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

Question 1

A software company wants to build a generative AI application that can answer questions based on its internal documentation. The documentation is stored in Google Drive and Confluence. They need a managed solution that can index these sources, provide relevant answers with citations, and integrate with their existing identity provider for access control. Which Google Cloud service should they use?

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

Accepted Answer

Vertex AI Search. Vertex AI Search is a fully managed service that can connect to Google Drive and Confluence, index the content, and provide generative AI answers with citations. It also integrates with identity providers to enforce document-level access control, making it the ideal solution for the software company's needs.

Answer

Vertex AI Feature Store

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Cloud SQL

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Vertex AI Pipelines

Question 2

A small startup wants to add image generation to its design tool. The developers want to call a Google Cloud generative AI model through a simple API without provisioning infrastructure, managing model servers, or handling GPU capacity planning. Which Google Cloud offering best fits this requirement?

Accepted Answer

The Imagen model on Vertex AI accessed through the Vertex AI API, using the managed model without deploying a custom endpoint.. Imagen on Vertex AI provides managed, API-based image generation where Google handles the serving infrastructure and scaling. The startup only needs to authenticate and send prompts, which satisfies the no-infrastructure and no-capacity-planning constraints. Deploying models to endpoints or self-hosting on Kubernetes or functions adds operational burden the team explicitly wants to avoid.

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Cloud Run functions that call a locally hosted diffusion model packaged in the function's container image.

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A custom container running Stable Diffusion on Google Kubernetes Engine with node auto-provisioning enabled.

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Vertex AI Model Garden, where they deploy Imagen to a dedicated endpoint with autoscaling replicas.

Question 3

A small marketing agency wants to experiment with Google's generative AI models without writing code or managing cloud infrastructure. They need a browser-based environment to draft campaign ideas and test prompts quickly. Which Google Cloud offering should they use?

Accepted Answer

Vertex AI Studio. Vertex AI Studio is the console-based workspace for prompt design and model testing, so a non-technical marketing team can draft and compare campaign ideas in a browser without code. The other choices require SQL, pipeline definitions, or deployed serverless code, none of which match a quick, no-code ideation need.

Answer

BigQuery ML with remote model inference

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Vertex AI Pipelines

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Cloud Run functions calling the Gemini API

Question 4

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 5

A bank is deploying a Gemini-powered assistant on Vertex AI to answer questions about loan products. Compliance requires that every answer be traceable to an approved internal source and that the assistant refuse to answer when no approved source supports the response. Which configuration should the bank implement?

Accepted Answer

Ground the model with Vertex AI Search over approved sources and use grounding confidence thresholds. Grounding the assistant in approved sources through Vertex AI Search makes each answer retrievable from a vetted document and provides citations for audit. Using grounding confidence thresholds lets the bank define when support is too weak and instruct the assistant to refuse. Fine-tuning, temperature changes, and safety filters do not deliver source traceability or principled abstention.

Answer

Raise the model temperature and add a system instruction to be accurate

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Enable a higher safety filter threshold so harmful content is blocked

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Fine-tune Gemini on historical chat logs from the bank's contact center

Question 6

A global retailer wants to build a generative AI application on Google Cloud that answers employee questions about HR policies. The team must ensure the application uses their private policy documents, keeps answers grounded in those documents, and avoids exposing confidential content to unauthorized staff. Which two Google Cloud components should they include in the architecture? (Choose two.)

Accepted Answer

Vertex AI Search configured with the HR policy documents as a data store. A grounded HR assistant needs a retrieval layer and a generation layer. Vertex AI Search indexes the private policy documents, retrieves relevant passages per question, and supports access controls, while Gemini models on Vertex AI generate answers from those passages with grounding. Feature Store, Pipelines, and Model Registry address structured features, workflow orchestration, and model versioning, none of which provide document grounding or content access control.

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Vertex AI Pipelines for nightly retraining of the Gemini model on HR documents

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Vertex AI Model Registry for versioning the HR policy document set

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Vertex AI Feature Store for storing employee tenure and department features

Question 7

A financial analytics firm wants to prototype prompts against several Gemini model versions quickly, compare outputs side by side, and then export the winning prompt configuration into a production application with enterprise controls. Which combination of Google Cloud offerings best supports this workflow?

Accepted Answer

Google AI Studio for prototyping, then Vertex AI for production deployment. Prototyping prompts against multiple Gemini models is fastest in Google AI Studio, and moving the validated configuration into a governed environment is what Vertex AI provides. The productivity suite and the language analysis API cannot perform side-by-side generative prompt comparison or serve as the production deployment target.

Answer

Vertex AI for prototyping, then Google AI Studio for production

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Cloud Natural Language API for prototyping, then Vertex AI for production

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Gemini for Google Workspace for both prototyping and production

Question 8

A bank is deploying a retrieval-augmented generation application on Vertex AI so that a Gemini model answers employee policy questions using the bank's internal document repository. The team wants the model's responses to cite source documents and to reduce fabricated content. Which two capabilities should they implement to ground the model in the bank's own content? (Choose two.)

Accepted Answer

Vertex AI embeddings with vector search over the document corpus. Grounding responses in the bank's own content requires retrieving relevant internal passages and supplying them to the model. Indexing documents with Vertex AI Search or generating embeddings and retrieving them through vector search both provide that context, which improves factual accuracy and supports citations to source documents. Training or safety-filter adjustments do not retrieve proprietary content at inference time and therefore cannot satisfy the citation requirement.

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Gemini safety filters configured to block sensitive topics

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Grounding with Google Search

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Vertex AI Pipelines scheduled retraining of the Gemini model

Question 9

A media company wants to build a generative AI application that answers questions using its proprietary video transcripts. They need the model to cite specific transcript segments and avoid hallucinating content not present in those transcripts. Which Google Cloud approach best meets these requirements?

Accepted Answer

Use Vertex AI Search with grounded generation over the transcript data store. Grounded generation with Vertex AI Search retrieves relevant transcript passages and conditions the model's answer on them, which both limits hallucination and enables citations to specific segments. Fine-tuning, prompt-only instructions, or high temperature do not provide the retrieval and attribution needed for factual, source-backed answers.

Answer

Deploy Gemini with a system instruction to answer only from provided content, without retrieval

Answer

Increase the model temperature and include the entire transcript corpus in every prompt

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Fine-tune a Gemini model on the full transcript corpus and deploy it to an endpoint

Question 10

A bank is piloting a Gemini-powered assistant that summarizes internal audit reports. Compliance requires that prompts and responses never leave the company's chosen Google Cloud region, that customer-managed encryption keys protect data at rest, and that no data is used to improve Google's models. Which combination of Vertex AI capabilities should the team configure to meet these requirements?

Accepted Answer

Vertex AI with data residency controls, CMEK through Cloud KMS, and the documented data governance guarantee that customer data is not used to train Google's foundation models.. Meeting all three compliance constraints requires regional processing for residency, Cloud KMS customer-managed keys for encryption at rest, and reliance on Vertex AI's data governance commitments that customer data is not used to train Google's foundation models. Network or prompt-level controls cannot substitute for these platform-level guarantees.

Answer

Vertex AI with CMEK enabled and a VPC Service Controls perimeter, relying on the perimeter to guarantee regional data processing.

Answer

Vertex AI with a global endpoint, default Google-managed encryption keys, and a prompt instruction telling the model not to retain any input.

Answer

Gemini accessed through AI Studio with billing enabled, plus a Cloud Armor policy restricting access to the bank's IP ranges.

Question 11

A hospital network wants to build a generative AI search experience over its clinical guideline PDFs so clinicians can ask natural-language questions and receive answers with citations to the source documents. The solution must run on Google Cloud and keep data within the network's project. Which offering is purpose-built for this requirement?

Accepted Answer

Vertex AI Search with grounding on the guideline corpus. Vertex AI Search indexes the guideline documents and returns grounded answers with citations, which is exactly the retrieval-augmented experience described. Vision, Workspace, and Translation services perform extraction, productivity assistance, or language conversion and cannot deliver cited question answering over the hospital's corpus.

Answer

Gemini for Google Workspace

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Cloud Vision API with OCR on the PDFs

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Cloud Translation API

Question 12

A media company wants to generate short video clips from text prompts for social media ads. The creative team needs a managed Google Cloud service that produces video from descriptive prompts and offers controls for aspect ratio and duration, without managing GPU infrastructure. Which offering should they use?

Accepted Answer

Vertex AI Veo. The team needs prompt-driven video generation delivered as a managed service, which is what Veo on Vertex AI provides, including controls over clip attributes and no infrastructure management. Imagen produces still images, Chirp targets speech, and Cloud Vision API analyzes existing images, so none of the alternatives can generate video content for the social ad campaign.

Answer

Vertex AI Chirp

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Vertex AI Imagen

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Cloud Vision API

Question 13

A financial services firm is evaluating Google Cloud generative AI offerings for an internal knowledge assistant. They need capabilities for controlling access to model endpoints and for monitoring model usage and safety signals. (Choose two.)

Accepted Answer

Cloud Monitoring dashboards and alerting for Vertex AI metrics. IAM roles and policies restrict and grant access to Vertex AI endpoints and related resources, satisfying the access-control requirement. Cloud Monitoring dashboards and alerts surface usage, latency, and error signals for those endpoints, satisfying the monitoring requirement. The remaining options address model export, feature serving, and storage lifecycle, none of which cover access control or operational monitoring.

Answer

BigQuery ML model export to Cloud Storage

Answer

Cloud Storage bucket lifecycle rules on training data

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Vertex AI Feature Store for online serving

Question 14

A hospital network wants patients to describe symptoms in a mobile app and receive immediate guidance, but the clinical knowledge base changes weekly and the network must be able to update answers without retraining a model. They also need the assistant to escalate to a nurse when confidence is low. Which Google Cloud approach best meets these requirements?

Accepted Answer

Build a Vertex AI Agent Builder agent grounded on a frequently refreshed Vertex AI Search data store, with a tool that triggers nurse escalation. The requirements pair a knowledge base that changes frequently with a need to escalate to a human, which maps to an agent grounded on a refreshable data store plus a tool for escalation. Updating the search data store keeps content current without retraining, while function calling handles the nurse handoff. Fine-tuning, giant prompts, or self-managed open models fail one or both constraints.

Answer

Fine-tune a Gemini model on the clinical knowledge base each week and deploy it to a Vertex AI endpoint

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Deploy an open model from Vertex AI Model Garden and manage the serving infrastructure on Google Kubernetes Engine

Answer

Use the Gemini API directly with a long system prompt containing the entire clinical knowledge base

Question 15

A university research team wants to summarize long academic papers using a Google Cloud model. They need a model that supports very large context windows so an entire paper fits in a single request. Which Gemini model characteristic should they prioritize?

Accepted Answer

A model version with a long context window, such as Gemini 1.5 Pro. Summarizing an entire academic paper in one request depends on the model's input context window. Gemini 1.5 Pro's long context capability lets the team include the full document, preserving structure and cross-references. Latency, output token limits, and language coverage affect other aspects but do not remove the need for a large input window.

Answer

A model with the largest number of supported languages

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A model with the lowest latency for short prompts

Answer

A model with the highest number of output tokens per response