Generative AI Leader Applying Generative AI in Business Practice Test 9 — 15 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. Retrieval-Augmented Generation (RAG) is the most appropriate approach because it allows the chatbot to answer questions using the latest policy documents without retraining the model. By indexing the documents in a vector store and retrieving relevant chunks at query time, RAG provides up-to-date answers while keeping the underlying LLM static, which is ideal for monthly document updates.

Answer

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

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Fine-tune a base LLM on the policy documents monthly

Answer

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

Question 2

A marketing team wants to use generative AI to create ad copy that matches their brand voice. They have several examples of previous high-performing ads. Which Vertex AI Studio feature would best help them achieve consistent tone and style without custom model training?

Accepted Answer

Few-shot prompting with examples of previous ads. Option C is correct because few-shot prompting in Vertex AI Studio allows the model to infer the desired tone and style from a small set of example ads without requiring custom model training. This approach leverages the model's in-context learning capability, making it ideal for quickly adapting to a brand voice while avoiding the cost and complexity of fine-tuning.

Answer

Use of a pre-built template in Vertex AI Studio

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Supervised fine-tuning on the ad examples

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Model evaluation to compare outputs

Question 3

A healthcare company is deploying a GenAI-powered report generation system that processes patient summaries. They need the output to be structured JSON for downstream ingestion. The team is using Vertex AI Studio prompt design. Which approach best ensures consistent structured output?

Accepted Answer

Define the JSON schema in the system instruction and provide one few-shot example of the expected JSON. Option B is correct because defining the JSON schema in the system instruction and providing a few-shot example directly constrains the model's output format at inference time, leveraging Vertex AI's instruction-following capabilities. This approach ensures the model generates valid JSON consistently without post-processing, as the schema acts as a structural template that the model learns to replicate.

Answer

Ask the model to output plain text and parse it with a regex after generation

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Use a lower temperature (0.0) and hope the model outputs valid JSON

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Generate output in Markdown and convert to JSON using a secondary script

Question 4

A financial services firm wants to use generative AI to assist employees in drafting emails. They are evaluating Duet AI in Gmail (now Gemini in Workspace). Which capability directly supports this use case?

Accepted Answer

Smart Compose in Gmail. Smart Compose in Gmail is a generative AI feature that provides real-time, context-aware suggestions to help users draft emails faster. It directly supports the use case of assisting employees in drafting emails by predicting and completing sentences as they type, reducing effort and improving efficiency.

Answer

Speaker notes generation in Slides

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Meeting summaries in Meet

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Help me write in Docs

Question 5

A legal department wants to automate contract analysis using GenAI. They need to identify risky clauses and extract key dates. Which Google Cloud service is best suited for this task?

Accepted Answer

Vertex AI Agent Builder. Vertex AI Agent Builder is the correct choice because it enables the creation of custom generative AI agents that can analyze contract text, identify risky clauses, and extract key dates using large language models (LLMs) and retrieval-augmented generation (RAG). It provides a no-code/low-code environment to build agents that leverage enterprise data, making it ideal for automating contract analysis without extensive ML expertise.

Answer

Document AI

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BigQuery

Answer

Model Garden

Question 6

A company is piloting a GenAI code review assistant. Developers report that the assistant often suggests incorrect or insecure code snippets. The team wants to improve the assistant's reliability before expanding the pilot. Which approach should they prioritize?

Accepted Answer

Fine-tune the base model on a curated dataset of secure code review examples. Fine-tuning on a curated dataset of secure code review examples directly addresses the assistant's tendency to suggest incorrect or insecure code by adapting the model's behavior to the specific patterns and standards of secure coding. Unlike retrieval or parameter adjustments, this approach modifies the model's weights to prioritize security and correctness in its outputs, making it the most effective method for improving reliability in a targeted domain.

Answer

Use RAG to retrieve code snippets from a database of known vulnerabilities

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Increase the model's temperature parameter to generate more diverse suggestions

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Switch to a larger foundation model without additional tuning

Question 7

Which of the following best describes the primary benefit of using Grounding with Google Search when building a GenAI chatbot?

Accepted Answer

It allows the model to access real-time information from the internet to reduce hallucinations. Grounding with Google Search connects the GenAI chatbot to real-time internet data, allowing it to retrieve current facts and events that the model was not trained on. This reduces hallucinations by ensuring responses are based on verified, up-to-date information rather than relying solely on the model's static training data.

Answer

It reduces the model's latency by caching responses

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It enables the model to generate images based on text descriptions

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It provides fine-tuning capabilities for domain-specific data

Question 8

A retail company wants to generate personalized marketing emails at scale. They have a customer database and past purchase history. Which implementation pattern is most cost-effective and scalable?

Accepted Answer

Use Vertex AI API in batch mode, sending customer data in each request and caching responses for common segments. Option B is correct because using Vertex AI API in batch mode allows the company to process large volumes of customer data asynchronously, significantly reducing costs compared to real-time or dedicated endpoint deployments. Caching responses for common segments further optimizes by avoiding redundant API calls, making this pattern both cost-effective and scalable for generating personalized marketing emails at scale.

Answer

Fine-tune a model on all customer data and deploy a dedicated endpoint

Answer

Use Duet AI in Gmail to manually draft each email

Answer

Use real-time streaming with WebSocket connections to generate each email on-demand

Question 9

A company is migrating a GenAI proof-of-concept to production. During the pilot, they used a large model (e.g., Gemini 1.5 Pro) and incurred high costs. The use case is simple: generating short product descriptions from structured data. Which cost optimization strategy should they implement first?

Accepted Answer

Switch to a smaller model like Gemini 1.5 Flash and use structured prompts. Option D is correct because the primary cost driver in this scenario is the model size itself. Since the use case is simple (generating short product descriptions from structured data), a smaller model like Gemini 1.5 Flash can handle the task with significantly lower inference cost per token. Structured prompts further optimize by reducing token waste and ensuring consistent output, making this the most direct and impactful first step.

Answer

Fine-tune a smaller model on the specific task

Answer

Implement batch processing to group requests

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Reduce the model's temperature to 0.0

Question 10

A company wants to evaluate the ROI of deploying a GenAI tool for customer support. They plan to measure productivity gains. Which metric is most directly tied to productivity improvement?

Accepted Answer

Average handling time per ticket. Average handling time (AHT) per ticket is the most direct metric for productivity improvement because it quantifies the time an agent spends resolving a customer issue. A GenAI tool that generates response drafts or retrieves knowledge base articles reduces the agent's wrap-up and research time, directly lowering AHT. This is a standard contact center metric (e.g., measured in seconds or minutes) that maps to cost savings and throughput gains.

Answer

Total token consumption

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Ticket deflection rate

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Customer satisfaction score (CSAT)

Question 11

A team wants to build a GenAI application that can interact with external APIs (e.g., to check inventory or place orders). Which Vertex AI component provides this capability?

Accepted Answer

Vertex AI Extensions. Extensions allow agents to call external APIs and perform actions beyond text generation. Agent Builder is the platform but Extensions provide the specific API integration. Model Garden and Grounding do not support API calls.

Answer

Grounding with Google Search

Answer

Model Garden

Answer

Vertex AI Agent Builder

Question 12

During a proof-of-concept, a team notices that their GenAI-powered meeting summarizer occasionally includes hallucinated details. They want to improve summary accuracy before production. Which action would be most effective?

Accepted Answer

Provide the meeting transcript as context in the prompt. Providing the meeting transcript as context grounds the model in factual data, reducing hallucinations. Few-shot prompting may help, but the primary issue is lack of source material. Adjusting temperature is less impactful. Switching to a larger model without context may not help.

Answer

Upgrade to a larger model

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Decrease the temperature to 0.0

Answer

Use few-shot prompting with example summaries

Question 13

A company is implementing a GenAI-powered internal knowledge base chatbot. They need to ensure answers are based on company documents and the system should be easy to update as documents change. Which TWO components should they use?

Accepted Answer

Vertex AI RAG Engine. Option C (Vertex AI RAG Engine) is correct because it provides a managed Retrieval-Augmented Generation (RAG) service that directly ingests company documents, indexes them into a vector database, and retrieves relevant chunks to ground the chatbot's answers. This ensures responses are based on the latest document content, and the system is easy to update by simply re-indexing or refreshing the document source without retraining the model.

Answer

Document AI

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Vertex AI Studio

Answer

Model Garden

Question 14

A company wants to adopt GenAI for code generation and review. To ensure code quality and security, they plan to implement a change management program. Which THREE actions are most effective?

Accepted Answer

Create a code review checklist specifically for AI-generated code. Training developers, piloting with a small team, and creating review checklists address skills, risk mitigation, and quality control. Phasing out senior developers would be counterproductive. A single champion is insufficient.

Answer

Appoint a single AI champion to manage all code generation

Answer

Phase out senior developers to rely entirely on AI-generated code

Question 15

A team is evaluating whether to build a custom fine-tuned model or use a pre-built API for a document summarization task. Which TWO factors most strongly support using a pre-built API?

Accepted Answer

The team lacks machine learning expertise to fine-tune a model. Option B is correct because a pre-built API eliminates the need for in-house machine learning expertise, as the team can simply call the API endpoint without managing model training, hyperparameter tuning, or infrastructure. This is especially critical when the team lacks the specialized skills required for fine-tuning, such as data preparation, gradient computation, and evaluation on domain-specific metrics like ROUGE.

Answer

The summarization domain is very specialized and requires unique terminology

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The expected usage volume is millions of API calls per day

Answer

The company has strict data residency requirements that prevent sending data to an API