Generative AI Leader Applying Generative AI in Business Practice Test 4 — 15 Questions

Question 1

A marketing team wants to generate product descriptions for 1000 new items. They need consistent brand voice and the ability to review and edit outputs before publishing. Which approach is most suitable?

Accepted Answer

Use the Gemini API with a few-shot prompt that includes examples of the desired brand voice. Using the Gemini API with a few-shot prompt containing examples of desired tone gives the team control and consistency while allowing quick edits. Fine-tuning would take time and cost, and a chatbot is not designed for batch generation. Grounding with Google Search is unnecessary for generated descriptions.

Answer

Use the Gemini API with grounding in Google Search to ensure factual accuracy

Answer

Deploy a customer service chatbot in Vertex AI Agent Builder to generate descriptions

Answer

Build a custom fine-tuned model on past product descriptions

Question 2

A company is evaluating ROI for a GenAI-based code review assistant. Which metric set BEST captures both productivity and quality improvements?

Accepted Answer

Time saved per code review and bug detection rate (percentage of bugs caught before deployment). Time saved per review (productivity) and bug detection rate (quality) directly measure the tool's impact. Code churn and developer satisfaction are secondary. Defect escape rate is important but harder to measure directly for code review.

Answer

Number of reviews completed per day and lines of code written

Answer

Model inference latency and cost per token

Answer

Developer satisfaction score and reduction in code churn (percentage of code rewritten)

Question 3

A legal firm wants to automate contract analysis to extract key clauses and risks. They have 10,000 contracts in PDF format. The solution must handle varying layouts and be cost-effective. Which approach is BEST?

Accepted Answer

Use Document AI to convert PDFs to structured text, then use Vertex AI with a prompt that specifies clauses to extract. Using Document AI to parse PDFs into text, then Vertex AI with a structured prompt for clause extraction combines robust document understanding with flexible GenAI. Fine-tuning on 100 contracts is insufficient for layout variation. Agent Builder is overkill. Direct Gemini API on raw PDFs loses document structure.

Answer

Build a retrieval-augmented generation (RAG) system in Vertex AI Agent Builder

Answer

Use the Gemini API directly with the raw PDF files as input

Answer

Fine-tune a Gemini model on 100 annotated contracts and run inference on all contracts

Question 4

A project manager wants to automatically generate weekly status reports from meeting notes and project data. The team uses Google Workspace. Which built-in capability is the QUICKEST to implement?

Accepted Answer

Use Gemini for Workspace (Duet AI) in Google Docs and Google Meet to generate summaries and reports. Gemini for Workspace (Duet AI) can generate summaries directly in Google Docs and Meet, leveraging existing data with no custom development. Custom API integration or Model Garden would require more effort. Apps Script is for custom automation, not built-in.

Answer

Select a model from Model Garden and deploy it as a private endpoint for report generation

Answer

Use Vertex AI Studio to design a custom prompt and call the Gemini API from a custom app

Answer

Write a Google Apps Script to call the Gemini API and format the report

Question 5

A company is piloting a GenAI feature for internal knowledge base search. During the pilot, users report that the AI sometimes gives incorrect answers based on outdated documents. What is the MOST effective way to address this issue?

Accepted Answer

Implement Retrieval-Augmented Generation (RAG) with the knowledge base documents indexed in a vector store and ensure the index is updated when documents change. Grounding with Google Search (or enterprise search) ties responses to fresh sources, but ensuring the knowledge base is up-to-date and using RAG with the latest documents is more direct. Fine-tuning on old data will not fix freshness. Reducing temperature helps but does not solve outdated content. Prompting to refuse uncertain answers can help but not as reliable as RAG.

Answer

Add a system instruction to the prompt telling the model to only answer if it is confident

Answer

Decrease the temperature parameter of the model to 0 to reduce randomness

Answer

Fine-tune the model on the current knowledge base to improve accuracy

Question 6

A startup with limited ML expertise wants to add a GenAI feature to their SaaS application that can generate personalized email drafts for users. They need fast time-to-market and low maintenance. Which build-vs-buy decision is BEST?

Accepted Answer

Buy a pre-built API such as the Gemini API and integrate it with prompt engineering for personalization. Using pre-built APIs (like Gemini API) with prompt engineering is the fastest path and requires no ML expertise. Custom fine-tuning would require data and expertise. Building from scratch is too heavy. Model Garden still requires deployment and management.

Answer

Select a model from Model Garden and deploy it on Vertex AI

Answer

Fine-tune an open-source model on a corpus of email drafts to create a custom model

Answer

Build a custom transformer model from scratch

Question 7

A company is deploying a GenAI-powered email drafting feature. They want to control costs while maintaining low latency for real-time suggestions. Which strategy is MOST effective?

Accepted Answer

Implement caching for frequently generated email drafts and use a smaller model variant for real-time requests. Caching common prompt-output pairs reduces API calls for repeated inputs. Choosing a smaller model balances speed and cost. Batching is for offline processing, not real-time. Long context is more expensive.

Answer

Batch all email drafting requests and run them every hour

Answer

Use the largest available model and increase the number of tokens per request to generate more complete drafts

Answer

Use a large model with a longer context window to reduce the number of API calls

Question 8

A data analyst wants to use Gemini in Google Sheets to help with complex formulas. Which feature should they use?

Accepted Answer

Gemini for Workspace in Google Sheets. Gemini for Workspace in Google Sheets provides an AI-powered side panel that can generate, explain, and debug complex formulas directly within the spreadsheet environment. This feature is specifically designed to assist with formula creation and data analysis tasks, making it the correct choice for a data analyst using Gemini in Google Sheets.

Answer

Model Garden in Vertex AI

Answer

Smart Compose in Gmail

Answer

Help me write in Google Docs

Question 9

A company wants to create a GenAI application that can answer questions about their internal policy documents, but they require that the model's responses are grounded in the actual documents to avoid hallucination. Which Vertex AI feature should they use?

Accepted Answer

Grounding with Google Search (or enterprise data sources). Grounding with Google Search or enterprise data sources ensures responses are based on verified sources. RAG Engine is a broader tool for building RAG applications. Vertex AI Studio is for prompt design. Agent Builder is for conversational agents but may not provide grounding directly.

Answer

Vertex AI Studio

Answer

Vertex AI Agent Builder

Answer

Model Garden

Question 10

A large enterprise is planning to roll out a GenAI assistant for contract negotiation. The legal team wants to ensure that the assistant's outputs are consistent and follow a predefined format for downstream processing. What is the BEST prompt engineering technique?

Accepted Answer

Define a structured output schema (e.g., JSON) in the prompt and request the model to output in that format. Option C is correct because defining a structured output schema (e.g., JSON) in the prompt explicitly constrains the model's output format, ensuring consistency and machine-readability for downstream contract negotiation processing. This technique leverages the model's instruction-following capability to produce parseable, schema-compliant responses, which is critical for automated legal workflows where variable formatting would break integration.

Answer

Use few-shot examples with variable formatting

Answer

Add a system instruction to 'be consistent'

Answer

Use chain-of-thought prompting to have the model reason step-by-step

Question 11

A company wants to adopt GenAI for internal knowledge management. They plan to start with a small pilot team, gather feedback, and then expand. Which change management approach is MOST aligned with this strategy?

Accepted Answer

Pilot with a small group, identify AI champions, collect feedback, and iteratively expand. Iterative rollout with a pilot group, AI champions, and measuring adoption is a proven change management pattern. Starting with the entire organization is risky. Mandatory training may cause resistance. Focusing only on technical deployment ignores the human side.

Answer

Conduct mandatory training for all employees before the rollout

Answer

Deploy the solution to the entire organization at once with a communication campaign

Answer

Focus solely on the technical deployment without change management activities

Question 12

A developer wants to add a GenAI feature to their existing web application. They need to integrate with the app's backend using REST APIs. Which integration pattern is MOST appropriate?

Accepted Answer

Integrate via Vertex AI API or Gemini API. API-first integration using Vertex AI API or Gemini API is the standard way to add GenAI to existing applications. Workspace add-ons are for Google Workspace apps. Apps Script is for automating Workspace. Agent Builder is for building conversational agents, not general API integration.

Answer

Use Apps Script to call the Gemini API

Answer

Use Vertex AI Agent Builder to create an agent and embed it via iframe

Answer

Build a Google Workspace add-on

Question 13

A company wants to build a GenAI-powered customer support chatbot. They require the chatbot to provide accurate answers based on the latest product documentation, and they need to control costs by minimizing token usage. Which TWO strategies should they use?

Accepted Answer

Cache frequent queries and their responses. RAG ensures answers are grounded in latest docs. Caching common queries reduces token usage. Fine-tuning is expensive and not dynamic. Using a large model increases cost. Long context windows increase token usage.

Answer

Use the largest available model for maximum accuracy

Answer

Use a longer context window to include entire documents in the prompt

Answer

Fine-tune a large language model on the product documentation

Question 14

A company is moving a GenAI proof-of-concept to production. The pilot showed promising results, but the production environment must handle higher traffic, ensure low latency, and manage costs. Which THREE actions should they take?

Accepted Answer

Implement caching for common or repetitive queries. A/B testing helps compare versions. Choosing a smaller model balances cost and latency. Caching reduces repeated work. Fine-tuning is expensive and not necessary. A single endpoint without testing is risky.

Answer

Deploy a single model endpoint without A/B testing to simplify architecture

Answer

Fine-tune the model on the entire dataset for better performance

Question 15

A company wants to use GenAI to generate marketing content such as blog posts and social media updates. They need the content to be on-brand and factually accurate. Which TWO features should they use?

Accepted Answer

Provide few-shot examples in the prompt to set the brand tone. Few-shot examples in prompts help maintain brand voice. Grounding with Google Search ensures factual accuracy. Vertex AI Studio is for prompt design but not directly for accuracy. Fine-tuning may be overkill. Longer context may dilute the message.

Answer

Use a longer context window to include all brand guidelines

Answer

Fine-tune the model on a large corpus of past marketing content

Answer

Use Vertex AI Studio to design prompts with no additional grounding