Generative AI Leader Applying Generative AI in Business Practice Test 8 — 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 based on the latest policy documents without retraining the model. By indexing the documents in a vector store and retrieving relevant chunks at inference time, RAG ensures the model's responses are grounded in the current data, accommodating monthly updates efficiently.

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

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Fine-tune a base LLM on the policy documents monthly

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

Question 2

A marketing team needs to generate personalized email campaigns for thousands of customers. They want to maintain brand tone consistency and avoid manual writing. Which GenAI approach is BEST suited?

Accepted Answer

Use Vertex AI Studio with prompt design and few-shot examples in the prompt. Option A is correct because Vertex AI Studio enables prompt engineering with few-shot examples, allowing the team to generate personalized emails while maintaining brand tone consistency without fine-tuning or custom training. This approach leverages a pre-trained large language model (LLM) with carefully designed prompts that include brand guidelines and a few examples, ensuring output adheres to the desired style and context. It avoids the overhead of fine-tuning or building custom models, making it ideal for rapid deployment and iterative refinement.

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Fine-tune a small model on brand guidelines only

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Embed a rules-based template engine with no AI

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Train a custom model from scratch on past campaigns

Question 3

A company is evaluating the ROI of deploying a GenAI code review assistant. They want to measure productivity gains. Which metric is MOST directly tied to developer efficiency?

Accepted Answer

Time saved per code review. Time saved per code review (Option B) is the most direct measure of developer efficiency because it quantifies the reduction in manual review effort, which is the primary benefit of a GenAI code review assistant. Unlike indirect metrics, this directly captures the core value proposition of automating or accelerating the review process, leading to faster development cycles and reduced context switching.

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Reduction in post-release bug count

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Developer satisfaction score

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Number of lines of code reviewed

Question 4

A company is deploying a GenAI contract analysis system that processes confidential legal documents. They need to ensure that the model does not retain or train on customer data. Which configuration is REQUIRED?

Accepted Answer

Opt out of model logging and data retention in the API settings. Option C is correct because the primary requirement is to prevent the GenAI model from retaining or training on confidential legal documents. Most enterprise GenAI APIs (e.g., OpenAI, Azure OpenAI) provide a data privacy setting that allows customers to opt out of model logging and data retention, ensuring that prompts and responses are not stored or used for model improvement. This configuration directly addresses the compliance need for data confidentiality in contract analysis.

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Select a model with a context window large enough to hold the entire contract

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Use a public model endpoint with data encryption in transit

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Use a smaller model to reduce the risk of data memorization

Question 5

A sales team wants to use Gemini for Google Workspace to draft emails in Gmail. Which feature should they enable to generate full email drafts from a brief prompt?

Accepted Answer

Help me write. Option C is correct because 'Help me write' is the specific Gemini for Google Workspace feature designed to generate full email drafts from a brief prompt. It leverages the underlying large language model to compose complete, context-aware email content, unlike features that only suggest short phrases or replies.

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Translate

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Smart Reply

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Smart Compose

Question 6

A company wants to add a GenAI-powered meeting summarization feature to their internal app. They have existing meeting transcript data in Cloud Storage. Which Google Cloud service should they use to build the summarization pipeline with minimal infrastructure management?

Accepted Answer

Vertex AI Agent Builder. Vertex AI Agent Builder is the correct choice because it provides a managed, serverless platform specifically designed for building GenAI-powered applications like meeting summarization. It integrates directly with existing data sources (e.g., Cloud Storage transcripts), offers pre-built models for summarization, and handles infrastructure scaling, security, and monitoring automatically, minimizing operational overhead.

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Cloud Functions

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Compute Engine with custom containers

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Google Chat API

Question 7

A company is running a GenAI proof-of-concept (PoC) for internal document Q&A. The PoC shows high latency and cost. The team suspects they are using an unnecessarily large model for the task. What is the BEST action to optimize?

Accepted Answer

Switch to a smaller model from Vertex AI Model Garden. Switching to a smaller model from Vertex AI Model Garden directly addresses the root cause of high latency and cost: an unnecessarily large model. Smaller models have fewer parameters, requiring less compute per inference, which reduces both response time and operational expense while often being sufficient for domain-specific tasks like internal document Q&A.

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Disable grounding and rely solely on the model's internal knowledge

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Increase the batch size for requests

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Fine-tune the existing model on the company's documents

Question 8

A company wants to generate images for slide decks using Gemini in Google Slides. Which Gemini feature in Google Slides should they use?

Accepted Answer

Image generation from text prompts. Gemini in Slides can generate images from text prompts, allowing users to create visuals directly within the presentation.

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Speaker notes generation

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Slide layout optimization

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Summation of slide content

Question 9

A company is building a GenAI chatbot that needs to answer questions using real-time data from their CRM and inventory systems. They want to ensure the model can access external data on demand. Which approach should they use?

Accepted Answer

Use Vertex AI Extensions to connect to CRM and inventory APIs. Option C is correct because Vertex AI Extensions allow the GenAI chatbot to connect to external APIs (like CRM and inventory systems) in real time, enabling on-demand data retrieval without retraining the model. This approach uses a retrieval-augmented generation (RAG) pattern where the model queries live data sources via API calls, ensuring responses are based on current information rather than static snapshots.

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Fine-tune the model on historical CRM and inventory data

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Prompt the model to guess the data based on general knowledge

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Export CRM data to BigQuery and use that static snapshot

Question 10

A company is adopting GenAI for internal knowledge base search. They want to measure the success of the rollout. Which metric is LEAST relevant for evaluating adoption?

Accepted Answer

Number of queries answered per day. Option D is the least relevant metric because it measures raw system throughput rather than adoption or business value. A high number of queries answered per day could indicate system abuse, redundant searches, or poor initial retrieval quality, not successful adoption. Adoption metrics should focus on user engagement, satisfaction, and efficiency gains, not just volume.

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Employee satisfaction with search results

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Reduction in time to find information

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Percentage of employees using the tool weekly

Question 11

A company wants to deploy a GenAI system for contract analysis that must produce structured output (e.g., JSON) for downstream processing. They need to ensure the output format is consistent. Which prompt engineering technique is MOST effective?

Accepted Answer

Provide a few-shot example with the desired JSON format. Option B is correct because providing a few-shot example with the desired JSON format explicitly demonstrates the exact structure, keys, and nesting required, which guides the model to generate consistent structured output. This technique leverages in-context learning, where the model infers the output pattern from the examples, making it more reliable than vague instructions or post-processing fixes.

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Post-process the output with a regex to fix formatting

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Use a system prompt that says 'output JSON'

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Use a larger model with better formatting capabilities

Question 12

A company is deciding between building a custom fine-tuned model vs. using a pre-built API for a document summarization task. The documents contain domain-specific jargon. Which factor STRONGLY favors using a pre-built API with prompt engineering?

Accepted Answer

The requirement for low initial development cost and fast time-to-market. Option D is correct because using a pre-built API with prompt engineering eliminates the need for expensive model training infrastructure and specialized ML expertise, enabling rapid deployment at low initial cost. For a document summarization task, prompt engineering can leverage the API's existing capabilities without custom fine-tuning, making it ideal when speed and budget are primary constraints.

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The need to handle highly specialized industry terminology

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The need for the model to learn a unique writing style from past summaries

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The need to keep all data on-premises for security compliance

Question 13

A company wants to deploy a GenAI code review assistant that integrates into their existing Git workflow. They want to use a managed Google Cloud service to minimize operational overhead. Which TWO services should they consider? (Choose two.)

Accepted Answer

Cloud Build. Cloud Build is correct because it is a managed CI/CD platform that integrates natively with Git repositories, enabling automated code review workflows triggered by pull requests or commits. It can invoke a GenAI code review service as a build step, minimizing operational overhead by eliminating the need to manage infrastructure.

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Compute Engine

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Cloud Spanner

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Cloud Run

Question 14

A company is deploying a GenAI system that generates product descriptions. During A/B testing, the new system shows a 20% increase in click-through rate (CTR) but a 15% increase in average cost per query due to the model size. The team wants to optimize cost without sacrificing the CTR gain. Which THREE actions should they take? (Choose three.)

Accepted Answer

Batch similar requests to reduce per-request overhead. Option A is correct because batching similar requests reduces the per-request overhead by combining multiple inference calls into a single batch, which amortizes the fixed costs (e.g., model loading, token processing) across more outputs. This directly lowers the average cost per query while preserving the model architecture and CTR gains, as the model's output quality remains unchanged.

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Use a larger model with higher accuracy to further increase CTR

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Increase the number of few-shot examples in the prompt

Question 15

A company is planning an iterative rollout of a GenAI assistant for sales teams. They want to maximize adoption and minimize disruption. Which TWO change management practices should they prioritize? (Choose two.)

Accepted Answer

Provide hands-on training sessions and documentation. Option A is correct because hands-on training and documentation directly address the learning curve and anxiety associated with adopting a GenAI assistant, ensuring users understand how to interact with the model effectively (e.g., prompt engineering basics, interpreting outputs) and reducing friction. This practice aligns with the iterative rollout strategy by providing staged support, which maximizes adoption through competence and confidence rather than coercion.

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Disable the legacy system immediately after GenAI launch

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Roll out to all employees simultaneously to create urgency

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Set a mandatory usage target for each sales rep