Generative AI Leader Applying Generative AI in Business — 40 Questions

Question 1

A company wants to build a customer service chatbot that answers questions about their internal policy documents. The documents are updated monthly, and the team cannot afford to retrain a model each time. Which approach is MOST appropriate?

Accepted Answer

Use Retrieval-Augmented Generation (RAG) with the policy documents indexed in a vector store. RAG (Retrieval-Augmented Generation) allows the LLM to retrieve relevant document sections at inference time, so knowledge stays current without retraining. The other options either require expensive retraining for each update or lack document grounding.

Answer

Train a custom model from scratch on the policy documents each month

Answer

Use a larger foundation model with a longer context window and paste all documents into each prompt

Answer

Fine-tune a base LLM on the policy documents monthly

Question 2

A marketing team wants to generate social media posts in a consistent brand voice. They have a few examples of high-performing posts. Which prompt engineering technique should they use?

Accepted Answer

Few-shot prompting with examples of past successful posts. Few-shot prompting provides the model with examples of the desired output style and tone, enabling consistent brand voice without fine-tuning.

Answer

Fine-tuning the model on all past social media posts

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Zero-shot prompting with a detailed description of the brand voice

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Chain-of-thought prompting to explain reasoning

Question 3

A company uses Vertex AI Agent Builder to create a customer support agent. They need the agent to answer questions about order status by calling an internal API. Which Vertex AI feature should they use?

Accepted Answer

Vertex AI Extensions. Extensions in Vertex AI Agent Builder allow the agent to call external APIs (including internal ones) as tools during conversation.

Answer

Vertex AI RAG Engine

Answer

Grounding with Google Search

Answer

Vertex AI Model Garden

Question 4

A hospital wants to summarize patient-doctor conversations into structured clinical notes using GenAI. They need high accuracy and must avoid hallucinated medical information. Which combination of techniques is BEST?

Accepted Answer

Fine-tune a medical-specific model on de-identified transcripts and use a structured output format in the prompt. Fine-tuning with structured output (e.g., JSON schema) and a strict prompt ensures the model produces accurate, formatted notes and reduces hallucinations.

Answer

Use RAG with medical textbooks and a low temperature setting

Answer

Use a large model with zero-shot prompting and post-process the output with rule-based checks

Answer

Use a Gemini model with a high temperature setting to encourage creativity

Question 5

A financial firm wants to use GenAI to draft emails for client communications. They need to ensure regulatory compliance and maintain a consistent professional tone. Which approach is MOST suitable?

Accepted Answer

Fine-tune a model on historical approved emails and deploy via Vertex AI API. Fine-tuning on approved examples ensures consistent tone and compliance. Fine-tuning adapts the model to the regulated domain better than prompting alone.

Answer

Use zero-shot prompting with a compliance checklist in the prompt

Answer

Use Vertex AI Agent Builder with grounding on regulatory documents

Answer

Use Gemini in Gmail with Smart Compose enabled

Question 6

A company wants to generate marketing images for a new product launch using GenAI. Which Google Cloud service should they use?

Accepted Answer

Vertex AI Imagen. Imagen on Vertex AI is Google Cloud's image generation model, available through Vertex AI APIs or Model Garden.

Answer

Gemini for Google Workspace

Answer

Vertex AI Agent Builder

Answer

Vertex AI Model Garden

Question 7

A company deploys a GenAI-powered code review assistant. During evaluation, they find that the assistant often suggests security vulnerabilities as improvements. What is the MOST likely cause?

Accepted Answer

The model was trained on a dataset with many insecure code examples. The most likely cause is that the model was trained on a dataset containing many insecure code examples. A GenAI code review assistant learns patterns from its training data; if that data includes prevalent security vulnerabilities (e.g., SQL injection, buffer overflows), the model will internalize those patterns as 'normal' or even 'desirable' improvements. This leads to the assistant suggesting insecure code changes because it is statistically replicating the flawed logic it was exposed to during training.

Answer

The model's temperature is set too low

Answer

The model is too small for code generation tasks

Answer

The prompt does not include a security constraint

Question 8

A legal firm wants to use GenAI for contract analysis. They need to extract key clauses and flag risky terms. Which combination of services on Vertex AI is BEST suited?

Accepted Answer

Fine-tune a foundation model on a dataset of labeled contracts and deploy it via Vertex AI Endpoint. Vertex AI's foundation models can parse contracts, and a fine-tuned model on legal documents would improve accuracy for clause extraction and risk flagging.

Answer

Use Vertex AI RAG Engine with a vector store of example clauses

Answer

Use Vertex AI Model Garden to select a model and then deploy it to an endpoint for real-time analysis

Answer

Use Vertex AI Agent Builder with a tool for document analysis

Question 9

A company wants to measure the ROI of a GenAI-based report generation tool. Which metric is MOST directly tied to business value?

Accepted Answer

Average time saved per report by analysts. Time saved per report directly quantifies productivity gains, a key component of ROI. Other metrics are indirect or operational.

Answer

Model accuracy on a test set

Answer

Number of API calls made per month

Answer

Percentage of reports that require human editing

Question 10

A company is building a GenAI application using the Gemini API. They want to minimize latency and cost for a high-volume use case. Which strategy is MOST effective?

Accepted Answer

Implement caching for identical or similar user queries. Caching frequent requests reduces redundant processing, reducing both latency and token costs. Batching can also help, but caching directly addresses repeated queries.

Answer

Use a fine-tuned smaller model for the specific task

Answer

Increase temperature to generate more diverse responses

Answer

Use the largest available model for all requests

Question 11

An employee wants to use GenAI to assist with writing formulas in Google Sheets. Which Gemini for Google Workspace feature should they use?

Accepted Answer

Formula assistance in Google Sheets. Gemini for Sheets provides formula assistance, helping users generate, explain, or debug formulas.

Answer

Help me write in Google Docs

Answer

Image generation in Google Slides

Answer

Smart Compose in Gmail

Question 12

A company runs a pilot for a GenAI-powered internal knowledge base assistant. They want to measure adoption. Which metric is BEST for this purpose?

Accepted Answer

Number of unique users per week divided by total employees. Option D is the best metric for measuring adoption because it directly captures the breadth of usage across the organization. Adoption is defined as the proportion of the target user base that actively uses the system, and dividing unique weekly users by total employees provides a clear percentage of uptake, which is the standard measure for adoption in enterprise GenAI deployments.

Answer

Number of follow-up questions asked per session

Answer

User satisfaction score from surveys

Answer

Average response time of the assistant

Question 13

A company wants to build a GenAI application that summarizes customer reviews into actionable insights. The solution must be cost-effective and handle variable traffic. Which TWO strategies should they implement? (Choose 2)

Accepted Answer

Use a small, fine-tuned model for summarization. Using a smaller model for summarization reduces cost for high volume. Implementing batching groups requests to optimize token usage. Other options increase cost or latency.

Answer

Deploy on a dedicated GPU cluster to minimize latency

Answer

Use the largest available foundation model for best accuracy

Answer

Store all raw reviews in the prompt context to avoid retrieval

Question 14

A company deploys a GenAI chatbot for customer support using Vertex AI Agent Builder. The chatbot sometimes gives incorrect answers. The team wants to improve accuracy without retraining the underlying model. Which THREE actions should they take? (Choose 3)

Accepted Answer

Enable Grounding with company knowledge base documents. Grounding with company documents (RAG) provides authoritative knowledge. Prompt tuning guides the model. Adding a human-in-the-loop fallback catches errors. Fine-tuning would require retraining. Increasing temperature would worsen accuracy.

Answer

Increase the model's temperature setting for more creative responses

Answer

Fine-tune the underlying model on past conversations

Question 15

A company wants to adopt GenAI for contract analysis. They are evaluating build vs. buy. Which TWO factors are MOST important when deciding to build a custom fine-tuned model instead of using a pre-built API? (Choose 2)

Accepted Answer

Desire to keep sensitive contract data within the company's VPC. Need for domain-specific terminology and data privacy requirements are key reasons to build. Cost and speed of deployment favor buy. Model accuracy can be high for both.

Answer

Need to deploy the solution within a week

Answer

Low budget for AI development

Answer

Inability to evaluate model accuracy

Question 16

A financial services company wants to automate contract analysis to extract key clauses and identify risky terms. They have thousands of PDF contracts and need a solution that can be quickly integrated into their existing document management system. Which Google Cloud service is MOST suitable?

Accepted Answer

Document AI (DocAI). DocAI is purpose-built for document understanding and structured extraction from PDFs. Vertex AI Studio focuses on prompt design for generative models, while Model Garden is for selecting models. Vertex AI Agent Builder is for building conversational agents, not document processing.

Answer

Model Garden

Answer

Vertex AI Studio

Answer

Vertex AI Agent Builder

Question 17

A retail company has deployed a generative AI chatbot for customer support. They notice that the model sometimes provides incorrect product information. The team wants to ground the model's responses in their product catalog to improve accuracy. Which Vertex AI feature should they enable?

Accepted Answer

Enable Grounding with Google Search. Option B is correct because Grounding with Google Search allows the model to retrieve real-time, authoritative information from the product catalog via Vertex AI's grounding service, ensuring responses are based on verified data rather than the model's internal knowledge. This feature directly addresses the need to reduce hallucinations by anchoring the model's output to a trusted source, such as a product database, without requiring custom retrieval infrastructure.

Answer

Use Vertex AI RAG Engine

Answer

Increase the model's temperature setting

Answer

Fine-tune the model with product catalog updates

Question 18

A marketing team wants to generate multiple versions of ad copy for A/B testing. They need consistent brand tone across all outputs. Which prompt engineering technique is most effective?

Accepted Answer

Include few-shot examples of previous ad copy that match the desired tone. Providing few-shot examples (sample outputs with the desired tone) is the most direct way to enforce consistency. Zero-shot gives no guidance; system instructions help but are less effective than examples for tone consistency. Temperature settings control randomness.

Answer

Use a zero-shot prompt with a detailed description of the brand voice

Answer

Use a system instruction that says 'Be consistent'

Answer

Set the temperature to 0.0 to eliminate randomness

Question 19

A healthcare organization wants to use generative AI to draft email responses to patient inquiries. They need to ensure that the model never generates medical advice and always includes a disclaimer. Where should they enforce these constraints?

Accepted Answer

Configure safety settings and system instructions in the Vertex AI API call. Safety settings and system instructions are applied at the API call level to constrain model behavior. Fine-tuning would embed the rules but is heavy. Grounding doesn't enforce constraints. Model Garden is for model selection.

Answer

Select a model from Model Garden that is pre-trained on medical data

Answer

Fine-tune the model with a dataset that only includes disclaimers

Answer

Use Grounding with Google Search to restrict knowledge to approved sources

Question 20

A company wants to build a proof-of-concept generative AI application quickly. They have limited ML expertise and need to test multiple foundation models with different prompts. Which Vertex AI tool should they use?

Accepted Answer

Vertex AI Studio. Vertex AI Studio is designed for rapid prototyping with prompt design, model testing, and evaluation without requiring coding. Model Garden is for browsing models but not for interactive testing. Agent Builder is for building agents. Custom training is for production.

Answer

Vertex AI Agent Builder

Answer

Model Garden

Answer

Vertex AI Training