Generative AI Leader Google Cloud's Generative AI Offerings Practice Test 10 — 15 Questions

Question 1

A media company wants to produce short video clips from text prompts for social media campaigns. They need a Google Cloud service that can generate video from text and edit existing video clips, with enterprise-grade controls. Which Google Cloud offering should they choose?

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

Accepted Answer

Vertex AI Veo. Veo on Vertex AI is purpose-built for generative video, producing clips from text or image prompts and supporting video editing. It is managed within Vertex AI, so organizations get enterprise security, access control, and compliance features. Other models in Vertex AI handle images, text, or speech, but not video generation and editing in one service.

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Vertex AI Gemini

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Vertex AI Imagen

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Vertex AI Chirp

Question 2

A global media company wants to add generative AI capabilities to its existing applications, including text summarization, image generation, and code assistance. They plan to use Google Cloud services and need a managed solution that provides access to multiple foundation models through a single API, with enterprise-grade security and scalability. Which Google Cloud offering should they choose?

Accepted Answer

Vertex AI. Vertex AI is the correct choice because it is Google Cloud's comprehensive platform for generative AI, offering access to multiple foundation models through a single API. It supports text, image, and code generation, and includes enterprise features like security, scalability, and integration with other Google Cloud services. The other options are either specialized tools or subsets of Vertex AI that do not fulfill the need for a unified, multi-modal generative AI platform.

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Dialogflow CX

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Document AI

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AutoML

Question 3

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 They need to ensure that the model's responses are grounded in the latest regulatory documents and do not include outdated or fabricated information.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.

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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 4

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.

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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 5

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

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Vertex AI Agent Builder

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Vertex AI Studio

Question 6

A logistics company wants to build a generative AI application that answers questions using its internal policy documents stored in Cloud Storage. They want a managed, serverless way to index those documents and retrieve relevant passages for grounding responses, without managing their own vector database. Which Google Cloud service should they use?

Accepted Answer

Vertex AI Search. Vertex AI Search is a managed retrieval and search service that ingests documents from Cloud Storage and other repositories, indexes them, and returns relevant passages. It is serverless, so the logistics company avoids building and tuning a vector database. Integrating it with a generative model provides grounded answers that cite internal policies, improving accuracy and trust.

Answer

Vertex AI Feature Store

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Cloud SQL with pgvector

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BigQuery ML

Question 7

A small marketing analytics team wants to build a generative AI assistant that can answer questions about their proprietary campaign performance data. They have very limited machine learning engineering resources and want the fastest possible path to a working prototype on Google Cloud. Which approach should they take?

Accepted Answer

Use the Gemini models through Vertex AI with a managed RAG Engine backed by their campaign data stored in BigQuery.. The key requirement is speed to a working prototype with minimal ML engineering, and managed Gemini endpoints plus a managed RAG pipeline satisfy that without infrastructure ownership. The team supplies campaign data and prompts while Google Cloud handles retrieval, grounding, and serving. Options that require training from scratch, self-managing GPU clusters, or calling external APIs all add engineering burden or governance risk that the scenario rules out.

Answer

Deploy an open-source large language model on a self-managed Google Kubernetes Engine cluster with autoscaling GPU node pools.

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Train a custom transformer model from scratch on their campaign data using Vertex AI Training with A3 machine types.

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Create a BigQuery ML remote model that calls a third-party public API and join it to campaign tables in scheduled queries.

Question 8

A financial institution is evaluating Google Cloud's generative AI offerings to build an internal knowledge assistant. They require a solution that can understand and generate text, support conversational interactions, and be customized with their proprietary data. They also need to ensure that sensitive data is not used to train the underlying models. Which two Google Cloud services or features should they use? (Choose two.)

Accepted Answer

Gemini models in Vertex AI. Gemini models in Vertex AI and Vertex AI Search and Conversation are the correct choices because they together provide the necessary generative AI capabilities and customization options. Gemini models offer advanced text generation and conversational AI, while Vertex AI Search and Conversation enables grounding in proprietary data and ensures data privacy. The other services are either for different modalities or lack generative AI features.

Answer

Dialogflow ES

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AutoML Natural Language

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Cloud Vision API

Question 9

A healthcare company is building a generative AI application using Vertex AI. They need to ensure that the application adheres to responsible AI principles, such as avoiding harmful outputs and protecting patient data. Which two Google Cloud features or practices should they implement? (Choose two.)

Accepted Answer

Configure safety filters and thresholds in Vertex AI. Configuring safety filters helps prevent harmful outputs, directly supporting the principle of avoiding harm. Using CMEK ensures that patient data is encrypted with keys managed by the company, enhancing data protection and compliance. Together, these features address both output safety and data privacy, which are critical for responsible AI in healthcare. The other options either increase risk or violate privacy.

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Increase the model's temperature to generate more diverse responses

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Disable logging of all API requests to protect patient data

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Fine-tune the model on a dataset of patient interactions without anonymization

Question 10

A small marketing agency wants to quickly experiment with prompts for Gemini to draft social media posts, without writing code or provisioning infrastructure. They need a Google Cloud console experience for iterating on prompts and comparing model outputs. Which offering should they use?

Accepted Answer

Vertex AI Studio. Vertex AI Studio is the console-based workspace for prompt design, model comparison, and rapid prototyping with Gemini. It requires no coding or infrastructure setup, making it ideal for a marketing team that wants to experiment with social post drafts. The other choices are pipeline orchestration, SQL-based modeling, or office productivity tools that do not offer interactive prompt experimentation.

Answer

BigQuery ML

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Gemini for Google Workspace

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Vertex AI Pipelines

Question 11

A startup's developer wants to quickly test different prompts against Gemini models, compare model outputs side by side, and export working prompt code, all from a browser interface before committing to an application architecture. Which Google Cloud offering should the developer use?

Accepted Answer

Vertex AI Studio. Vertex AI Studio is the browser-based environment built for prompt design and rapid experimentation with Gemini models, including side-by-side comparison and code export. When the goal is to evaluate prompt behavior before committing to an application design, this console is the intended starting point rather than pipeline orchestration, end-user assistants, or a model catalog.

Answer

Vertex AI Model Garden

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Gemini for Google Workspace

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Vertex AI Pipelines

Question 12

A global bank wants to use Gemini models in Vertex AI to summarize sensitive customer emails. The security team requires that prompts and responses never leave the bank's controlled network perimeter and that access is restricted to approved projects. Which Google Cloud capability should they configure?

Accepted Answer

VPC Service Controls. VPC Service Controls let organizations define a service perimeter around Google Cloud services, including Vertex AI. Resources inside the perimeter can communicate, but access from outside is blocked, which prevents data exfiltration. Combined with IAM policies limiting access to approved projects, this satisfies the bank's requirement that sensitive prompts and responses remain within a controlled boundary.

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Cloud Armor

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Cloud CDN

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Cloud Interconnect

Question 13

A logistics company stores delivery exception reports as PDFs in a Cloud Storage bucket. An analyst needs to ask natural-language questions across all the reports and receive answers with citations to the source pages, with minimal development work. Which Google Cloud capability should the analyst use?

Accepted Answer

Vertex AI Search with a data store grounded on the Cloud Storage documents. Vertex AI Search ingests documents directly from Cloud Storage, builds a managed index, and returns natural-language answers grounded in those documents with citations to source pages. The alternatives all require substantial custom development: Pipelines orchestrates custom code, BigQuery ML needs the data loaded and parsed, and Cloud Vision only extracts raw text and features without answering questions.

Answer

BigQuery ML with a remote Gemini model

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Vertex AI Pipelines orchestrating a custom document parser

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Cloud Vision API batch annotation of the PDFs

Question 14

A media company wants its editors to summarize long internal research documents. Legal requires that no document content be used to train or improve any model, and that data stays within the company's Google Cloud project. Which capability should the company verify before adopting a Gemini-based solution on Vertex AI?

Accepted Answer

That prompts and responses are excluded from model training and covered by Google Cloud's data processing terms.. Legal's requirements are about how data is governed, not about model features. Confirming that prompts and responses are excluded from training and processed under enterprise data terms addresses both the no-training condition and the residency condition. Fine-tuning contradicts the no-training rule, context window size is irrelevant to governance, and personal accounts bypass the enterprise controls the company depends on.

Answer

That the summarization endpoint supports a 1-million-token context window for the longest documents.

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That the company's editors can access the Gemini web app with their personal Google accounts for convenience.

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That the model can be fine-tuned on the research documents so summaries match the editors' preferred style.

Question 15

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