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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Google Cloud's Generative AI Offerings | 35% | - Overview of Google Cloud generative AI services and tools - Model Garden and available models - Vertex AI generative AI capabilities - Enterprise integration and security features - Generative AI application development platforms |
| Topic 2: Techniques to Improve Generative AI Model Output | 20% | - Fine-tuning and adaptation methods - Prompt engineering principles and best practices - Mitigation of bias and inaccuracies - Evaluation and optimization of output quality |
| Topic 3: Fundamentals of Generative AI | 30% | - Foundation models: definition, capabilities, and use cases - Key technologies and differences from traditional AI - Responsible AI principles and application - Core concepts and characteristics of generative AI |
| Topic 4: Business Strategies for Successful Generative AI Solutions | 15% | - Governance, risk management, and compliance - Planning and adoption frameworks - Scaling and measuring success of generative AI initiatives - Identifying business use cases and value opportunities |
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NEW QUESTION # 78
What does Vertex AI Search enable companies to do?
Answer: C
Explanation:
Vertex AI Search is designed to enable powerful search experiences over an organization's own data (first-party), external data (third-party), and can leverage Google's knowledge graph to provide more relevant and accurate responses, especially when grounding Large Language Models (LLMs). It does not index the entire public web like Google Search.
NEW QUESTION # 79
A manager wants to ensure that only quality data is used in their AI model. Which scenario is most likely to lead to an unfair and biased outcome?
Answer: C
Explanation:
Training a facial-recognition system predominantly on one demographic creates representation bias. The model receives insufficient examples from other groups and will probably perform less accurately for those populations, producing systematically unequal outcomes. This is directly associated with fairness because model performance varies according to demographic characteristics. The other scenarios describe serious data-quality problems, but their primary effects differ. Missing merchant details can reduce fraud-detection accuracy, incorrectly encoded text introduces corruption, and bot traffic distorts customer-behavior signals. Those defects may degrade overall performance without necessarily disadvantaging a protected or underrepresented group. Responsible AI development requires representative datasets, subgroup-level evaluation, documented data provenance, and ongoing monitoring for unequal error rates. Therefore, the facial-recognition dataset presents the clearest and most direct risk of an unfair and biased outcome.
NEW QUESTION # 80
A learning and development team wants to quickly create a new hire training video with a custom avatar and voiceover that matches their company's branding and key messaging. They did not receive any money to spend on the production. What should they do?
Answer: B
Explanation:
The scenario requires quick creation of a training video using a custom avatar and voiceover while adhering to zero cost for production.
Google Vids is an AI-powered video creation app (part of Google Workspace/Gemini features) designed to make video creation accessible for teams without the overhead of traditional production. It specifically offers features like AI avatars and voiceovers for content such as trainings, demos, and onboarding videos. This directly addresses the need for a low-cost, fast solution for a new hire training video with custom branding elements (custom avatars and voiceovers are a key feature of the tool).
NEW QUESTION # 81
A company is developing an AI character for a video game. The AI character needs to learn how to navigate a complex environment and make decisions to achieve certain objectives within the game. When the AI takes actions that lead to positive outcomes, like finding a reward or overcoming an obstacle, it receives a positive score. When it takes actions that lead to negative outcomes, like hitting a wall or losing progress, it receives a negative score. Through this process of trial and error, the AI gradually improves the character's ability to play the game effectively. What machine learning should the company use?
Answer: C
Explanation:
This scenario perfectly describes reinforcement learning. In reinforcement learning, an agent learns to make decisions by interacting with an environment, receiving1 rewards for desirable actions and penalties for undesirable ones,2 and iteratively improving its behavior through trial and error to maximize cumulative reward.
________________________________________
NEW QUESTION # 82
A company is defining their generative AI strategy. They want to follow Google-recommended practices to increase their chances of success. Which strategy should they use?
Answer: A
Explanation:
Google Cloud's adoption frameworks advise an executive-sponsored, top-down strategy when initiating generative AI programs. A top-down approach ensures executive alignment with tangible business objectives, formalizes risk and governance controls, allocates appropriate cloud infrastructure and data resources, and prioritizes use cases delivering high business value rather than disconnected experimentation.
NEW QUESTION # 83
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