents
  • 4. Models
  • 5. Infrastructure
Business strategies for a successful gen AI solution15%- Describe best practices for a successful gen AI project.
  • 1. Building a business case
  • 2. Choosing the right model
  • 3. Evaluating AI solutions
- Describe change management best practices and their importance.
  • 1. Creating a culture of innovation
  • 2. Enabling AI adoption
- Describe Google's approach to responsible AI and its importance.
  • 1. Google's AI principles
  • 2. Responsible AI best practices

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Google Cloud Certified - Generative AI Leader Exam Sample Questions (Q82-Q87):

NEW QUESTION # 82
A development team is building an internal knowledge base chatbot to answer employee questions about company policies and procedures. This information is stored across various documents in Google Cloud Storage and is updated regularly by different departments. What is the primary benefit of using Google Cloud's RAG APIs in this scenario?

Answer: C

Explanation:
The primary benefit of RAG (Retrieval-Augmented Generation) in this context is its ability to ensure the chatbot provides accurate and up-to-date information. By retrieving relevant and recent policy documents from Cloud Storage in real-time and then grounding the LLM's response with this information, the chatbot avoids hallucinating or providing outdated answers, which is crucial for an internal knowledge base.
________________________________________


NEW QUESTION # 83
A team is using a generative AI model to automatically generate short summaries of customer feedback. They need to ensure that these summaries are concise and easy to digest. What model setting should they adjust?

Answer: C

Explanation:
The objective is to make the generated summaries concise--that is, to control their length. In the configuration of a generative AI model, particularly a large language model (LLM), the parameter used to directly control the maximum size of the response is the Output Length parameter (often referred to as max_output_tokens or max_tokens). By setting a low limit on this parameter, the team can ensure that the model is forced to terminate its response once that limit is reached, resulting in a shorter, more concise summary that is "easy to digest," as requested.


NEW QUESTION # 84
A software engineering team is experimenting with generative AI within their coding process. What is a benefit of using gen AI to create unit tests for code?

Answer: D

Explanation:
Generative AI can analyze code and rapidly propose unit-test cases, assertions, test data, boundary conditions, and failure scenarios. This accelerates test creation and reduces the repetitive manual effort required to validate individual functions or components. Engineers must still review generated tests because they may contain incorrect assumptions, omit important cases, or reproduce weaknesses in the implementation.
Generating tests does not inherently reduce the complexity of the production code, so option B is not assured.
It also cannot guarantee errorless software because unit tests cover only the scenarios they exercise and may themselves be incomplete. Human oversight remains essential for evaluating coverage, security, business requirements, and test correctness, eliminating options C and D. The defensible benefit is therefore reduced manual validation effort while maintaining qualified engineering review.


NEW QUESTION # 85
A marketing agency with a large digital asset library needs a Google Cloud solution to quickly and accurately search its digital files based on visual, spoken, or thematic content. What Google Cloud product should the agency use?

Answer: B

Explanation:
Google Cloud's Media Search (part of Vertex AI Search / Agent Search for media) is specialized for ingesting, indexing, and retrieving multimedia content-such as video, audio, and visual assets-by understanding spoken dialogue, visual actions, and semantic themes across unstructured digital libraries. Search for commerce and Vision API Product search are tailored to e-commerce product catalogs, while Document search focuses on text-heavy formats (PDFs, docs).


NEW QUESTION # 86
A global travel booking platform named VistaVoyage is developing a generative AI system to identify payment fraud across about 45 million reservations each day. The team is concerned that adversaries may make small tweaks to inputs so the model incorrectly treats fraudulent behavior as legitimate. At what point in the machine learning lifecycle should robust protections against these adversarial tactics be established to preserve security?

Answer: C

Explanation:
Adversarial robustness needs to be designed into the model from the start and then sustained in production. During training you can harden models with adversarial training, robust data augmentation, regularization, and careful evaluation against adversarial and out of distribution test sets. In production you should continuously monitor for drift, anomalies, and suspicious input patterns and you should feed incidents back into retraining so the system improves over time.
This lifecycle approach ensures protections evolve with attacker tactics and with data and model changes.


NEW QUESTION # 87
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