Generative AI Leader Free Practice Questions

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Sample Questions

Q1

A multinational CPG company is launching a generative AI initiative to create marketing copy for new products. The team is globally distributed, with varying levels of technical expertise. The primary goal is rapid prototyping and iteration on prompts without managing infrastructure. Which Google Cloud tool is the most appropriate starting point for this team?

Q2

A healthcare startup is building a generative AI application to summarize patient-doctor conversations. To comply with privacy regulations like HIPAA, it is critical that the model's responses are based exclusively on the provided medical transcript and not on its general knowledge. Which technique is essential for achieving this requirement?

Q3

As a business leader evaluating the adoption of generative AI, you are presented with Google's Secure AI Framework (SAIF). What is the primary business benefit of adopting such a framework?

Q4Multiple answers

A large language model is trained on a massive dataset of text and code from the public internet. This dataset's content ends in December 2022. If you ask this model about a major world event that occurred in March 2023, it provides an inaccurate or fabricated answer. This is a classic example of which two foundation model limitations? (Select TWO).

Q5

True or False: In the context of the generative AI landscape, the 'Platform' layer's primary role is to provide the core computing resources like GPUs and TPUs.

Q6Multiple answers

An e-commerce company wants to create a highly personalized shopping experience. Their goal is to build an AI agent that can understand a customer's conversational query (e.g., 'I'm looking for a waterproof jacket for hiking in the mountains'), check real-time inventory in a database, and then recommend specific products. Which three Google Cloud services or components are essential to build this agent? (Select THREE).

Q7

**Case Study:** **Company Background:** Future Gadgets Inc. is a consumer electronics company known for its innovative smart home devices. They have a large customer base and maintain an extensive online knowledge base with product manuals, troubleshooting guides, and community forums. The company prides itself on excellent customer support but is facing rising costs and long wait times in its human-staffed contact center. **Current Situation:** Customer support agents spend a significant amount of their time answering repetitive questions that are already documented in the knowledge base. This prevents them from focusing on complex, high-value customer issues. The company wants to deploy a generative AI solution to provide instant, accurate answers to customers on their support website, deflecting common queries from the contact center. **Requirements & Constraints:** - The solution must provide answers grounded *only* in the company's official knowledge base to avoid providing incorrect or speculative information. - It must be able to understand natural language questions from customers. - The company wants a managed, low-maintenance solution as their AI team is small. - The solution must be integrated into their existing website. **Goal:** As the project leader, you must choose the most effective Google Cloud approach to build this support agent while meeting all requirements. Which solution should you choose?

Q8

A marketing team is using a generative AI model to create social media posts. To generate diverse and eye-catching options, they need to encourage the model to produce less predictable and more 'creative' text. Which sampling parameter should they adjust?

Q9

A logistics company is implementing a gen AI solution to optimize delivery routes. The project leader must articulate the business value of Google Cloud's AI-optimized infrastructure. Which of the following is a key benefit of using Google's custom-designed TPUs for this type of task?

Q10

A project manager is outlining the stages of a new machine learning project for a stakeholder presentation. They need to correctly map the project phases. What is the correct sequence of stages in a typical machine learning lifecycle?