Generative AI Leader Practice Exam 65 — 20 Questions

Question 1

A marketing team is using an LLM on Vertex AI to generate product descriptions. They want to consistently control the creativity and randomness of the output. Which parameter should they adjust?

Accepted Answer

Temperature. Temperature is the primary parameter for controlling the randomness and creativity of generative AI output. Lower temperatures yield more predictable, focused responses, while higher temperatures yield more diverse and creative ones. For a marketing team seeking consistent control over creativity, temperature is the correct choice.

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Top-P

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Max output tokens

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Top-K

Question 2

A team is designing prompts for a generative AI application that summarizes long legal contracts. They want outputs that are accurate, consistently formatted, and safe from leaking instructions. Which two prompt engineering practices should they apply? (Choose two.)

Accepted Answer

Instruct the model to ignore any instructions embedded inside the contract text.. Giving a clear task plus a formatting example improves output consistency, and explicitly telling the model to ignore instructions inside the contract text reduces prompt-injection risk. These two practices together address accuracy, formatting stability, and safety when summarizing long legal documents, which were the team's stated goals.

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Set temperature to the maximum value to encourage more varied legal wording.

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Insert the entire contract text without delimiters and ask the model to figure out the important parts.

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Ask the model to reveal its system instructions so the team can verify them.

Question 3

A company is deploying a generative AI model on Vertex AI for real-time chat. They observe that the model sometimes generates toxic or biased responses. They want to implement a safety mechanism to filter out such content before it reaches users. Which Google Cloud feature should they use?

Accepted Answer

Vertex AI Safety Filters. Vertex AI Safety Filters are specifically built to detect and block harmful content in generative AI applications. They can be applied to both input and output, providing real-time protection. Other services like Cloud Armor or Sensitive Data Protection serve different purposes and cannot effectively filter toxic or biased language.

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Sensitive Data Protection

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Vertex AI Model Monitoring

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Cloud Armor

Question 4

A marketing agency wants to quickly generate original images for social media campaigns without deep technical expertise. They need a fully managed Google Cloud service that provides an API for text-to-image generation. Which service should they use?

Accepted Answer

Vertex AI Imagen. Vertex AI Imagen is Google Cloud's managed text-to-image generation service, offering an API that turns text prompts into images. It is designed for creative use cases and requires no ML expertise, making it the right choice. The other services are for vision analysis or conversational AI and do not generate images.

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Cloud Vision API

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Dialogflow CX

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Vertex AI Vision

Question 5

A global software company uses a generative AI model to produce localized release notes. Outputs are accurate but inconsistently formatted: some use tables, some bullet lists, and some paragraphs, which breaks the publishing pipeline. The team wants stable, machine-parseable formatting across all runs. Which technique should they prioritize?

Accepted Answer

Define a strict output schema, such as a JSON or Markdown template, and require the model to conform to it.. Inconsistent formatting is a specification gap. Declaring a strict output schema, such as a fixed JSON structure or Markdown template, forces every generation into the same shape and makes the output machine-parseable. Temperature averaging, reasoning prompts, and fine-tuning influence variability, reasoning, or style bias but do not guarantee structural conformity.

Answer

Set the model's temperature to a moderate value and rely on repeated runs to average out formatting differences.

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Instruct the model to think step by step about the release content before formatting the output.

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Fine-tune the model on a dataset of previously published release notes to teach consistent formatting.

Question 6

A retail company uses a generative AI model to create personalized product recommendations. The model sometimes generates recommendations that include products the company does not sell. Which technique should be used to prevent the model from generating non-existent products?

Accepted Answer

Implement a post-processing filter that checks generated product names against the company's inventory database.. A post-processing filter that cross-references generated product names with the company's inventory database ensures that only real products are recommended. This method is direct, reliable, and does not rely on the model's internal knowledge, effectively preventing hallucinations of non-existent products.

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Increase the model's temperature to allow more creative recommendations.

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Fine-tune the model on a dataset of customer reviews.

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Use a larger context window to include the entire product catalog in the prompt.

Question 7

A media company is using Vertex AI to build a generative AI application that creates personalized news summaries. They want to ensure the model's outputs are factually grounded in their curated article database and that the application can scale to thousands of concurrent users. Which two Google Cloud services should they use to achieve these goals? (Choose two.)

Accepted Answer

Vertex AI Search. Vertex AI Search grounds the generative summaries in the curated article database, ensuring factual accuracy. Vertex AI Endpoints provides the scalable online serving infrastructure needed to handle thousands of concurrent users. Together, they address both grounding and scalability, while the other services focus on data processing, content delivery, or pipeline orchestration.

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Cloud Dataflow

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Cloud CDN

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Vertex AI Pipelines

Question 8

A retail chain's leadership wants to understand how generative AI could improve its customer service operations but is unsure where to start. They ask a cloud consultant to identify candidate use cases, estimate potential value, and flag which ones are realistically achievable with current technology. Which activity should the consultant perform first?

Accepted Answer

Run a generative AI use case discovery workshop with business stakeholders to inventory candidate scenarios and prioritize them by value and feasibility.. The consultant should begin with structured use case discovery, because the organization needs an inventory of candidate scenarios ranked by business value and technical feasibility before committing to any build or procurement. This produces the prioritized shortlist that later pilots, model choices, and investment decisions can be based on, aligning generative AI work with measurable business outcomes.

Answer

Fine-tune a foundation model on the retailer's historical customer service transcripts and measure its response quality.

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Deploy a general-purpose chatbot on the public website and monitor engagement metrics for one quarter.

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Migrate the retailer's contact center platform to a new SaaS vendor that advertises built-in generative AI features.

Question 9

A product team is designing a generative AI application to create personalized email campaigns. They want to ensure the model produces high-quality, relevant content while minimizing risks such as bias and harmful outputs. Which two practices should the team implement? (Choose two.)

Accepted Answer

Implement content filters and safety thresholds to block harmful or inappropriate outputs.. Regular fairness evaluations help detect and reduce bias across demographic groups, ensuring equitable and appropriate content. Content filters and safety thresholds act as a safeguard to block harmful or inappropriate outputs before they reach users. Together, these practices address both bias and harmful content, aligning with responsible AI development. Other options either increase randomness, risk overfitting, or remove necessary oversight.

Answer

Fine-tune the model on a small dataset of previously successful email campaigns without any validation.

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Disable all logging and monitoring to protect user privacy and reduce overhead.

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Set the model's temperature to the maximum value to encourage diverse and creative emails.

Question 10

A healthcare organization is using a generative AI model to summarize patient discharge instructions. They need to ensure the summaries are accurate and do not omit critical information. Which technique should they implement to reduce the risk of omissions?

Accepted Answer

Implement a retrieval-augmented generation (RAG) system that retrieves relevant sections of the discharge instructions and includes them in the prompt.. Retrieval-augmented generation (RAG) is the best choice because it grounds the model in the actual discharge instructions, reducing the chance of omissions. By retrieving and including relevant text in the prompt, the model can generate summaries that are comprehensive and accurate. Other techniques like fine-tuning or chain-of-thought do not directly ensure that all critical information from the source is captured.

Answer

Increase the model's token limit to allow longer summaries that can include more details.

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Fine-tune the model on a large dataset of medical summaries to improve its ability to capture key points.

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Use chain-of-thought prompting to encourage the model to reason step by step.

Question 11

A financial institution is using Vertex AI to generate personalized investment advice. They need to ensure that the model's responses are grounded in the latest regulatory documents and do not include outdated or fabricated information. Which feature should they implement to achieve this?

Accepted Answer

Grounding with Vertex AI Search. Grounding with Vertex AI Search enables the model to dynamically retrieve and cite information from a connected data store, such as a set of regulatory documents. This ensures that the generated advice is based on the most current and authoritative sources, reducing the risk of outdated or fabricated content. The other options do not provide real-time grounding and are either static or counterproductive for factual accuracy.

Answer

Fine-tuning the model on historical advisory emails

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Using a larger context window

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Increasing the model's temperature setting

Question 12

A small marketing agency wants its staff to draft blog posts, summarize meeting notes, and brainstorm campaign ideas using a conversational assistant. The agency has no cloud engineering team and prefers a ready-to-use product with enterprise-grade data protections rather than building anything. Which Google Cloud offering best matches this need?

Accepted Answer

Gemini for Google Workspace. Gemini for Google Workspace delivers generative AI assistance inside the productivity apps the agency already uses, letting staff draft, summarize, and brainstorm without any development work. The remaining offerings are developer platforms or infrastructure for building and serving models or applications, which conflict with the requirement for a ready-to-use product and the absence of an engineering team.

Answer

Vertex AI Model Garden

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Vertex AI Agent Builder

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Google Kubernetes Engine

Question 13

A small marketing agency wants to add a Google Cloud generative AI assistant that can summarize campaign documents stored in Google Drive and answer follow-up questions about them, with minimal development effort. Which Google Cloud offering should they choose?

Accepted Answer

Gemini for Google Workspace. Gemini for Google Workspace is the right choice because it brings generative AI into the productivity apps where the agency already stores and edits campaign documents. It can summarize Drive files and support conversational follow-up without custom engineering. The other services are building-block APIs or model catalogs that would require substantial integration work before delivering the same outcome.

Answer

Vertex AI Model Garden

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Cloud Vision API

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Google Cloud Speech-to-Text

Question 14

A retail company wants to build an internal tool that generates short product descriptions from bullet points. They have no machine learning engineers on staff and want to avoid managing any infrastructure. Which Google Cloud approach best fits their need?

Accepted Answer

Use the Gemini API in Vertex AI to generate descriptions from the bullet points with a prompt.. The Gemini API in Vertex AI is a managed service that lets teams generate text without building or operating ML infrastructure. By supplying bullet points in a prompt, the model produces product descriptions on demand. This aligns with the company's lack of ML engineers and desire to avoid infrastructure management, making it the most practical and direct solution.

Answer

Use BigQuery ML to create a linear regression model that predicts description length.

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Train a custom image generation model on Vertex AI using their product photos.

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Deploy an open-source LLM on a Compute Engine VM with a GPU and expose it via a REST API.

Question 15

A healthcare company is using a generative AI model to summarize patient records. They are concerned about the model generating incorrect medical information. They want to ensure that the summaries are grounded in the provided records and do not include hallucinations. Which technique should they implement?

Accepted Answer

Use retrieval-augmented generation (RAG) to fetch relevant patient data and include it in the prompt.. Retrieval-augmented generation (RAG) is specifically designed to ground model outputs in external knowledge sources. By retrieving relevant patient records and including them in the prompt, the model can generate summaries that are based on the actual data, significantly reducing the risk of hallucinations. This is critical in healthcare where accuracy is paramount.

Answer

Increase the temperature to allow the model to explore more possibilities.

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Reduce the max output tokens to limit the summary length.

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Fine-tune the model on a large corpus of medical textbooks.

Question 16

A retail marketing team wants to generate product descriptions in five languages. The team has no machine learning engineers and wants to avoid managing any infrastructure. Which Google Cloud option should the GenAI Leader recommend first?

Accepted Answer

Call the Gemini API on Vertex AI with prompts requesting the product description in each target language.. A managed foundation model API is the fastest route to multilingual generation for a team without ML engineers, because Google operates the model, scaling, and serving. The other choices require training pipelines, cluster operations, or an analytics-only pattern that does not generate marketing copy from product attributes.

Answer

Provision a Vertex AI training job with custom containers and train a multilingual model from scratch on product data.

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Create a Google Kubernetes Engine cluster with GPU node pools and self-host an open model behind an inference server.

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Build a BigQuery ML remote model that calls a public translation endpoint and post-process the output with SQL transformations.

Question 17

A retail company wants to build an internal assistant that answers employee questions using the company's HR policy documents. The documents change frequently, and the company does not want to retrain a model every time a policy is updated. Which approach best meets these requirements on Google Cloud?

Accepted Answer

Use retrieval-augmented generation (RAG) with a vector store of the HR documents. Retrieval-augmented generation separates knowledge from the model: documents are embedded and indexed, and relevant chunks are retrieved and passed as context for each query. Updating a policy means re-indexing that document, not retraining, which directly meets the frequent-change requirement. It also grounds responses in source material, improving accuracy and enabling citations, which is essential for an HR policy assistant.

Answer

Fine-tune a foundation model on the HR documents each time a policy changes

Answer

Increase the model's context window and paste all HR documents into every prompt

Answer

Deploy the model with a lower temperature and rely on its pretrained HR knowledge

Question 18

A financial services firm is deploying a generative AI model to answer customer queries about investment products. They want to ensure the model's responses are based on the most current and authoritative internal documents. Which technique should they implement?

Accepted Answer

Using retrieval-augmented generation (RAG) with a vector database. Retrieval-augmented generation (RAG) dynamically retrieves relevant documents from a vector database and provides them to the model as context, ensuring responses are grounded in the most current authoritative sources. This is ideal for the financial firm's need to answer queries based on up-to-date internal documents. Other options do not provide real-time grounding.

Answer

Deploying the model with a larger context window

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Fine-tuning the model on historical customer interactions

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Increasing the model's temperature parameter

Question 19

A media company uses a Gemini model to produce short news digests from long articles. Editors complain that digests vary in length and sometimes bury the key fact in the middle. The team wants a repeatable, machine-checkable structure for every digest. Which technique should they use?

Accepted Answer

Provide a response schema with defined fields such as headline, key fact, and supporting details, and request JSON output.. A response schema with JSON output turns the digest into a structured object with named fields, so the key fact has a fixed location and length is bounded by the schema. This is directly machine-checkable and repeatable, unlike prose instructions, sampling settings, or style-oriented fine-tuning.

Answer

Fine-tune the model on a corpus of previously published digests written by editors.

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Set the temperature to 0 and the topK to 1 to make output fully deterministic.

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Ask the model to be concise and to put the most important information first in the prompt.

Question 20

A marketing team wants to generate social media posts with Google Cloud's generative AI, but they have no machine learning experience. They need a no-code interface to experiment with prompts and tune settings like temperature. Which Google Cloud service should they use?

Accepted Answer

Vertex AI Studio. Vertex AI Studio is the correct choice because it offers a no-code environment specifically for generative AI experimentation. It allows users to write prompts, select foundation models, and adjust generation parameters, then see results instantly. This matches the marketing team's need to create social media posts without ML expertise.

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Document AI

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BigQuery ML

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Dialogflow CX