Generative AI Leader Practice Exam 62 — 20 Questions

Question 1

A media company uses a generative AI model to draft weekly newsletter articles from bullet points. The drafts are factually correct but read as terse and disjointed. Editors want smoother narrative flow without changing the underlying facts. Which technique should they apply first to improve the output?

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

Accepted Answer

Provide a few-shot prompt with two or three exemplar newsletter paragraphs demonstrating the desired narrative style.. The drafts are factually sound but stylistically weak, so the fastest effective fix is showing the model what good output looks like. Few-shot examples in the prompt communicate structure, tone, and transitions directly. Sampling changes and output-length limits affect randomness or size, not narrative quality, and fine-tuning is unnecessarily heavy for a style-only gap.

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Fine-tune the model on the company's entire archive of past newsletters.

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Increase the model's temperature setting so the model chooses less probable words.

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Reduce the maximum output tokens so the model must compress each article.

Question 2

A media company uses Gemini on Vertex AI to generate short news summaries from long articles. The summaries frequently miss key facts and sometimes include details not in the source. The team wants to improve factual grounding and coverage without retraining the model. (Choose two.)

Accepted Answer

Use retrieval-augmented generation (RAG) with Vertex AI Search to supply relevant source passages in the prompt.. Retrieval-augmented generation supplies authoritative source passages at inference time, and few-shot examples teach the model the desired grounded summarization pattern. Together they improve fact coverage and reduce unsupported claims without retraining. Temperature tuning, larger machines, and streaming do not add source knowledge or enforce faithfulness, so they fail to solve the stated problem.

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Enable streaming responses so the model can revise earlier sentences as it generates.

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Deploy the model on a larger machine type with more vCPUs to increase factual accuracy.

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Increase the model temperature to 1.5 so the model explores more of the source content.

Question 3

A retail company wants to build a generative AI application that creates personalized product descriptions. They need the model to stay current with weekly inventory changes and brand voice guidelines without retraining the model. Which approach should they use?

Accepted Answer

Use prompt engineering with few-shot examples that include current inventory and brand voice.. Prompt engineering allows the model to generate content based on context provided at inference time, so the application can incorporate the latest inventory and brand guidelines without modifying the model. This approach is flexible, cost-effective, and supports frequent updates, unlike fine-tuning or retraining, which are static and resource-intensive.

Answer

Deploy the model with a low temperature setting to ensure factual accuracy.

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Fine-tune a foundation model on the weekly inventory data and brand guidelines.

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Retrain the foundation model from scratch using the company's entire product catalog.

Question 4

A healthcare organization wants to build a generative AI application that can answer patient questions based on their own medical knowledge base. They need the model to cite sources and avoid generating unsupported information. Which Google Cloud feature should they use to ground the model's responses in their proprietary data?

Accepted Answer

Vertex AI Search and Conversation. Vertex AI Search and Conversation enables grounding of generative AI outputs by retrieving relevant documents from an enterprise knowledge base and providing citations. This reduces hallucinations and ensures answers are based on proprietary data. The other options are for model evaluation, pipeline orchestration, or feature management, none of which provide grounding for generative AI responses.

Answer

Vertex AI Pipelines

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Vertex AI Model Evaluation

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Vertex AI Feature Store

Question 5

A media company needs to generate short video clips from text prompts for a marketing campaign. They want to use a Google Cloud service that can create videos up to 8 seconds long, 720p resolution, and can be accessed via Vertex AI. Which Google Cloud generative AI offering should they use?

Accepted Answer

Veo on Vertex AI. Veo on Vertex AI is specifically built for video generation from text, supporting up to 8-second 720p clips, which aligns with the media company's need for short marketing videos. Other models like Imagen, Gemini, or Chirp serve different modalities and cannot produce video outputs.

Answer

Imagen on Vertex AI

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Gemini on Vertex AI

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Chirp on Vertex AI

Question 6

A marketing team wants to generate product descriptions from a short bullet list of features. They need the model to produce creative, varied phrasing rather than a single deterministic output, while keeping the text grammatically correct. Which Vertex AI generative AI parameter should they adjust to introduce randomness into the model's word choices?

Accepted Answer

Temperature. Temperature is the sampling parameter that scales the model's predicted token distribution, making outputs more random and creative when increased. Marketing copy benefits from this controlled variability because it produces distinct phrasings from the same feature list. Other parameters such as Top-K, Top-P, and maximum output tokens influence candidate selection or length but do not by themselves introduce the randomness the team wants.

Answer

Maximum output tokens

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Top-P

Answer

Top-K

Question 7

A financial analyst uses a generative AI model to answer questions about recent market trends. They notice that the model sometimes provides outdated information. They want to ensure the model's responses are based on the most current data. Which approach should they take?

Accepted Answer

Implement retrieval-augmented generation (RAG) to fetch the latest market data from a live source.. Retrieval-augmented generation (RAG) allows the model to access and incorporate real-time data from external sources. This ensures responses are based on the most current information, which is critical for market trends. Other options like fine-tuning or increasing temperature do not provide up-to-date data.

Answer

Increase the model's temperature to encourage more diverse answers.

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Fine-tune the model on a dataset of historical market reports.

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Use a larger model with more parameters to improve knowledge retention.

Question 8

An enterprise wants to use a foundation model through Google Cloud but must ensure that their prompts and responses are not used to train the underlying model and that data is encrypted in transit and at rest. They also want to avoid managing infrastructure. Which approach best meets these requirements?

Accepted Answer

Use Vertex AI generative AI models through the Google Cloud console and API under the enterprise's own project and data-governance controls.. Vertex AI offers managed access to generative models with enterprise controls inside the customer's Google Cloud project. Google Cloud's terms specify that customer data for foundation models is not used to train those models, and encryption in transit and at rest is provided by default. This combination satisfies the governance and no-infrastructure-management requirements.

Answer

Use a third-party public chatbot website to test prompts and then copy the results into internal systems.

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Fine-tune a model on the enterprise's confidential prompts so the model learns the company's style.

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Download an open-source model and run it on a Compute Engine VM that the team patches and scales manually.

Question 9

A logistics company wants to build a generative AI application that answers questions over thousands of internal policy PDFs stored in Cloud Storage. They need Google Cloud to handle document ingestion, chunking, indexing, and retrieval for grounding, while they focus only on the application logic. Which Google Cloud offering should they use?

Accepted Answer

Vertex AI Search. Vertex AI Search is purpose-built for ingesting enterprise documents, chunking and indexing them, and retrieving relevant passages to ground generative responses. It supports Cloud Storage as a data source and abstracts away the retrieval pipeline, so the logistics team can focus on application logic. The other services handle features, document parsing, or image analysis and do not deliver managed semantic retrieval for grounding.

Answer

Cloud Vision API

Answer

Vertex AI Feature Store

Answer

Document AI

Question 10

A financial services firm is designing a generative AI assistant for advisors. Compliance requires that every response be traceable to an approved source document and that no response rely on the model's pretrained knowledge alone. The team plans to use Gemini models on Vertex AI. Which design choice most directly satisfies the traceability requirement?

Accepted Answer

Enable grounding with citations against a curated corpus of approved documents. Grounding with citations retrieves passages from a curated approved corpus and returns references alongside generated text, giving auditors a clear link from each claim to its source. This prevents reliance on pretrained knowledge alone. Temperature, fine-tuning, and token limits influence style, behavior, or length but cannot establish provenance, so they do not satisfy the compliance mandate.

Answer

Fine-tune the model on historical advisor emails to match the firm's tone

Answer

Increase the model's temperature so responses vary and appear less templated

Answer

Set a low maximum output token limit to keep answers short and reviewable

Question 11

A financial analytics team needs a managed Google Cloud environment to ground Gemini responses in their proprietary market reports and to evaluate model outputs before releasing an internal research assistant. They want minimal infrastructure management and native integration with BigQuery. Which Google Cloud offering should they choose?

Accepted Answer

Vertex AI. Vertex AI is the managed Google Cloud platform that unifies model access, grounding with enterprise data, evaluation, and deployment. It natively connects to BigQuery and supports grounding Gemini in proprietary content, which directly addresses the need to ground responses and evaluate outputs before release. The other services are either prototyping surfaces, agent-building tools, or productivity assistants that do not deliver the required governed, end-to-end environment.

Answer

Gemini for Google Workspace

Answer

Vertex AI Agent Builder

Answer

Vertex AI Studio

Question 12

A healthcare company is using a generative AI model to draft patient education materials. The model sometimes generates content that includes specific medical advice, which could be harmful if inaccurate. The company wants to ensure that the model's outputs are safe and do not provide medical recommendations. Which technique should they implement?

Accepted Answer

Implement prompt engineering with explicit instructions to avoid providing medical advice.. Prompt engineering with explicit instructions is a direct and efficient way to constrain the model's output. By clearly stating that the model should not provide medical advice, the model is guided to generate only general educational content. This approach is flexible and can be updated as needed without retraining, making it suitable for the healthcare company's requirement to ensure safety.

Answer

Apply a safety filter that blocks any output containing medical terminology.

Answer

Reduce the model's temperature to make outputs more deterministic and less likely to include advice.

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Use reinforcement learning from human feedback (RLHF) to fine-tune the model to avoid giving advice.

Question 13

A marketing team uses a Gemini model to generate ad copy. They notice the outputs are repetitive and lack variety across multiple runs for the same prompt. They want more diverse creative options without sacrificing relevance. Which parameter adjustment should they make?

Accepted Answer

Increase the temperature to allow more varied token sampling.. Increasing temperature broadens token sampling, yielding more varied creative outputs while still following the prompt. Lower temperature, top-k of 1, and reduced max output tokens all make outputs more deterministic or shorter, not more diverse. Temperature is the primary control for balancing creativity and coherence in ad copy generation.

Answer

Set top-k to 1 so the model always picks the single most likely token.

Answer

Decrease the temperature to reduce repetition.

Answer

Reduce the max output tokens to force the model to vary its wording.

Question 14

A financial services company wants to build a generative AI application that can answer questions based on their internal documents, which are constantly updated. They need the model to cite sources and avoid hallucination. Which Google Cloud service should they use?

Accepted Answer

Vertex AI Search. Vertex AI Search provides enterprise-grade search and RAG capabilities, enabling applications to retrieve relevant passages from internal documents and generate answers with citations. It handles dynamic document updates, reducing hallucination by grounding responses in the source material.

Answer

Vertex AI Model Garden

Answer

Vertex AI Pipelines

Answer

Vertex AI Feature Store

Question 15

A company wants to use a generative AI model to create product descriptions from a list of features. They need the model to consistently follow a specific format: a headline, followed by three bullet points, and a closing sentence. Which technique should they use to guide the model's output structure?

Accepted Answer

Few-shot prompting with examples of the desired format.. Few-shot prompting is ideal for teaching the model a specific output format by providing examples. It allows the model to infer the pattern and apply it consistently. Other options like increasing temperature or changing model size do not directly address the need for structured output.

Answer

Increasing the temperature to allow more creative freedom.

Answer

Using a larger model with more parameters.

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Setting top-k to 1 to force deterministic output.

Question 16

A marketing team is using a generative AI model on Vertex AI to create ad copy for a new product launch. The initial outputs are generic and do not reflect the brand's tone. The team wants to quickly improve the outputs without retraining the model. They have a set of example ad copies that exemplify the desired tone. Which technique should they use?

Accepted Answer

Use few-shot prompting by including the example ad copies in the prompt.. Few-shot prompting is ideal when you have example outputs that demonstrate the desired style or format. By including these examples in the prompt, the model can infer the pattern and generate new content that aligns with the brand's tone. This approach requires no training and can be implemented immediately, making it the most efficient solution for the marketing team.

Answer

Increase the model's temperature setting to encourage more creative outputs.

Answer

Deploy the model to a new endpoint with higher throughput.

Answer

Fine-tune the model on the example ad copies.

Question 17

A developer is using a generative AI model to summarize long articles. They want to ensure the summaries are concise and do not exceed a certain length. Which parameter should they adjust to control the maximum length of the generated summary?

Accepted Answer

Max output tokens. The max output tokens parameter directly limits the number of tokens the model can generate. By setting it to a desired value, the developer can prevent summaries from becoming too long. Other parameters like temperature, top-p, and top-k influence randomness and diversity but do not control output length.

Answer

Temperature

Answer

Top-p

Answer

Top-k

Question 18

A healthcare analytics team is evaluating a generative AI model for summarizing clinical notes. They observe that summaries are fluent but occasionally invent details not present in the source notes. They want a practical mitigation that reduces fabricated content without retraining the model. Which approach should they apply first?

Accepted Answer

Lower the temperature and instruct the model to answer only from the provided note text. Reducing temperature and explicitly instructing the model to rely only on the provided note constrains generation toward the source content, cutting down fabricated details without retraining. It is fast, inexpensive, and directly targets the failure mode. Longer output, higher randomness, or unrelated fine-tuning do not improve fidelity and can make hallucination worse.

Answer

Raise the temperature to encourage the model to explore alternative phrasings of the notes

Answer

Fine-tune the model on a large corpus of unrelated medical literature to improve fluency

Answer

Increase the maximum output token limit so the model has more room to explain itself

Question 19

A startup wants to quickly add a conversational AI assistant to its mobile app without managing any infrastructure. They need a managed API that provides access to Gemini models for chat and content generation. Which Google Cloud offering should they use?

Accepted Answer

Gemini API in Google AI Studio. The Gemini API in Google AI Studio offers a managed, serverless way to integrate Gemini models into applications, eliminating infrastructure management. It is designed for developers who need quick access to conversational AI capabilities. The other options are either prototyping tools, model catalogs, or specialized agent-building platforms that require more setup and do not provide the same level of managed API access.

Answer

Vertex AI Model Garden

Answer

Vertex AI Studio

Answer

Dialogflow CX

Question 20

A financial analyst wants to quickly generate a first draft of a market commentary from a few key statistics using a Google Cloud generative AI model. They have no coding experience and want to see the model's response immediately while adjusting the prompt. What should they do first?

Accepted Answer

Open Vertex AI Studio, select a generative model, and enter the statistics and instructions in the prompt box.. Vertex AI Studio is the Google Cloud tool for interactive, code-free prompt experimentation. An analyst can choose a model, type statistics and instructions, and read the generated commentary right away. This matches the need for a quick first draft and immediate prompt adjustments better than any code-based or pipeline-based approach.

Answer

Build a pipeline in Vertex AI Pipelines to preprocess the statistics before generation.

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Create a custom notebook in Colab Enterprise and install the necessary libraries before prompting.

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Write a Python script using the Vertex AI SDK to call the model and print the output.