Generative AI Leader Google Cloud's Generative AI Offerings Practice Test 13 — 15 Questions

Question 1

A healthcare organization is evaluating Google Cloud's generative AI offerings for building a patient triage assistant. They need a model that can process text and images from patient records and generate text responses. They also require the ability to fine-tune the model on their own data. Which two Google Cloud services or features should they use? (Choose two.)

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

Accepted Answer

Vertex AI Fine-Tuning. Gemini on Vertex AI provides the multimodal capabilities to process text and images, and Vertex AI Fine-Tuning enables customization on the organization's data. Together, they form the foundation for a tailored patient triage assistant that can handle diverse patient records.

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Vertex AI Pipelines

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Vertex AI Model Garden

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Vertex AI Feature Store

Question 2

A software company wants to embed an AI coding assistant into its internal developer portal. The assistant must complete code in the developer's current editor context and also answer natural-language questions about the repository. Which Google Cloud offering is designed for this use case?

Accepted Answer

Gemini Code Assist, which provides code completion and conversational assistance integrated with developer tooling.. The scenario needs two developer-centric capabilities: inline completion in the editor and conversational answers about the repository. Gemini Code Assist is the Google Cloud offering built for both, integrating with supported IDEs and developer tooling. The other services serve data analytics, ML workflow orchestration, or data security, and none provides editor-integrated code assistance for a developer portal.

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Vertex AI Pipelines, which orchestrates machine learning workflow steps such as training and evaluation.

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Cloud Data Loss Prevention, which discovers and de-identifies sensitive data across storage systems.

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Gemini in BigQuery, which generates SQL from natural-language prompts against datasets in BigQuery.

Question 3

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 4

A bank is building a Gemini-powered assistant on Vertex AI that must answer questions about internal policy documents and must not fabricate policy details. The architects want to reduce hallucination and provide auditable sourcing. Which two Google Cloud capabilities should they combine? (Choose two.)

Accepted Answer

Enabling citations that map generated statements to retrieved source chunks. Grounding retrieves authoritative passages so responses are conditioned on real policy text, and citations expose exactly which passages supported each statement. Together they reduce fabrication and create an auditable chain from answer to source. Generation settings and safety controls do not supply factual anchoring, and fine-tuning on unrelated marketing material cannot substitute for retrieval over the actual policy corpus.

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Fine-tuning the model on the bank's public marketing brochures

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Disabling safety filters to allow unrestricted policy answers

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Setting the model temperature to its maximum value

Question 5

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 6

A marketing team wants to generate personalized email subject lines that match their brand voice. They need a Google Cloud service that allows them to fine-tune a generative model on their own email data. Which service should they use?

Accepted Answer

Vertex AI. Vertex AI is the Google Cloud platform that supports fine-tuning generative models like Gemini on custom datasets. It allows the marketing team to train a model on their email data to capture their brand voice and generate personalized subject lines. Other services either lack generative capabilities or are not intended for fine-tuning on custom text data.

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Cloud Natural Language API

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Dialogflow CX

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AutoML Natural Language

Question 7

A media company wants its editorial team to summarize long internal reports and ask follow-up questions about the content, all inside a Google Workspace interface they already use daily. They prefer not to build any custom application or call APIs. Which Google Cloud generative AI offering should they adopt?

Accepted Answer

Gemini for Google Workspace. Gemini for Google Workspace is the offering designed to bring Gemini capabilities directly into the productivity apps an organization already uses, including document summarization and conversational assistance. Because the team wants those abilities inside Docs and Gmail without developing a custom application, the embedded Workspace assistant fits, whereas developer consoles and narrow pretrained APIs do not match the consumption model or the feature set requested.

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Vertex AI Studio

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Google Cloud Speech-to-Text

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Cloud Natural Language API

Question 8

A financial analytics firm wants to query its BigQuery data using natural language, without exporting data to a separate service. They need a solution that integrates directly with BigQuery and uses Gemini models to generate SQL and interpret results. Which Google Cloud capability should they use?

Accepted Answer

Gemini in BigQuery. Gemini in BigQuery embeds generative AI capabilities directly into BigQuery, allowing users to ask questions in plain language, automatically generate SQL, and get explanations of results. This eliminates the need to export data or build custom integrations, making it the ideal choice for a financial firm that wants to query BigQuery data conversationally.

Answer

Cloud SQL with Gemini

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Vertex AI Search

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BigQuery ML with Gemini models

Question 9

A media company is building an application that must summarize 400-page legal contracts. Their current model has a context window of only 32,000 tokens, and truncating the documents loses critical clauses. They want to use a Gemini model on Vertex AI that can ingest the entire contract in a single request. Which Gemini model capability should they select for this workload?

Accepted Answer

A Gemini model variant with a 1 million token context window. The blocker is input capacity: a 400-page contract far exceeds 32,000 tokens. Gemini 1.5 Pro and Gemini 1.5 Flash on Vertex AI offer context windows up to 1 million tokens, letting the application pass the full document plus instructions in one call. Output limits, grounding, and fine-tuning change generation length, factual grounding, or behavior respectively, but none of them enlarges the input window.

Answer

A Gemini model fine-tuned on legal contracts with supervised tuning

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A Gemini model variant with a 32,000 token context window plus a larger output token limit

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A Gemini model with grounding enabled through Vertex AI Search

Question 10

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 11

A logistics company wants an AI agent that answers driver questions about delivery procedures, can look up a shipment's live status through an internal REST API, and can escalate to a dispatcher when it cannot resolve an issue. Which Google Cloud offering is purpose-built for assembling this kind of agent?

Accepted Answer

Vertex AI Agent Builder with tools and a connected data store. An agent framework is needed when a solution must converse, ground answers in internal knowledge and invoke external systems through tools. Vertex AI Agent Builder provides those capabilities natively, including data store grounding and tool calls, plus escalation handling. Data pipelines, feature serving and scheduled jobs each solve adjacent problems but cannot assemble an interactive, tool-using agent on their own.

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Cloud Dataflow with a streaming pipeline for shipment events

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Vertex AI Feature Store for serving driver profile attributes

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Cloud Scheduler jobs that poll the shipment API and email updates

Question 12

A healthcare technology company is using Vertex AI to build a generative AI assistant that answers patient questions about medications. They must ensure the assistant does not provide medical advice and adheres to safety guidelines. Which Google Cloud feature should they use to enforce these constraints?

Accepted Answer

Vertex AI safety filters and configurable safety settings. Vertex AI safety filters and configurable safety settings allow developers to set thresholds and categories that block harmful or inappropriate content, including medical advice. This is the only option that provides real-time enforcement of safety guidelines. Monitoring, Explainable AI, and Pipelines serve different purposes and cannot enforce content restrictions.

Answer

Vertex AI Explainable AI

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Vertex AI Pipelines

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Vertex AI Model Monitoring

Question 13

A startup wants to experiment with Gemini prompts in a browser-based console, compare responses from different models, and save promising prompts for later reuse, without provisioning infrastructure. Which Google Cloud offering best fits this need?

Accepted Answer

Vertex AI Studio. Vertex AI Studio provides an interactive, code-light workspace for writing prompts, comparing model outputs, and storing prompts for reuse, which aligns precisely with the startup's experimentation goal. The other services are specialized for data warehousing, document extraction, or image analysis and lack the prompt design and comparison capabilities, so they cannot deliver the described experience.

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BigQuery ML

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Cloud Vision API

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Document AI

Question 14

A healthcare company wants to use generative AI to summarize patient notes while ensuring compliance with strict data privacy regulations. They plan to use Google Cloud's Vertex AI. Which two features should they implement to protect sensitive data? (Choose two.)

Accepted Answer

VPC Service Controls. Customer-managed encryption keys (CMEK) and VPC Service Controls are both critical for protecting sensitive healthcare data in Vertex AI. CMEK gives the organization control over encryption keys, while VPC Service Controls prevent unauthorized data exfiltration. Together, they help meet regulatory requirements for data privacy and security.

Answer

Storing data in a public Cloud Storage bucket

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Disabling audit logging

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Public IP addressing for Vertex AI endpoints

Question 15

A media company wants to generate realistic images for a new marketing campaign. They need a Google Cloud service that can create images from text prompts and offers enterprise-grade controls for content safety and intellectual property. Which service should they use?

Accepted Answer

Vertex AI Imagen. Vertex AI Imagen is specifically built for generating images from text prompts with enterprise-grade safety and IP controls. It is the Google Cloud offering for text-to-image generation, making it the correct choice for a media company that needs realistic images for marketing while ensuring compliance and safety.

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Vertex AI Vector Search

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Cloud Vision API

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Vertex AI Gemini