Generative AI Leader Applying Generative AI in Business Practice Test 2 — 15 Questions

Question 1

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 2

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 3

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 4

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 5

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

Question 6

A project manager wants to track the ROI of a generative AI feature that assists customer support agents. Which metric is MOST directly tied to productivity improvement?

Accepted Answer

Average handle time (AHT) per ticket. Average handle time (AHT) directly measures the time agents spend per interaction, so a reduction indicates productivity gain. CSAT measures satisfaction, not efficiency. Cost per API call is a cost metric. Adoption rate measures usage.

Answer

Adoption rate of the AI tool

Answer

Customer satisfaction (CSAT) score

Answer

Cost per API call

Question 7

A company is using a large language model to generate code reviews. They want to reduce token costs while maintaining quality. Which approach is MOST effective?

Accepted Answer

Use a smaller model fine-tuned for code (e.g., Codey). Using a smaller, specialized model for code tasks reduces cost per token and often performs well on code tasks. Caching is not applicable for code generation. Reducing max tokens might truncate output. Fewer examples may harm quality.

Answer

Reduce the max output tokens to 100

Answer

Use a zero-shot prompt instead of few-shot to reduce prompt tokens

Answer

Cache frequently used code snippets in a prompt template

Question 8

A company wants to embed a generative AI writing assistant into their Google Docs workflow. The assistant should help users draft emails and reports based on prompts. Which Google Workspace feature should they leverage?

Accepted Answer

Duet AI in Google Docs (Gemini for Google Workspace). Duet AI in Docs (now Gemini for Google Workspace) provides 'Help me write' functionality for drafting content. Apps Script can be used for custom add-ons but requires development. Vertex AI API is for external integration. Smart Compose is for Gmail only.

Answer

Google Workspace Add-ons with Vertex AI

Answer

Vertex AI API integrated via Apps Script

Answer

Gmail Smart Compose

Question 9

A marketing agency uses generative AI to create social media posts. They need the output to be in a specific JSON format for downstream processing. Which prompt technique should they use?

Accepted Answer

Specify the desired JSON schema in the prompt and use few-shot examples. Option D is correct because explicitly specifying the desired JSON schema in the prompt combined with few-shot examples provides the most reliable way to enforce structured output from a generative AI model. This technique leverages the model's pattern-matching ability by showing concrete input-output pairs, which is more effective than vague instructions or parameter adjustments alone for achieving exact JSON formatting.

Answer

Set the temperature to 0.0

Answer

Use a model fine-tuned for JSON generation

Answer

Include a system instruction to output JSON

Question 10

A company is evaluating whether to use a pre-built API or fine-tune a model for their use case. They have a large dataset of domain-specific jargon and need high accuracy on specialized terms. Which factor MOST strongly suggests fine-tuning?

Accepted Answer

The model needs to understand and generate domain-specific jargon accurately. Domain-specific vocabulary is a classic reason to fine-tune, as pre-built APIs may not handle jargon well. Latency sensitivity favors smaller models or APIs. Tight budget favors pre-built. Rapid prototyping favors pre-built.

Answer

The team wants to rapidly prototype a solution

Answer

The team has a limited budget for compute resources

Answer

The application requires low latency responses

Question 11

A company is rolling out a generative AI tool to employees. To ensure successful adoption, they plan to provide training and identify early adopters. Which change management practice is MOST critical early in the rollout?

Accepted Answer

Identify and train AI champions who can support their teams. Identifying AI champions who can advocate and help peers is crucial for organic adoption. Training all employees at once is less effective. Monitoring usage is important but later. Setting up a help desk is reactive.

Answer

Set up a dedicated help desk for AI tool issues

Answer

Conduct mandatory training for all employees before launch

Answer

Monitor usage metrics for the first month

Question 12

A data scientist is using Vertex AI RAG Engine to build a question-answering system over a large corpus of technical manuals. Users report that answers are often verbose and include irrelevant details. Which configuration change is MOST likely to improve answer conciseness?

Accepted Answer

Decrease the number of retrieved documents (k) in the retrieval step. RAG Engine allows tuning the number of retrieved documents (k). Reducing k limits the context, encouraging concise answers. Changing chunk size might affect relevance but not directly conciseness. Embedding model type affects retrieval quality. Increasing max output tokens would worsen verbosity.

Answer

Increase the chunk size of documents in the index

Answer

Use a different embedding model for retrieval

Answer

Increase the max output token limit

Question 13

A company wants to build a generative AI-powered internal knowledge base for employees. They need to integrate with existing Google Workspace documents (Docs, Slides) and allow natural language queries. Which TWO services should they combine?

Accepted Answer

Vertex AI Agent Builder. Vertex AI Agent Builder can create a conversational agent, and RAG Engine can index Google Workspace documents for retrieval. Gemini API alone is not a knowledge base. DocAI is for document parsing, not retrieval. Model Garden is a model repository.

Answer

Model Garden

Answer

Gemini API

Answer

Document AI (DocAI)

Question 14

A company is transitioning a generative AI pilot to production. They need to ensure cost predictability and scalability. Which THREE actions should they take?

Accepted Answer

Implement response caching for common queries. Response caching for common queries reduces latency and API costs by serving repeated requests from a cache instead of invoking the generative AI model each time. This directly improves cost predictability (fewer model invocations) and scalability (reduced load on the model endpoint), making it a core optimization for production deployments.

Answer

Provision reserved throughput for all requests

Answer

Conduct A/B testing on model versions

Question 15

A project manager wants to measure the success of a generative AI feature that summarizes meeting transcripts. Which TWO metrics are MOST appropriate for evaluating quality improvement?

Accepted Answer

Accuracy of summaries (e.g., factuality, completeness). Accuracy of summaries and user satisfaction directly measure quality. AHT measures productivity. Cost per summary is financial. API latency is performance.

Answer

Average handle time for meeting follow-ups

Answer

API response time

Answer

Cost per summary generated