Generative AI Leader Practice Test 21 — 15 Questions

Question 1

A company is piloting a GenAI feature that summarizes customer support tickets. They want to measure the impact on agent productivity before rolling out to all teams. Which approach BEST evaluates the pilot?

Choose an answer to begin — your selection is scored in the full session.

Accepted Answer

Run an A/B test where half the agents use the tool and half do not, then compare average handling time. A/B testing with a control group provides a rigorous comparison of productivity metrics. The other options lack a baseline for comparison.

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Survey agents on their perception of productivity after using the tool

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Compare the cost of the API before and after deployment

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Measure the number of summaries generated per day

Question 2

A company is deploying a generative AI model for customer support. They want to reduce hallucinations while maintaining fluency. They have a large dataset of previous support conversations. Which strategy should they prioritize?

Accepted Answer

Implement retrieval-augmented generation (RAG) using the conversation dataset as a knowledge base.. Retrieval-augmented generation (RAG) directly addresses hallucinations by grounding the model's responses in factual, retrieved data from the conversation dataset. This approach allows the model to generate fluent, contextually relevant answers while reducing the risk of inventing information, as it retrieves actual support interactions as evidence before generating a response.

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Increase the beam search width to 10.

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Fine-tune the model on the conversation dataset.

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Set the temperature to 0.1.

Question 3

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.

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Batch all email drafting requests and run them every hour

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

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

Question 4

An enterprise is evaluating whether to build a custom fine-tuned model or use a pre-built API for code generation. Which three factors should they consider in the build vs. buy decision? (Choose THREE)

Accepted Answer

Data privacy and security requirements. Option A (Data privacy and security requirements) is correct because a build decision is often driven by the need to keep proprietary source code and sensitive data within the organization's own environment, whereas a pre-built API may send prompts and code to a third-party provider, raising data-residency, retention, and compliance concerns. Option D (Level of customization needed for the organization's coding standards) is correct because fine-tuning a custom model allows tailoring to internal style guides, frameworks, and domain-specific APIs, while a generic pre-built API may not align with those standards. Option E (Availability of pre-built models for the specific programming language) is correct because if a suitable pre-built model already supports the target language well, buying is more efficient, whereas a lack of adequate pre-built support pushes the decision toward building. Option B (Number of developer seats) is not a primary build-vs-buy factor because seat count mainly affects licensing cost and scaling, not the fundamental capability or control trade-off. Option C (Compatibility with on-premises legacy systems) is not a core factor because integration can typically be handled through APIs or gateways regardless of whether the model is built or bought.

Answer

Number of developer seats in the organization

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Compatibility with on-premises legacy systems

Question 5

A media company wants its editorial staff to draft blog posts inside a web-based workspace where Gemini can summarize uploaded research PDFs, generate outlines, and cite files from the team's shared drive, all without writing code or managing any Google Cloud infrastructure. Which Google Cloud generative AI offering best fits this requirement?

Accepted Answer

Gemini for Google Workspace. Gemini for Google Workspace is the offering designed to bring Gemini into the productivity apps employees already use, with enterprise-grade privacy protections and grounding in content the user can access. Because the scenario calls for drafting and summarizing inside a workspace with no coding or infrastructure, the embedded Workspace experience is the natural fit rather than developer consoles or agent platforms.

Answer

Google Cloud Vertex AI Agent Builder with a custom tool

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Gemini Enterprise with a connected data store

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Vertex AI Studio with a tuned Gemini model endpoint

Question 6

A marketing company wants to fine-tune a generative AI model to adopt a specific brand voice. Which tuning method is most appropriate?

Accepted Answer

Supervised fine-tuning with labeled examples of the brand voice. Supervised fine-tuning (SFT) is the most appropriate method because it directly trains the model on a curated dataset of input-output pairs that exemplify the desired brand voice. By adjusting the model's weights through backpropagation on labeled examples, the model learns to mimic the specific tone, vocabulary, and stylistic patterns of the brand, making it the most precise approach for adopting a fixed voice.

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RLHF with general user feedback

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Grounding with external knowledge base

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Prompt engineering with system instructions

Question 7

A large insurance company is using generative AI to automate claims processing. They have deployed a custom fine-tuned model on Vertex AI that reads claim documents and extracts key information. Recently, they noticed that the model’s performance degrades over time for certain claim types, leading to incorrect payouts. The team needs to detect and address model drift with minimal manual intervention. They have a data pipeline that captures incoming claims and user feedback on predictions. Which approach should they take?

Accepted Answer

Set up continuous evaluation with automated retraining pipelines based on performance metrics. It establishes a closed-loop MLOps pipeline where continuous evaluation of performance metrics (e.g., precision, recall, or F1-score on streaming data) triggers automated retraining when drift is detected. This minimizes manual intervention while ensuring the model adapts to distribution shifts in claim types, which is critical for maintaining accurate payouts in production.

Answer

Implement a human review process for all claims the model processes

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Switch to a simpler rule-based system to avoid drift

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Manually retrain the model monthly using a snapshot of recent claims

Question 8

A media company uses generative AI to produce personalized news summaries for subscribers. They notice that the summaries sometimes contain factual inaccuracies, leading to customer complaints. The team needs to improve accuracy without slowing down the generation speed. They are using a pre-trained model via Vertex AI. What strategy should they implement?

Accepted Answer

Implement retrieval-augmented generation (RAG) with a trusted knowledge base. Retrieval-augmented generation (RAG) grounds the model's output in a trusted, external knowledge base, allowing it to retrieve verified facts in real time without retraining. This directly addresses factual inaccuracies while maintaining generation speed, as the pre-trained model remains unchanged and only the retrieval step is added. RAG avoids the latency of human review and the computational cost of fine-tuning or switching models.

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Switch to a larger, more accurate foundation model

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Fine-tune the model on a dataset of verified news articles

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Add a human-in-the-loop review for every summary

Question 9

A product manager wants to generate meeting summaries automatically using Gemini for Google Workspace. They need summaries to be sent to all participants immediately after the meeting ends. Which Gemini feature should they use?

Accepted Answer

Gemini in Google Meet - Take notes and summaries. Gemini in Google Meet can automatically generate meeting summaries and share them after the meeting. The other options are for different Workspace apps or manual use.

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Gemini in Google Docs - Help me write

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Gemini in Gmail - Smart Compose

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Vertex AI Agent Builder with a meeting transcription model

Question 10

A developer wants to improve the factual accuracy of the model's summaries. Based on the exhibit, what should they do?

Accepted Answer

Configure grounding with a knowledge base.. Grounding with a knowledge base is the correct approach because it anchors the model's output to a trusted, external source of facts, directly improving factual accuracy without modifying the model's weights. This technique uses retrieval-augmented generation (RAG) to fetch relevant documents from the knowledge base and inject them into the prompt context, ensuring the summary is based on verified information rather than relying solely on the model's parametric memory.

Answer

Enable the support engine.

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Increase the model's context window.

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Re-train the model with a dataset of facts.

Question 11

A company fine-tunes a model using Vertex AI and notices the model's performance drops on the original training task (e.g., language understanding) after fine-tuning for a new task (e.g., summarization). What could be the cause?

Accepted Answer

Catastrophic forgetting. Catastrophic forgetting occurs when a neural network loses previously learned knowledge upon being fine-tuned on a new task. In this scenario, fine-tuning the model for summarization overwrites the weights responsible for language understanding, causing performance degradation on the original task. This is a well-known limitation of sequential fine-tuning in deep learning.

Answer

Data leakage

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Model quantization

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Underfitting

Question 12

A product team at a retailer is using Vertex AI Studio to build a Gemini-powered assistant that answers questions about their internal return policy. Early tests show the model invents policy details such as a 45-day return window, even though the official policy allows only 30 days. The team wants the assistant to answer strictly from a set of approved policy PDFs stored in a Cloud Storage bucket, and they want to avoid retraining the model. Which technique should they use?

Accepted Answer

Retrieval-augmented generation (RAG) with a Vertex AI Search data store grounded on the approved policy documents.. Grounding with retrieval-augmented generation lets the assistant fetch the relevant passages from the approved policy PDFs and condition its answer on that retrieved text, so the 30-day window is stated correctly and the response can cite the source. It requires no retraining, updates automatically when documents change, and directly addresses hallucinated policy details.

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Adding a system instruction telling the model to be accurate and to never hallucinate policy details.

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Supervised fine-tuning of the Gemini model on a labeled dataset of correct policy answers.

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Lowering the model's temperature to 0 and increasing the top-k value in the generation configuration.

Question 13

A company is choosing between Google's Gemini API and an open-source model. Which factor is most important for a business with limited ML expertise?

Accepted Answer

Ease of integration and availability of support. For a business with limited ML expertise, ease of integration and availability of support are paramount because they reduce the need for in-house machine learning engineering talent. Google's Gemini API offers managed infrastructure, pre-built SDKs, and enterprise-grade support (e.g., SLA-backed uptime, dedicated account management), which directly lowers the barrier to entry and operational risk. In contrast, open-source models require significant expertise for deployment, scaling, and troubleshooting, making them unsuitable for teams without deep ML skills.

Answer

Model parameter count

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Cost per token

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Community size

Question 14

A fintech startup is building a generative AI application that generates personalized investment advice based on user profiles and market data. They are using Vertex AI Agent Builder to create an agent that retrieves information from a BigQuery table containing user data and from a real-time market data API. The agent needs to ensure that responses comply with financial regulations, meaning the model must not give specific stock recommendations unless the user explicitly requests them after disclaimers. The team has implemented grounding with both sources. During testing, the agent sometimes spontaneously suggests buying a particular stock without being asked, which could lead to regulatory issues. The team wants to enforce strict control over the agent's behavior. What should the team do?

Accepted Answer

Implement a custom system instruction that explicitly prohibits unsolicited stock recommendations and requires a disclaimer before any advice. System instructions in Vertex AI Agent Builder allow you to define strict behavioral rules that the agent must follow, such as prohibiting unsolicited stock recommendations and requiring a disclaimer before any advice. This directly addresses the regulatory compliance issue by enforcing a policy at the agent's instruction layer, which overrides any learned or grounded behavior. Unlike other options, this approach provides explicit, enforceable control without altering data sources or model training.

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Increase the safety filter sensitivity to block any financial recommendations

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Add more historical data to the BigQuery table to improve grounding accuracy

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Fine-tune the model on a dataset of compliant conversations

Question 15

A large enterprise is evaluating gen AI for internal knowledge management. They need to ensure accuracy and reduce hallucinations. Which strategy is most effective?

Accepted Answer

Use Retrieval-Augmented Generation (RAG). Retrieval-Augmented Generation (RAG) is the most effective strategy because it grounds the model's responses in an external, authoritative knowledge base, retrieving relevant documents at inference time to provide factual context. This directly reduces hallucinations by ensuring the generated output is based on retrieved evidence rather than relying solely on the model's parametric memory, which is critical for enterprise knowledge management where accuracy is paramount.

Answer

Fine-tune a model on domain-specific data

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Increase model temperature

Answer

Use a larger model without customization