Google Cloud's Generative AI Offerings

A healthcare company is building a chatbot to answer patient queries based on their medical documents stored in Cloud Storage. They want to minimize latency and ensure data residency in the EU. Which Vertex AI service should they use?

A startup wants to generate product descriptions from a few keywords using a large language model. They have no prior ML experience and need the fastest time-to-market. Which Google Cloud service should they use?

A retail company wants to build a customer service chatbot that can handle returns, order status, and FAQs. They need to integrate with their existing backend systems. Which Google Cloud service should they use?

A media company uses Vertex AI to generate video captions. The generated captions sometimes contain factual errors about named entities (e.g., actor names). Which technique would most likely reduce these errors?

A company is using Vertex AI Gemini API to analyze customer feedback. They notice that the model occasionally generates offensive content. They have already set safety settings to block high-probability harmful content. What additional step should they take to further reduce offensive outputs?

A global e-commerce company wants to translate product descriptions into 50 languages with high accuracy. They need to handle domain-specific terms (e.g., 'size chart', 'return policy'). Which approach should they use?

Which TWO options are benefits of using Vertex AI Model Garden compared to using raw pre-trained models from external sources? (Choose two.)

Which THREE factors should be considered when choosing between Gemini 1.5 Pro and Gemini 1.5 Flash for a customer-facing chatbot? (Choose three.)

Which TWO features are available in Vertex AI Studio for prompt engineering? (Choose two.)

A company is building a generative AI chatbot for customer support using Vertex AI. They want to ground the model responses with their internal knowledge base stored in Cloud Storage and BigQuery. Which feature should they use to ensure the model only answers from the provided data and avoids hallucination?

A developer is using Vertex AI Studio to prototype a chat application. They want to provide the model with a system instruction to set the tone and style. How should they configure this in the Vertex AI Studio interface?

An organization is using Vertex AI Agent Builder to create a customer service agent. They want the agent to be able to hand off to a human agent when it cannot answer a question. What should they configure in the agent's design?

A data scientist wants to fine-tune a foundation model from Vertex AI Model Garden on their custom dataset. They want to choose a cost-effective method that updates only a small subset of parameters. Which fine-tuning approach should they use?

Which TWO features are available in Vertex AI Agent Builder to enhance the conversational abilities of an agent? (Choose TWO.)

Which THREE considerations are critical when deploying a generative AI model using Vertex AI Endpoints for a latency-sensitive application? (Choose THREE.)

A developer receives the above JSON response from a Vertex AI language model. The output content is correct, but the developer expected the model to not answer geography questions. What should the developer do to prevent the model from responding to geography queries?

You are a generative AI architect for a large e-commerce company. Your team has built a product description generator using Vertex AI's text-bison model. The model is accessed via the Vertex AI API from a web application. You have set the temperature to 0.5 and top_k to 40. The team reports that the generated descriptions are often too generic and lack creativity. They want the descriptions to be more diverse and engaging. You are also concerned about cost, as each API call is billed. Which change should you recommend to increase creativity while managing cost?

A global e-commerce company is using Vertex AI to build a generative AI chatbot for customer support. The chatbot is powered by the Gemini 1.5 Pro model and uses a vector search index for retrieval-augmented generation (RAG) over product documentation. The company has deployed the application in four regions (us-central1, europe-west4, asia-east1, and australia-southeast1) using a multi-region deployment with a global endpoint. The application is critical and requires high availability with a target latency of under 500ms for the RAG pipeline. Recently, users in Australia are experiencing inconsistent latency spikes, with response times exceeding 2 seconds during peak hours. The team suspects that the issue is related to the vector search index's replication and serving configuration. The index has 10 million embeddings with a dimension of 768. It is stored in a single regional bucket in us-central1, and the vector search index endpoint is deployed in all four regions with the same deployed index ID. The team is using the default configuration for index updates and serving. Which action should the team take to resolve the latency issue for Australian users?

A company is building a customer support chatbot using Vertex AI Agent Builder. They want the agent to answer questions based on their internal knowledge base. Which feature should they use?

A company is using Gemini Pro for code generation. They want to ensure that the generated code does not contain security vulnerabilities. Which approach should they implement?

You want to use a Google foundation model to generate text summaries of news articles. Which Vertex AI service should you use?

A developer is using the Vertex AI Gemini API to generate product descriptions. They get a 400 error 'INVALID_ARGUMENT: The model's maximum input token limit is 8192.' What is the most likely issue?

A company is using Vertex AI Agent Builder to create a travel booking agent. They want the agent to book flights and hotels dynamically. What action type should they use?

Which TWO actions can reduce the cost of using Vertex AI Gemini API? (Choose two.)

Which THREE factors should you consider when selecting a foundation model from Model Garden? (Choose three.)

Refer to the exhibit. This is the IAM policy for a project containing a Vertex AI Agent Builder agent and a data store. The agent is unable to access the data store. What is the most likely cause?

A data scientist wants to quickly prototype a text generation application using Google's foundation models. Which Google Cloud service should they use?

An organization is using Vertex AI to fine-tune a large language model. They notice training is taking longer than expected and cost is increasing. Which action is most likely to reduce training time and cost without significantly impacting model quality?

A security team wants to prevent prompt injection attacks on their generative AI application hosted on Vertex AI. Which best practice should they implement?

A developer needs to generate embeddings for text data to be used in a semantic search application. Which Google Cloud service should they use?

A company is using Vertex AI Model Garden to discover and test various foundation models. They need a model that can generate code from natural language. Which model should they select?

A financial services firm needs to generate synthetic data for training models while ensuring that no real customer data leaks. Which technique should they use?

A project manager wants to understand which Google Cloud generative AI services are subject to the 'Prohibited Use' policy. Where can they find the most up-to-date information?

A company is evaluating Google Cloud's generative AI offerings for enterprise use. Which TWO considerations are most important when selecting the right model deployment option?

An organization is building a generative AI application on Vertex AI. Which THREE actions should they take to ensure responsible AI practices?

A developer wants to use the Gemini API to generate creative text. Which TWO parameters can they adjust to influence the output?

A company is building a customer support chatbot using Vertex AI Agent Builder. They want the agent to answer questions based on internal knowledge base documents stored in Cloud Storage. Which feature should they configure to ensure the agent can retrieve relevant information from these documents?

A startup wants to generate images from text descriptions for their marketing materials. They prefer a managed service that requires minimal coding. Which Google Cloud generative AI offering should they use?

An organization is using Vertex AI Gemini API for a multimodal chatbot. They notice that the model sometimes provides incorrect information with high confidence. They want to reduce hallucinations without retraining the model. What is the most effective approach?

A retailer wants to use generative AI to write product descriptions automatically. They have a large dataset of existing product descriptions and need to customize a foundation model for their brand voice. Which Vertex AI feature should they use?

A company deploys a fine-tuned text generation model on Vertex AI Endpoints. They want to monitor for data drift and performance degradation over time. Which GCP service should they integrate?

A financial services firm is using Vertex AI to generate investment reports. They need to ensure that the model outputs are explainable and comply with regulatory requirements. Which Vertex AI feature should they use?

A developer wants to integrate Gemini multimodal capabilities (text + image) into a mobile app using Python. Which Google Cloud client library should they use?

A healthcare company is building a chatbot to answer patient queries using Vertex AI Agent Builder. They want to ensure the chatbot only uses approved medical references and does not generate unverified advice. How should they configure the agent?

Which TWO safety features are available in Vertex AI Gemini API? (Select TWO.)

A data scientist runs the above command to upload a model to Vertex AI Model Registry. The model is a TensorFlow 2.6 model trained on tabular data. After deployment to an endpoint, the prediction latency is higher than expected. What is the most likely cause?

A developer is configuring a Vertex AI Agent Builder agent to use grounding. They receive the following error when calling the API: `404 Not Found: Data store resource not found.` What is the most likely cause?

A data scientist is using the Vertex AI PaLM API for text generation. They notice that the model occasionally generates toxic content. Which parameter should they adjust to reduce the likelihood of toxic outputs?

An organization is deploying a summarization model on Vertex AI and needs to ensure that the model's responses are consistent and avoid hallucinations. They have a labeled dataset of source documents and human-written summaries. Which approach would best align the model with their quality requirements?

A developer is using the Vertex AI PaLM API and receives a 429 Resource Exhausted error. What is the most likely cause?

A company is using Vertex AI Model Registry to manage multiple versions of its custom generative model. They want to automatically route a percentage of traffic to a new model version for testing. What should they do?

A developer needs to use the Vertex AI PaLM API to generate text embeddings for a large corpus of documents. Which model should they use?

A machine learning engineer is deploying a large generative model on Vertex AI. The model requires a GPU with high memory. Which machine configuration should they choose?

A developer is using the Vertex AI PaLM API to generate code. They want to ensure the output is safe and adheres to company policies. Which THREE attributes can they configure in the safety_settings parameter?

The exhibit shows the output of describing a model on Vertex AI. What does 'modelSource: MODEL_GARDEN' indicate about this model?

A model response deployed on Vertex AI includes safety attributes with a toxicity score of 0.9 and an insult score of 0.3. The application must reject any prediction where the toxicity score exceeds 0.8. Based on the response, what action should the application take?

A startup wants to quickly integrate a generative AI chatbot into their customer support platform. They need a solution that can answer questions based on their internal knowledge base with minimal setup. Which Google Cloud service should they use?

A company is building a document summarization tool using Vertex AI Gemini API. They notice that the model sometimes returns incomplete summaries that miss key points. Which approach is most likely to improve summary quality without increasing token usage significantly?

An engineer is testing a generative AI application using the Gemini API. They receive a 400 error with message 'INVALID_ARGUMENT: text has been blocked.' What is the most likely cause?

A retailer is building a product recommendation chatbot using Vertex AI Agent Builder. They want the agent to answer questions about product availability, prices, and promotions, but also to escalate to a human agent when the query is complex. What should they configure in Agent Builder?

A developer wants to use Gemini 1.5 Pro to analyze hour-long video content and generate a summary. Which feature of Gemini 1.5 Pro is most suitable for this task?

A data scientist uses Vertex AI Model Evaluation to assess a fine-tuned model for sentiment analysis. The evaluation report shows high precision but low recall on the 'negative' class. What is the best course of action to improve recall without sacrificing too much precision?

Which THREE steps are required to secure a generative AI pipeline that uses Vertex AI and involves sensitive customer data?

Which THREE benefits does Vertex AI Agent Builder provide over building a custom conversational agent from scratch?

A company is building a customer service chatbot using Vertex AI Agent Builder. The chatbot needs to answer questions based on a large internal knowledge base stored in a Cloud Storage bucket. The team wants to ensure the model can reference the latest documents without fine-tuning. Which configuration should they use?

A data scientist wants to generate realistic product images for an online catalog using Google Cloud's generative AI. Which service should they use?

A company deploys a Gemini model on Vertex AI for a healthcare application. They need to ensure that the model does not generate medical advice and that responses are grounded in trusted medical sources. Which combination of safety measures should they implement?

A developer is using Vertex AI's Generative AI Studio to prototype a text summarization model. The initial results are too verbose. What is the most efficient way to adjust the output length without retraining?

A startup wants to embed generative AI features into their mobile app but has limited ML expertise. Which Google Cloud service is best suited for rapid integration with no ML training?

A company is deploying a large language model on Vertex AI for real-time inference. They observe high latency and want to optimize. They have already enabled model caching. What next step should they take to reduce latency?

A marketing team wants to use Vertex AI to generate ad copy. They need the model to follow a specific tone and style. What is the best approach?

A developer wants to generate Python code using Google Cloud's generative AI. Which model should they invoke?

A company is building a generative AI application that must adhere to strict data residency regulations. Which TWO Google Cloud features can help ensure that data does not leave a specific geographic region?

A machine learning engineer is tuning a large language model on Vertex AI for question answering. They want to evaluate the model's performance before deployment. Which THREE metrics should they consider?

A data scientist is using Vertex AI's Generative AI Studio to experiment with prompt designs. Which THREE features are available in the studio?

A fintech startup is building a generative AI application that generates personalized investment advice based on user profiles and market data. They are using Vertex AI Agent Builder to create an agent that retrieves information from a BigQuery table containing user data and from a real-time market data API. The agent needs to ensure that responses comply with financial regulations, meaning the model must not give specific stock recommendations unless the user explicitly requests them after disclaimers. The team has implemented grounding with both sources. During testing, the agent sometimes spontaneously suggests buying a particular stock without being asked, which could lead to regulatory issues. The team wants to enforce strict control over the agent's behavior. What should the team do?

A retail company is building a chatbot for customer service. They need the model to generate product descriptions based on a catalog but also answer questions about store policies. The team wants to minimize latency and cost while maintaining high accuracy. Which Google Cloud generative AI offering should they use?

A media company is using Vertex AI Imagen to generate marketing images. The output frequently contains unrealistic artifacts, especially in human faces. The team has fine-tuned the model using their brand assets. What is the most likely cause and recommended fix?

A startup wants to deploy a custom-tuned large language model for real-time inference on Vertex AI. They need the lowest possible latency for end users. What deployment strategy should they choose?

Which TWO of the following are capabilities of Vertex AI Model Garden? (Choose 2)

A healthcare startup is using Vertex AI Imagen to generate synthetic medical images for training a diagnostic model. The images must comply with HIPAA regulations and cannot contain any real patient data. The team fine-tuned Imagen on a dataset of de-identified medical scans. However, during testing, they notice that some generated images closely resemble specific patients from the original dataset, even though the dataset was de-identified. They suspect that the model memorized some training examples. The team needs to address this issue without losing image quality. They have access to the original training data and Vertex AI tools. What action should they take?

A marketing agency wants to use Vertex AI to automatically generate social media posts for clients. They plan to use the Gemini API with few-shot prompting. The agency's developers have limited experience with generative AI and want the fastest way to prototype and iterate on prompts. They are already using Google Cloud for other services. Which approach should they take to quickly develop and test prompts?

An e-commerce company is using Vertex AI PaLM 2 for Text (via Model Garden) to generate product descriptions. They have an existing pipeline that calls the model with a prompt including product attributes. Recently, they migrated to the Gemini API. The team notices that the Gemini model sometimes outputs descriptions that are factually inconsistent with the input (e.g., wrong color or size). This was less frequent with PaLM 2. They have not changed the prompts. What is the most likely cause and solution?

A multinational corporation is using Vertex AI to generate multilingual customer support responses. They have fine-tuned the Gemini model on support tickets in English and now want to extend to 10 additional languages. The fine-tuning dataset for new languages is small (1000 tickets each). During evaluation, the model performs well for common languages (Spanish, French) but poorly for languages like Finnish and Thai. The team needs to improve performance for low-resource languages. They have budget constraints and cannot collect more data quickly. Which approach should they take?

A gaming company is using Vertex AI Imagen to create concept art. They have a stable pipeline that generates images based on text prompts. Recently, they introduced a new feature: using a reference image to guide the style (image-to-image generation). However, when using a reference image, the generated images often have unnatural color shifts and artifacts. The team suspects that the reference image is being resized to a resolution that the model wasn't trained on. They are using the default Imagen settings. What is the most likely cause and the best solution?

A company needs to fine-tune a foundation model on Vertex AI for a custom text classification task with only 500 labeled examples. They want to minimize cost while achieving high accuracy. What is the MOST cost-effective approach?

Which THREE capabilities are provided by Vertex AI Agent Builder? (Choose three.)

A software company is using Vertex AI to build a generative AI application that creates code snippets from natural language descriptions. They want to improve the model's performance on their specific coding style and libraries. Which two techniques should they use? (Choose two.)

A media company wants its internal knowledge assistant to answer employee questions using the company's own policy documents and past project reports, while keeping the Gemini model's general reasoning ability intact. The team has a large corpus stored in Cloud Storage and does not want to retrain or fine-tune the model. Which Google Cloud approach should they use?

A media company wants to create a custom AI assistant that can answer employee questions by referencing internal policy documents stored in Google Drive. They need a low-code solution that allows them to configure the assistant, connect data sources, and deploy it for internal use. Which Google Cloud offering should they use?

A small marketing team wants to use a Google Cloud generative AI model to generate creative text for social media posts. They prefer a fully managed, ready-to-use API that requires minimal setup and does not need model training. Which Google Cloud service should they use?

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?

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?

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?

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?

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?

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?

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?

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.)

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.)

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?

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?

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?

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?

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?

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?

A media company needs to generate short video clips from text prompts for a marketing campaign. They want to use a Google Cloud service that can create videos up to 8 seconds long, 720p resolution, and can be accessed via Vertex AI. Which Google Cloud generative AI offering should they use?

A startup wants to quickly prototype a generative AI application that can write marketing copy. They have limited machine learning expertise and want to avoid managing infrastructure. They prefer a fully managed, no-code or low-code solution that provides access to Google's foundation models. Which Google Cloud offering should they use?

A software company wants to use a Google Cloud generative AI model to automatically generate code snippets from natural language descriptions. The developers need the model to produce syntactically correct code in multiple programming languages. Which Google Cloud offering is specifically designed for this use case?

An enterprise architect must let a fleet of internal applications call Gemini models on Google Cloud while enforcing VPC Service Controls perimeters, customer-managed encryption keys, and audit logging tied to the organization's existing IAM. Which Google Cloud offering should the architect standardize on?

A logistics company wants to build a generative AI application that answers questions over thousands of internal policy PDFs stored in Cloud Storage. They need Google Cloud to handle document ingestion, chunking, indexing, and retrieval for grounding, while they focus only on the application logic. Which Google Cloud offering should they use?

A bank is deploying a Gemini model on Vertex AI to draft responses to customer complaints. Compliance requires that the deployment (Choose two.)

A healthcare provider is building a generative AI assistant on Vertex AI to answer patient questions about appointment scheduling. They must ensure the model does not produce harmful or medically unsafe content, and they want to monitor and tune safety behavior over time. Which two Google Cloud features should they use? (Choose two.)

A university's IT department is evaluating Google Cloud generative AI offerings to build a course-assistant tool for students. They want to reduce engineering effort by using managed services rather than hosting models themselves. Which two Google Cloud offerings should they consider? (Choose two.)

A startup wants to build a conversational AI assistant that can understand and generate text, images, and code. They need a single model that can handle multimodal inputs and outputs. Which Google Cloud generative AI model should they choose?

A large enterprise is using Vertex AI to deploy a generative AI model for internal document summarization. They need to ensure that the model's responses are based on the most current internal documents and that the model does not hallucinate. They also want to minimize latency and cost. Which feature of Vertex AI should they implement?

A financial services company wants to use generative AI to summarize large volumes of internal documents while ensuring that sensitive data never leaves their virtual private cloud (VPC). They need a solution that provides Gemini models with enterprise-grade security and data residency controls. Which Google Cloud service should they use?

A media production studio wants to create short video clips from text prompts for social media content. They need a Google Cloud generative AI model that can generate videos from text. Which offering should they use?

A global logistics company wants to build a generative AI assistant that can answer operational questions by retrieving information from internal PDF and HTML documents stored in Cloud Storage. They need a managed, serverless retrieval-augmented generation (RAG) capability that requires minimal infrastructure management and integrates with Vertex AI. Which Google Cloud service should they use?

A media company wants to let its editors query a large archive of internal video transcripts using everyday conversational questions, and the app must return grounded answers that cite the exact source clips. The team has no machine learning engineers and wants the least operational overhead. Which Google Cloud offering should they use?

A logistics company wants employees to ask natural-language questions about shipment trends and have Gemini generate SQL, charts, and narrative summaries directly against data already stored in BigQuery, without moving or duplicating that data. Which Google Cloud capability should the company adopt?

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?

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?

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?

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?

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?

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?

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?

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?

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?

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?

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?

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.)

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.)

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?

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?

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.)

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?

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.)

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.)

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?

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?

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?

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?

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?

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?

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?

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?

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?

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.)

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?

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?

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?

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?

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?

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.)

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?

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?

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?

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?

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?

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?

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.)

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?

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?

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.)

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?

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?

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?

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?

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?

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.)

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?

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.)

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?

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?

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?

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?

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.)

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?

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?

A software vendor embeds Gemini in its SaaS product and needs each tenant's prompts and outputs isolated so that one tenant's data never appears in another tenant's responses. The vendor also wants usage tracked per tenant for billing. Which design should the team implement on Google Cloud?