Generative AI Leader Google Cloud's Generative AI Offerings Practice Test 14 — 15 Questions

Question 1

A small marketing agency wants to let its non-technical staff draft blog posts using generative AI without writing any code or managing infrastructure. The agency already uses Google Workspace. Which Google Cloud offering should they adopt to meet this need most directly?

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

Accepted Answer

Gemini for Google Workspace. Gemini for Google Workspace brings generative assistance into the productivity applications the agency already relies on, so non-technical staff can draft and edit content without any coding or infrastructure work. The other services either require AI engineering effort or perform analysis rather than content generation.

Answer

Cloud Natural Language API

Answer

Vertex AI Agent Builder

Answer

Vertex AI Model Garden

Question 2

A university research group wants to experiment with Google's Gemini models through a simple web interface, without writing any code or provisioning cloud infrastructure. They need to upload PDFs, ask questions, and iterate on prompts interactively. Which Google Cloud offering should they use?

Accepted Answer

Vertex AI Studio. Vertex AI Studio is the console-based environment for prompting Gemini models, uploading files such as PDFs, adjusting parameters like temperature and token limits, and saving prompts. It requires no coding or infrastructure provisioning, which matches the research group's interactive experimentation needs. Pipelines, Feature Store, and Model Registry serve orchestration, feature management, and model cataloging respectively, none of which provide a prompt playground.

Answer

Vertex AI Model Registry

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Vertex AI Feature Store

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Vertex AI Pipelines

Question 3

A team needs to generate photorealistic product imagery for an e-commerce catalog from text descriptions, and later needs to edit existing photos by removing unwanted objects while preserving the rest of the scene. Which Google Cloud generative media capabilities should they use for these two tasks, respectively?

Accepted Answer

Imagen for text-to-image generation, and Imagen editing capabilities for object removal and inpainting. Text-to-image generation and masked image editing are distinct capabilities, and both are provided within the same generative media family. Using it for generation from product descriptions and for inpainting-based object removal keeps quality and tooling consistent. Analytics platforms, video pipelines and detection-only models each cover adjacent ground but cannot fulfill the complete pair of tasks described.

Answer

Gemini for text-to-image generation, and BigQuery for object removal

Answer

Vertex AI Vision for text-to-image generation, and Imagen for object removal

Answer

Imagen for text-to-image generation, and a custom-trained object detection model for removal

Question 4

A logistics company must answer employee questions using its internal policy manuals, which change weekly. The manuals are stored in a Cloud Storage bucket and must never be used to train a shared model. The company wants relevant, up-to-date answers with citations. Which Google Cloud approach should it use?

Accepted Answer

Use Vertex AI Search with the Cloud Storage bucket as a grounded data store. Grounding responses in a Vertex AI Search data store keeps answers tied to the current contents of the Cloud Storage bucket and provides citations to the source passages. Because the documents are indexed rather than used to train a shared model, the company retains control over sensitive policy text. Fine-tuning and prompt stuffing both fail on freshness, data handling, or scalability.

Answer

Fine-tune a Gemini model on the policy manuals and redeploy it weekly

Answer

Export the manuals into prompt text and paste them into every request

Answer

Increase the Gemini model's temperature setting to improve factual recall

Question 5

A financial services firm is evaluating Google Cloud generative AI offerings to build an internal assistant that answers employee policy questions using the firm's own documents. The firm requires that answers be grounded in those documents rather than the model's general knowledge, and that the assistant cite the source passages it used. Which two capabilities should the firm rely on? (Choose two.)

Accepted Answer

Citations that return the source passages the model used for its answer. Grounding with Vertex AI Search retrieves authoritative passages from the firm's own corpus so answers rest on internal documents, and citations expose the exact passages used, delivering the provenance the firm demands. Tuning does not guarantee traceable grounding, Google Search grounding pulls public content, and higher temperature only adds randomness, so those do not meet the requirements.

Answer

Raising the model temperature so responses vary more across repeated questions

Answer

Tuning a model on the firm's documents so answers are memorized in the model weights

Answer

Grounding with Google Search to pull current public web results into the response

Question 6

A logistics company wants to build a generative AI application that answers employee questions using its internal policy documents while keeping the data inside its own Google Cloud project. The team needs enterprise-grade security, access control, and the ability to choose among multiple foundation models. Which offering should they choose?

Accepted Answer

Vertex AI with Gemini models. Vertex AI provides the enterprise controls, model flexibility, and grounding capabilities needed to build a policy-question answering application inside the company's own project. The productivity, prototyping, and vision services either lack the required governance or address entirely different workloads.

Answer

Google AI Studio

Answer

Cloud Vision API

Answer

Gemini for Google Workspace

Question 7

A logistics company wants its internal assistant to answer questions using its private fleet maintenance manuals. The manuals change weekly, and the company does not want to retrain a model each time. Accuracy must be traceable to the source document. Which Google Cloud approach should they implement?

Accepted Answer

Use Retrieval-Augmented Generation with Vertex AI Search as the grounding data store. Retrieval-Augmented Generation keeps the knowledge in an external index rather than in model weights. Vertex AI Search can index the private manuals, refresh them as they change, retrieve relevant passages per query, and return grounding metadata that supports citations. Fine-tuning, temperature changes, and static system instructions either require retraining, do not retrieve private data, or cannot scale to frequently updated documents.

Answer

Distill the manuals into a system instruction embedded in every prompt

Answer

Fine-tune a Gemini model on the maintenance manuals using supervised tuning

Answer

Increase the model temperature so it produces more detailed answers

Question 8

A healthcare organization is using Vertex AI to build a generative AI application that summarizes patient notes. They need to ensure that the model does not inadvertently generate or expose protected health information (PHI) in its outputs. Which Google Cloud feature should they implement to detect and redact sensitive data in the model's responses?

Accepted Answer

Cloud Data Loss Prevention (DLP) API. Cloud DLP API is designed to discover, classify, and protect sensitive data such as PHI. By integrating it into the output pipeline of the generative AI application, the healthcare organization can automatically detect and redact PHI from model responses, ensuring compliance and preventing data leaks.

Answer

Vertex AI Model Monitoring

Answer

VPC Service Controls

Answer

Cloud Identity-Aware Proxy (IAP)

Question 9

A logistics company needs a generative AI model that can accept both text and images as input, and produce text output for describing shipping damage. They want to use a Google Cloud model through the Vertex AI API. Which Gemini model capability should they select?

Accepted Answer

Gemini 1.5 Pro with multimodal input support. A multimodal Gemini model such as Gemini 1.5 Pro can take both text and images and return text, directly satisfying the need to describe shipping damage from photos and written notes. The other services either restrict input to text, produce vectors instead of language, or focus on vision classification rather than generative text output.

Answer

Gemini 1.5 Flash with text-only input

Answer

Vertex AI Vision product for image classification

Answer

Vertex AI Embeddings API for text and image vectors

Question 10

A logistics company wants to build a generative AI application that answers employee questions about internal HR policies. The company's policy documents are already stored in Google Drive and must stay synchronized automatically as they are edited. The team has limited ML engineering resources and prefers a managed, low-code path. Which Google Cloud approach should they choose?

Accepted Answer

Create a Vertex AI Search application with a data store connected to the Google Drive folder, then ground Gemini responses on that data store.. Grounding Gemini on a Vertex AI Search data store connected to Google Drive satisfies both constraints: it keeps answers tied to the latest policy documents automatically, and it delivers a managed, low-code experience. The other approaches either require custom ML work, break synchronization, or scale poorly as the document set grows.

Answer

Use the Gemini API with a long system prompt that pastes the full text of every HR policy document into each request.

Answer

Deploy Gemini on a Compute Engine VM with an attached GPU and write a custom retrieval pipeline that polls the Drive API for changes.

Answer

Export the Drive documents to Cloud Storage, then train a custom Gemini model from scratch on those files using Vertex AI Training.

Question 11

A logistics company wants its operations analysts to ask natural-language questions such as 'Which routes had the most delays last quarter?' and receive answers grounded in data stored in BigQuery, without analysts writing SQL. The team has no plans to build or train custom models. Which Google Cloud offering should they adopt?

Accepted Answer

BigQuery data canvas. The analysts need conversational analytics over data already in BigQuery with no model training, which is exactly what BigQuery data canvas delivers through Gemini-assisted natural-language exploration that generates SQL and visualizations. The other services target model cataloging, pipeline orchestration, or data integration, none of which provide prompt-driven querying of BigQuery tables for non-technical users.

Answer

Vertex AI Pipelines

Answer

Vertex AI Model Garden

Answer

Cloud Data Fusion

Question 12

A media company wants to use Google Cloud generative AI to produce short video clips from text prompts for internal storyboarding. The team needs a managed model that can generate video from text and wants to evaluate it before committing to production. Which two Google Cloud offerings or capabilities should they use? (Choose two.)

Accepted Answer

Veo on Vertex AI. Veo on Vertex AI provides the actual text-to-video generation, while Vertex AI Model Garden gives the team a managed place to discover and evaluate that model before production adoption. Together they cover both the creative capability and the evaluation step. The remaining services address images, documents, or language translation and cannot generate video.

Answer

Cloud Translation API

Answer

Document AI

Answer

Imagen on Vertex AI

Question 13

A hospital network wants to transcribe clinician dictation and then have a generative model produce structured discharge summaries from those transcripts, all within Google Cloud. Which combination of offerings matches this workflow?

Accepted Answer

Cloud Speech-to-Text followed by Vertex AI Gemini. The workflow requires converting spoken dictation into text and then generating a structured summary from that text. Speech-to-Text handles the audio transcription, and Gemini on Vertex AI performs the generative summarization, so this pairing correctly sequences the two capabilities. The alternatives invert the audio direction or substitute document and image services that cannot transcribe dictation.

Answer

Document AI followed by Cloud Vision API

Answer

Cloud Text-to-Speech followed by Vertex AI Gemini

Answer

Vertex AI Gemini followed by Cloud Speech-to-Text

Question 14

A bank's risk team must review every AI-generated customer communication for compliance before it is sent. They want to enforce a policy that blocks any message containing prohibited financial advice and routes flagged messages to a human reviewer. Which Vertex AI capability should they configure?

Accepted Answer

A custom classifier deployed on Vertex AI Endpoints and invoked in the generation workflow. The bank needs an inline, policy-specific gate on each generated message. A custom classifier trained on compliant and non-compliant examples, deployed to a Vertex AI Endpoint, can be called in the generation pipeline to score each message; the application then blocks or routes based on that score. Monitoring, standard safety filters, and offline evaluation do not provide real-time domain policy enforcement and routing.

Answer

Vertex AI Model Monitoring with skew and drift detection

Answer

Vertex AI safety filters configured with adjustable harm thresholds

Answer

Vertex AI Model Evaluation with an automatic metric threshold

Question 15

A media company is evaluating Google Cloud's generative AI offerings for two distinct needs: enabling its journalists to summarize research inside Google Docs, and building a custom internal tool that calls Gemini models programmatically with its own authentication and logging. Which two Google Cloud offerings map to these needs? (Choose two.)

Accepted Answer

Vertex AI for programmatic Gemini model access. The journalists' in-editor summarization need is met by Gemini for Google Workspace, while the custom programmatic tool requires Vertex AI's APIs, IAM, and logging. The remaining services handle speech, conversational flows, or document parsing and do not provide the requested generative capabilities.

Answer

Document AI for parsing research documents

Answer

Cloud Speech-to-Text for transcribing interviews

Answer

Dialogflow CX for building the internal tool