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 financial services firm uses a fine-tuned Gemini model in Vertex AI for regulatory compliance checks. They notice that token usage is high, increasing costs. They want to reduce costs without sacrificing accuracy. Which approach should they take?

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.)

What is the most likely cause of the error?

Why is the model responding in English despite the prompt asking for French translation?

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 financial services firm needs to deploy a large language model (LLM) for analyzing sensitive client documents. They require the model to run within their Virtual Private Cloud (VPC) with no internet access and must comply with data residency regulations. Which Google Cloud generative AI offering should they use?

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 company is using Vertex AI for multimodal generative AI to analyze images and text. They need to ensure that the model's outputs are auditable and can be traced back to the input data. Which feature should they enable?

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?

A team deployed a custom generative AI model using KServe on Google Kubernetes Engine (GKE) with the above configuration. They notice that the model is taking longer than expected to respond. What is the most likely cause?

You are a machine learning engineer at a healthcare startup. Your team has developed a generative AI model that summarizes patient medical records. The model is deployed on Vertex AI Endpoints using a custom container. You have configured the endpoint with a single n1-standard-4 machine (4 vCPUs, 15 GB memory) without accelerators. The model uses a small transformer architecture. During load testing with 50 concurrent requests, you observe that the average latency is 8 seconds, which exceeds the requirement of 2 seconds. Additionally, some requests time out after 10 seconds. You suspect the CPU is the bottleneck. You also notice that the model inference code uses TensorFlow but is not optimized for inference. Which action should you take to reduce latency?

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 data scientist is using Vertex AI Model-as-a-Service (MaaS) to deploy a fine-tuned open-source model. They notice high latency during inference. What is the most likely cause?

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 financial services company wants to use Vertex AI Grounding with enterprise data to power a regulatory compliance chatbot. They have strict data residency requirements: data must remain in the EU. What should they do?

You are using Vertex AI Model Garden to deploy a Llama model. Which deployment option provides the best latency for real-time inference?

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?

A team is deploying a real-time chat application using Gemini. They need to ensure the model does not generate harmful content. Which safety filter configuration should they use?

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

Which TWO components are essential for building a multi-turn conversational agent using Vertex AI Agent Builder? (Choose two.)

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

Refer to the exhibit. You ran the gcloud command to list a model, but received this error. What is the most likely issue?

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?

Refer to the exhibit. A developer has defined a dynamic action in the Vertex AI Agent Builder agent YAML. The agent is not triggering the action. What is the most likely issue?

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

A company is deploying a chatbot that must ensure customer data remains within the European Union. Which approach should they take?

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?

During a load test, a Vertex AI endpoint serving a large language model experiences high latency and increased error rates. The endpoint is configured with autoscaling. What is the most likely cause?

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 data scientist is using Vertex AI Model Registry to manage multiple versions of a custom text classification model. They need to ensure that only the version that passes all evaluation metrics can be deployed to a Vertex AI Endpoint for online predictions. What deployment strategy should they use?

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 actions can help reduce latency for an online prediction endpoint served by a large language model on Vertex AI? (Select TWO.)

Which THREE capabilities does Vertex AI Agent Builder provide out of the box? (Select THREE.)

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 machine learning engineer submits the above batch prediction job for a large language model. The job is expected to process 100,000 instances. The job takes much longer than expected. Which change would most likely reduce the execution time?

A developer is configuring a Vertex AI Agent Builder agent to use grounding. They receive the above error when calling the API. What is the most likely cause?

A company wants to build a chatbot that can answer questions about its internal knowledge base using natural language. Which Google Cloud Generative AI offering should they use to quickly prototype and deploy this chatbot with minimal coding?

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 team wants to fine-tune a PaLM 2 model with their own data on Vertex AI. What is the recommended way to prepare the training data?

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 data scientist is comparing two fine-tuned models on Vertex AI Model Evaluation. They want to choose the model with better factual accuracy for a medical Q&A task. Which evaluation metric should they prioritize?

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?

An organization is building a search application using Vertex AI Vector Search. They have encoded their documents into embeddings and want to retrieve the most similar documents for a query. Which TWO actions are required to set up a Vector Search index?

A company is using Vertex AI Generative AI Studio to iterate on a prompt template. They want to save and organize multiple versions of prompts. Which TWO features should they use?

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?

The exhibit shows the response from a model deployed on Vertex AI that includes safety attributes. The application must reject any prediction where the toxicity score exceeds 0.8. Based on the response, what action should the application take?

The exhibit shows a command to deploy a model to a Vertex AI endpoint with GPU. The deployment fails due to a resource constraint. What is the most likely reason?

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?

A financial institution wants to deploy a custom fine-tuned model for loan approval recommendations. They must ensure compliance with regulatory requirements, including explainability and bias monitoring. Which combination of Google Cloud services and practices best addresses these needs?

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 large enterprise is migrating their on-premise ML workloads to Vertex AI. They have a custom PyTorch model for text classification that they want to serve with minimal code changes. Which Vertex AI capability should they use for model serving?

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?

A global e-commerce company uses Vertex AI Gemini API for real-time product description generation. They observe that sometimes the model generates text in a language other than the user's language, despite being prompted in English. They need to ensure output language consistency. Which approach is most effective?

Which TWO factors are most important when choosing a base foundation model for fine-tuning on a domain-specific task?

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 using Vertex AI Model Garden to deploy a foundation model for document summarization. They notice that the model sometimes generates summaries that include factual errors. They want to reduce hallucinations without sacrificing latency. Which approach should they try first?

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 large enterprise is deploying a multi-modal generative AI application that processes customer support emails (text) and attached screenshots (images). They need to run inference on over 10,000 requests per minute with strict latency requirements (p99 < 500ms). They have already selected Gemini 1.5 Pro as the model and deployed it on Vertex AI using a GPU-based endpoint with autoscaling. During testing, they observe that the p99 latency spikes to over 2 seconds during peak traffic. The application is stateless and requests are independent. The team has access to Cloud Observability and can modify the deployment configuration. Which course of action should the team take to meet the latency requirements while minimizing cost?

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)

Which THREE of the following are features of Vertex AI Studio (Gen AI Studio)? (Choose 3)

Which TWO of the following are best practices for configuring safety settings in Vertex AI generative models? (Choose 2)

A financial services company is building a customer-facing chatbot using Vertex AI Gemini API to answer questions about account balances, transactions, and branch locations. The chatbot must adhere to strict data privacy regulations (e.g., GDPR) that prohibit sending personally identifiable information (PII) to the model provider. The architecture uses a retrieval-augmented generation (RAG) approach where customer queries are passed to a Cloud Run service, which queries a BigQuery database for relevant data and then sends the context along with the query to the Gemini API. The team is concerned that the context may contain PII. They want to minimize modifications to the existing architecture. What step should the team take to ensure compliance?

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 news organization is using Vertex AI Gemini to summarize articles. They observe that the summaries sometimes contain hallucinated facts—specifically, dates and statistics that are not in the original article. The team is using the default temperature and top_p settings. They want to reduce hallucinations without making summaries too repetitive or overly conservative. They also need to keep latency low. Which action 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 small business wants to use Vertex AI to analyze customer reviews and extract sentiment, product mentions, and overall themes. They have a small dataset of 500 reviews in a CSV file. The team is not experienced with machine learning and wants a pre-built solution that requires minimal coding. They want to start quickly and scale later. Which Google Cloud offering should they use?

A research lab is using Vertex AI to generate high-resolution medical images (2560x1920) of cell structures using Imagen. They have fine-tuned the model on their own microscope images. The generated images are sharp but often contain repeating patterns (e.g., identical cell arrangements) that are not biologically plausible. The team suspects the model is overfitting to spatial patterns in the training data. They have already tried increasing the training dataset size and augmenting it with rotations and flips. What additional technique should they try within Vertex AI?

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.)

Which command correctly updates the traffic split?

A retail company has deployed a customer support chatbot using Vertex AI Agent Builder. The chatbot is configured with a knowledge base stored in BigQuery (user manuals) and Cloud Storage (product images). The agent uses a Gemini 1.5 Pro model for response generation. Users report that the chatbot frequently gives incorrect answers and sometimes does not reference the knowledge base at all. Logs show high latency (average response time > 10 seconds) and many responses are generic or hallucinated. The agent's grounding configuration currently uses the default settings. The development team is considering the following actions: A) Switch to a smaller model like Gemini 1.5 Flash to reduce latency. B) Increase the context window of the model to allow more knowledge base content. C) Enable Vertex AI Search for grounding and configure a search aggregation strategy that retrieves relevant documents from the knowledge base. D) Fine-tune the Gemini model with the company's historical chat logs to improve domain-specific responses. Which action should the team take FIRST to address the issues?