Generative AI Leader Google Cloud's Generative AI Offerings Practice Test 12 — 15 Questions

Question 1

An e-commerce company wants to add a conversational shopping assistant to its mobile app. The assistant must answer product questions using the company's catalog, call a backend API to check live inventory, and escalate to a human agent when the customer requests it. Which Google Cloud offering is designed for this?

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

Accepted Answer

Vertex AI Agent Builder. Vertex AI Agent Builder provides the components to build conversational agents with grounding in enterprise data, function calling for backend APIs, and escalation paths to human agents. It directly supports catalog-grounded answers, live inventory checks, and handoff, matching the scenario. The other services are model catalogs, batch pipelines, or productivity tools that do not deliver agent orchestration for a mobile assistant.

Answer

Gemini for Google Workspace

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Vertex AI Pipelines

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Vertex AI Model Garden

Question 2

A hospital wants to build a generative AI assistant that answers clinician questions using the hospital's own clinical guidelines. The guidelines change frequently, and the hospital wants the assistant to cite the exact guideline section used. Which approach best meets these requirements while minimizing model retraining?

Accepted Answer

Ground the assistant on the guidelines using retrieval-augmented generation with Vertex AI Search. Grounding with retrieval-augmented generation separates knowledge from the model: guideline passages are indexed and retrieved per query, so updates mean re-indexing rather than retraining, and responses can cite the retrieved section. Fine-tuning forces retraining on every change and gives no citations, temperature affects randomness not accuracy, and stuffing all guidelines into the prompt hits context limits without reliable attribution.

Answer

Expand the model's context window and paste all guidelines into every prompt

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Fine-tune the Gemini model every time the clinical guidelines are updated

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Increase the model's temperature so it recalls guideline details more accurately

Question 3

A media company wants to automatically generate concise summaries of news articles using a generative AI model. They need a fully managed service that provides access to Google's foundation models without requiring machine learning expertise. Which Google Cloud offering should they choose?

Accepted Answer

Generative AI on Vertex AI (Gemini models). Generative AI on Vertex AI offers Gemini models as a fully managed service, enabling users to generate summaries via simple API calls without ML expertise. It provides pre-trained models that excel at summarization tasks. The other options are either model catalogs requiring configuration, custom ML training services, or specialized translation tools that do not perform summarization.

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Vertex AI Model Garden

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AutoML Natural Language

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Cloud Translation API

Question 4

A research team wants to fine-tune a Gemini model on Vertex AI using a dataset of proprietary scientific abstracts. They need to adjust the model's behavior with supervised fine-tuning while keeping the base model's general knowledge. Which Vertex AI capability should they use?

Accepted Answer

Supervised fine-tuning of Gemini on Vertex AI. Supervised fine-tuning on Vertex AI lets teams adapt Gemini models with their own labeled examples, adjusting behavior for specialized domains such as scientific literature. The tuned model retains the base model's broad knowledge while learning task-specific patterns. This is the managed capability designed for customizing Gemini, unlike vision, search, or orchestration services that serve different purposes.

Answer

Vertex AI Pipelines

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Vertex AI Vision

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Vertex AI Matching Engine

Question 5

A company is using Google Cloud's generative AI offerings to build a customer-facing application. They need to ensure that the AI-generated content complies with their brand guidelines and does not produce harmful or inappropriate responses. They also want to monitor and filter content in real-time. Which Google Cloud feature should they use?

Accepted Answer

Vertex AI safety filters and attributes. Vertex AI safety filters and attributes are the correct choice because they are specifically designed to detect and block harmful content in generative AI outputs. They can be configured to enforce brand-specific guidelines and are applied in real-time during model inference. The other options are security or data protection services that do not address content moderation for generative AI.

Answer

Cloud Armor

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Identity and Access Management (IAM)

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Cloud Data Loss Prevention (DLP)

Question 6

A financial analyst needs to quickly extract key figures and summarize insights from a 200-page earnings report PDF. They want to use a Google Cloud generative AI model that can process long documents and answer questions. Which Gemini model capability should they leverage?

Accepted Answer

Gemini's long context window. Gemini's long context window is designed to handle very large inputs, such as a 200-page PDF, in a single request. This allows the analyst to ask questions and extract insights without manual segmentation. Other features like function calling, multimodality, or grounding address different needs and do not solve the core challenge of processing a long document.

Answer

Gemini's multimodal input

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Gemini's function calling

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Gemini's grounding with Google Search

Question 7

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

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Vertex AI Pipelines

Answer

Vertex AI Feature Store

Question 8

A logistics company needs an assistant that answers driver questions about shipment status. The answers must reflect live data from an operational database and must include citations so dispatchers can verify each claim. The company wants a managed Google Cloud capability rather than custom retrieval code. Which capability should they use?

Accepted Answer

Grounding with Vertex AI Search data stores connected to the operational data, used through Vertex AI Agent Builder.. The requirements are live data plus citations, delivered through a managed capability. Grounding with Vertex AI Search data stores retrieves from indexed enterprise sources and returns citations, and Agent Builder provides the managed orchestration layer that surfaces them. Temperature changes, manual exports, and fine-tuning cannot supply live lookups or verifiable source references, so they fail the scenario regardless of model quality.

Answer

Increasing the Gemini model's temperature setting so responses draw more broadly on the model's internal knowledge.

Answer

Exporting the operational database nightly to a Cloud Storage bucket and pasting relevant rows into each prompt manually.

Answer

Fine-tuning the Gemini model on historical shipment records so it memorizes typical status patterns.

Question 9

A financial analytics firm is building a generative AI application that must analyze long earnings call transcripts and produce summaries. The transcripts often exceed 200,000 tokens, and the firm wants to minimize cost while maintaining high accuracy. They plan to use Gemini models on Vertex AI. Which approach should they take?

Accepted Answer

Use gemini-1.5-pro with its long context window and pass the entire transcript in a single request.. Gemini 1.5 Pro's extended context window allows the entire transcript to be processed in one request, preserving cross-references and reducing the need for complex chunking pipelines. Chunking loses context, gemini-1.0-pro lacks the required context length, and fine-tuning does not increase context capacity, so the long-context model is the most accurate and cost-effective choice.

Answer

Use gemini-1.0-pro and rely on its built-in automatic long-document summarization feature.

Answer

Split the transcript into 1,000-token chunks and summarize each chunk separately, then concatenate the summaries.

Answer

Fine-tune a smaller Gemini model on the firm's past transcripts and use it for summarization.

Question 10

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 11

A software vendor is building a product that must call a Gemini model through a stable, versioned API with enterprise controls such as VPC Service Controls, and must run on Google Cloud infrastructure. Which Google Cloud offering should the vendor use?

Accepted Answer

The Gemini API in Vertex AI. The Gemini API in Vertex AI is the enterprise path to Google's foundation models: it offers versioned endpoints, runs on Google Cloud, and integrates with IAM, VPC Service Controls, and audit logging. Workspace is an end-user assistant, AI Studio targets prototyping without enterprise governance, and Feature Store serves ML features rather than generative model inference.

Answer

Gemini for Google Workspace

Answer

Google AI Studio

Answer

Vertex AI Feature Store

Question 12

A bank is evaluating Google Cloud generative AI offerings to build an internal document-processing application. Leadership requires that the solution support grounding responses in the bank's own document repository and provide enterprise controls such as IAM-based access and audit logging. Which two Google Cloud offerings should the bank consider to meet these requirements? (Choose two.)

Accepted Answer

Vertex AI Search with enterprise data stores for grounding. Vertex AI Search with enterprise data stores and Vertex AI with Gemini grounding both let an organization ground model responses in its own documents while operating under Cloud IAM and audit logging. Those two offerings align with the bank's dual requirement of private-repository grounding and enterprise governance for a custom application, whereas prototyping tools and end-user assistant products do not.

Answer

Gemini for Google Workspace

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Google AI Studio with the Gemini API

Answer

Gemini Enterprise as a standalone assistant for analysts

Question 13

A media company is evaluating Google Cloud generative AI offerings to build a production application that summarizes long articles and generates headlines. They want to use Gemini models with enterprise controls and need to understand which capabilities are provided by Vertex AI. (Choose two.)

Accepted Answer

Access to Gemini models through a managed API with enterprise security and data governance controls.. Vertex AI delivers managed access to Gemini with enterprise security and governance, plus tuning and evaluation tools to align models with custom summarization criteria. These two capabilities directly support a production media application that needs controlled model access and measurable quality. The remaining options describe publishing outcomes or unrealistic automation that Vertex AI does not provide.

Answer

Automated generation of print-ready magazine layouts from article text.

Answer

Guaranteed placement of generated headlines on the publication's homepage.

Answer

Automatic translation of every generated article into all supported languages without configuration.

Question 14

A small marketing agency wants to add an AI assistant that summarizes campaign briefs and drafts social posts. The developers have limited cloud experience and prefer a fully managed, serverless way to call Google's Gemini models without provisioning infrastructure. Which approach best meets this requirement?

Accepted Answer

Use the Gemini API through Google AI Studio for rapid prototyping and API-key access. When the goal is simply to consume Gemini capabilities with minimal setup, the managed Gemini API accessed through Google AI Studio removes infrastructure concerns entirely. It supports summarization and drafting through straightforward API calls, letting a small team focus on prompts and application logic rather than serving stacks. Self-hosted or custom-trained alternatives introduce operational or data-science overhead the scenario does not require.

Answer

Deploy an open model on a Compute Engine VM with an attached GPU

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Create a Vertex AI custom training job for a bespoke summarization model

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Build a Cloud Run service that hosts a self-managed transformer container

Question 15

A game development studio wants to create dynamic non-player character (NPC) dialogues that adapt to player choices. They need a Google Cloud service that allows them to build conversational agents with custom logic and integrate with their game backend. Which service should they use?

Accepted Answer

Vertex AI Agent Builder. Vertex AI Agent Builder is designed for creating generative AI-powered conversational agents with custom logic and backend integration. It enables dynamic, context-aware dialogues that can adapt to player choices, making it the best fit for the game studio. Other options either lack generative capabilities or are not tailored for building conversational agents.

Answer

Vertex AI Prediction

Answer

Cloud Functions

Answer

Dialogflow CX