Fundamentals of Generative AI

A startup is building a customer support chatbot using Vertex AI and wants to ground responses in their product documentation to reduce hallucinations. Which approach should they use?

A data scientist notices that a text generation model deployed on Vertex AI returns repetitive outputs after a few turns in a chat application. What is the most likely cause and the best parameter adjustment?

A financial services company wants to use generative AI to generate personalized investment advice. They must ensure responses comply with regulatory requirements (e.g., no guarantees of returns). Which Vertex AI safety feature should they primarily use?

A company is using Vertex AI to generate marketing copy. They notice that the output sometimes contains factual inaccuracies. Which parameter adjustment is most likely to improve factual accuracy?

A team is fine-tuning a large language model on custom data using Vertex AI. They find that the training loss decreases but validation loss increases. What is the best course of action?

A developer wants to generate product descriptions from a list of features using Vertex AI. Which model type is best suited for this task?

A company is using Vertex AI to generate email responses. They want to ensure sensitive customer data (PII) is not included in the output. What is the most effective approach?

Which TWO statements are true about generative AI models?

A company is deploying a generative AI model for medical diagnosis support. Which THREE considerations are critical for responsible AI?

A team is evaluating generative AI models for a content moderation system. Which THREE metrics are most important to assess?

A company wants to build a chatbot using Vertex AI that can answer customer questions based on their internal knowledge base. Which Google Cloud service should they use to store and retrieve the knowledge base efficiently?

A data scientist is fine-tuning a large language model using Vertex AI. The training job fails with an out-of-memory error. Which action should they take to resolve this issue?

A company is deploying a generative AI application that generates medical reports. They need to ensure the output is factual and minimizes hallucinations. Which approach is most effective?

A developer is using Vertex AI PaLM API to generate code snippets. The responses sometimes contain security vulnerabilities. What is the best practice to mitigate this?

A machine learning engineer is building a text-to-image model using Vertex AI. They want to reduce inference latency. Which strategy is most effective?

A company is using Vertex AI to generate personalized marketing emails. The model sometimes produces biased content. What is the most effective way to detect and mitigate bias?

Which TWO options are best practices for deploying generative AI models on Vertex AI? (Choose two.)

Which THREE factors should be considered when choosing between fine-tuning and prompt engineering for a generative AI task? (Choose three.)

A data scientist sees the above error when trying to deploy a model to an endpoint. What is the most likely cause?

A developer receives the above JSON response from a Vertex AI PaLM API call for a medical advice application. What should the developer be most concerned about?

You are an ML engineer at a retail company. You have deployed a generative AI model on Vertex AI to generate product descriptions. The model uses a custom container and is deployed to a single endpoint. Recently, you noticed that inference latency has increased significantly during peak hours, causing timeouts. You have checked the logs and found that the CPU utilization on the deployed instances is consistently above 90% during peak hours. The model is currently deployed with a single machine type (n1-standard-4) and no scaling. You need to reduce latency without incurring excessive cost. What should you do?

You are a data scientist at a financial institution. You are using Vertex AI to fine-tune a large language model (LLM) for generating financial reports. You have prepared a dataset of 10,000 examples. During fine-tuning, you notice that the training loss is decreasing steadily, but the validation loss is increasing after 5 epochs. The model's generated reports on the validation set contain many factual errors and nonsensical statements. You suspect overfitting. You have limited compute budget and need to improve generalization. What should you do?

A retail company is building a generative AI chatbot to assist customers with product recommendations and order tracking. The chatbot uses Vertex AI with Gemini 1.5 Pro, and the development team has implemented a Retrieval-Augmented Generation (RAG) pipeline using Vertex AI Search for grounding. The pipeline uses a vector store containing product descriptions and order history. During testing, the team observes that the chatbot sometimes provides incorrect order statuses—for example, claiming an order is 'shipped' when it is actually 'pending'. The team suspects the issue is related to how context is retrieved and used. The RAG pipeline currently retrieves the top 5 chunks based on cosine similarity from the vector store, and passes them as context to the model. The team is considering several changes to improve factual accuracy. Which single action would most effectively reduce hallucinations in this scenario?

A marketing team wants to generate product descriptions using generative AI. They need to ensure factual accuracy and avoid hallucinations. Which approach should they use?

A company fine-tunes a text model on internal HR policies. After deployment, the model sometimes outputs sensitive employee information. What is the most likely cause?

A developer uses the Vertex AI Python SDK to call a Gemini model for structured JSON output. However, the model often returns malformed JSON. Which parameter should the developer set in the generation configuration to enforce valid JSON output?

A graphic design company wants to generate high-quality synthetic images for product mockups. Which Google Cloud generative AI service is most suitable?

A data scientist fine-tunes a foundation model on customer support transcripts. After evaluation, the model's responses are too formal. Which adjustment during fine-tuning is most likely to make responses more conversational?

An organization uses a fine-tuned model for medical diagnosis and must comply with HIPAA. Which measure is essential when deploying the model on Vertex AI?

A prompt engineer wants to improve the model's adherence to a specific output format (e.g., always start with a greeting). Which technique should they try first?

During a RAG pipeline implementation, the retrieval system frequently returns irrelevant documents, causing the generator to produce incorrect answers. Which change is most likely to improve the relevance of retrieved documents?

A team is training a custom foundation model using JAX on TPUs on Google Cloud. They encounter frequent Out of Memory (OOM) errors. Which action is most effective in resolving the OOM error?

A data scientist is selecting a base model for generating Python code. Which TWO factors are most important to consider?

What are THREE benefits of using embedding models in a Retrieval Augmented Generation (RAG) system?

A company deploys a Gemini model on Vertex AI for a customer-facing chatbot. They observe the chatbot occasionally produces toxic language. Which TWO measures should they implement immediately to reduce toxic outputs?

Refer to the exhibit. A data scientist runs the gcloud command and sees the model listed. However, when they try to deploy the model to an endpoint, they get an error: 'Model is not deployable'. What is the most likely reason?

Refer to the exhibit. This IAM policy is applied to a Vertex AI project. A user 'test@example.com' reports they cannot create a ModelEvaluationPipelineJob. Which action should the administrator take?

Refer to the exhibit. A developer sees this error when trying to call a Vertex AI endpoint for online prediction. What permission does the requesting identity need to be granted?

A startup wants to use a pre-trained model to generate product descriptions without training. Which Google Cloud service should they use?

A data scientist fine-tunes a large language model on Vertex AI but gets poor results on validation data. What is the most likely cause?

A gen AI application produces hallucinations (factually incorrect outputs). Which mitigation strategy is LEAST effective?

What is the purpose of grounding in Vertex AI?

A company wants to build a chatbot that answers questions based on internal documents. Which approach is most appropriate?

A developer uses Vertex AI to generate code but the output is not syntactically correct. Which parameter should be adjusted?

Which Google Cloud product provides access to pre-trained foundation models like Gemini?

A company fine-tunes a model using Vertex AI and notices the model's performance drops on the original training task (e.g., language understanding) after fine-tuning for a new task (e.g., summarization). What could be the cause?

Which of the following is a best practice when using Vertex AI for prompt engineering?

Which TWO are benefits of using retrieval-augmented generation (RAG) over fine-tuning?

Which THREE are valid methods to reduce bias in generative AI outputs?

Which TWO are components of the Vertex AI Generative AI Studio?

Refer to the exhibit. A developer runs this command. What is the primary purpose?

Refer to the exhibit. A user with this IAM role tries to deploy a model to a Vertex AI Endpoint but fails. What is the most likely reason?

Refer to the exhibit. What is the most likely cause of this error?

A company wants to use generative AI to summarize customer support tickets. Which Google Cloud tool is best suited for this task?

A developer is using Vertex AI Gemini API for a chatbot. The chatbot sometimes outputs harmful content. What is the best first step to mitigate this?

A company has a large dataset of proprietary documents and wants to build a Q&A system using a foundation model without exposing the documents to the model. Which approach is most appropriate?

A data scientist notices that a Gemini model generates inconsistent responses to similar prompts. What is the likely cause?

A company wants to generate images from text descriptions using Google Cloud. Which service should they use?

A team is building a medical diagnosis assistant using a foundation model. To comply with regulations, they need to ensure the model does not make up facts. What is the best approach?

After fine-tuning a foundation model on company emails, the model outputs confidential information. What is the most likely cause?

A developer wants to quickly experiment with different foundation models available in Google Cloud. Which tool should they use?

A company is deploying a chatbot that uses a foundation model. They want to minimize latency for user queries. Which action is most effective?

A team is evaluating generative AI models on Vertex AI. They need to compare models based on specific criteria. Which TWO criteria are most important for selecting a model for a text summarization task?

A company is fine-tuning a Gemma model using Vertex AI. They observe that the model overfits. Which TWO actions should they take to mitigate overfitting?

Which THREE components are core to a typical Retrieval Augmented Generation (RAG) system?

Refer to the exhibit. A developer creates a model resource with this YAML config but gets an error that the model is not deployable. What is missing?

Refer to the exhibit. A developer sees this error when trying to deploy a model from Vertex AI Model Registry. What is the most likely cause?

Refer to the exhibit. A developer runs this command but forgets to specify the model name. What will happen?

A company is using Vertex AI to deploy a text generation model for a chatbot. They want to reduce the response latency. Which configuration change is most effective?

A data scientist needs to fine-tune a foundation model for a sentiment analysis task without managing infrastructure. Which Google Cloud service should they use?

An organization wants to ensure their generative AI application does not produce toxic or harmful content. Which Vertex AI feature should they implement?

A team uses PaLM 2 API to generate product descriptions, but the output sometimes contains factual inaccuracies. What is the best approach to improve accuracy?

A developer wants to build a RAG application using Vertex AI. Which vector database is natively integrated with Vertex AI for storing embeddings?

During model evaluation, a team observes good performance on training data but poor on validation data. Which regularization technique is most appropriate to address this?

A financial institution deploys a chatbot using Gemini Pro in Vertex AI. Compliance requires logging all user inputs and model outputs for audit. Which approach meets this requirement?

A research team is training a large language model from scratch using TPUs on Google Cloud. Which storage solution provides the highest throughput for training data?

An MLOps engineer wants to implement continuous evaluation of a generative model in production. Which Vertex AI component should they use?

A company is designing a prompt engineering strategy for a customer service chatbot using Gemini. Which two practices are recommended for improving response quality? (Choose TWO)

A developer is using Vertex AI Studio to test a text generation model. Which two actions can be performed in Vertex AI Studio? (Choose TWO)

A company is migrating an on-premises NLP pipeline to Vertex AI. Which three capabilities of Vertex AI align with common MLOps best practices for generative AI? (Choose THREE)

Refer to the exhibit. A developer executed the command to list endpoints. They notice that two models are deployed to the same endpoint. What is the most likely reason for this configuration?

Refer to the exhibit. An administrator creates this IAM policy for a Vertex AI project. What is the effect of this policy?

Refer to the exhibit. A machine learning engineer is configuring a model using this YAML. What is the purpose of the 'tuningPipeline' field?

A company wants to use a pre-trained language model for customer support summarization. They need to ensure responses are concise and accurate. Which prompt engineering technique is most effective?

A developer is using Vertex AI Generative AI Studio to fine-tune a PaLM 2 model for code generation. After training, they notice the model generates plausible but incorrect code. What is the most likely cause?

A company wants to build a chatbot that answers questions using their internal knowledge base. Which approach is most suitable?

A company is deploying a generative AI model for medical advice. What is the most important consideration?

A team uses Vertex AI to host a large language model. They want to reduce latency for real-time applications. What is the best strategy?

A company has fine-tuned a foundation model on proprietary data. During evaluation, they find the model performs well on seen examples but poorly on unseen but similar tasks. What is the problem?

Which Google Cloud service provides a managed environment for prompt engineering and model evaluation?

A company wants to generate images from text descriptions. Which model in Vertex AI Model Garden should they use?

During fine-tuning a model on Vertex AI, the job fails with error 'ResourceExhausted: Out of memory'. What is the most likely cause?

Which TWO of the following are best practices for prompt engineering?

Which THREE of the following are potential risks when deploying generative AI?

Which TWO are benefits of using pre-trained foundation models instead of training from scratch?

Refer to the exhibit. A team has deployed a model to an endpoint with the configuration shown. They notice that during peak traffic, the endpoint frequently returns 429 (Too Many Requests) errors. Which action should they take to resolve this issue?

Refer to the exhibit. A company has this IAM policy on a Vertex AI project. Alice complains she cannot create a new model. What is the most likely reason?

Refer to the exhibit. A data scientist is fine-tuning a model. The training loss and accuracy are improving each epoch. However, after training, the model performs poorly on a held-out validation set. What is the most likely issue?

A startup is building a customer service chatbot that generates responses in real-time. They want the model to have up-to-date information on the latest product catalog but cannot afford frequent fine-tuning. Which technique should they use to inject current data into the model without retraining?

A medical imaging team wants to generate synthetic X-ray images to augment a training dataset for a rare disease. Which type of generative model is most suitable for generating high-fidelity, realistic medical images?

An enterprise deploys a large language model (LLM) for internal document summarization. Users complain that summaries sometimes include statements not present in the original document. Which mitigation strategy should the team prioritize to address this hallucination issue?

A company is developing a code generation assistant and wants to ensure the model respects access control policies, e.g., it should not generate code that uses internal APIs that the user is not authorized to access. Which technique is most effective for embedding such policy constraints into the model's behavior?

A team is tuning a large language model for a question-answering task. They notice the model gives high confidence scores to answers that are factually incorrect. Which evaluation metric should they primarily use to detect this overconfidence problem?

A generative AI model is trained on a dataset containing biased text. The team wants to debias the model without significantly sacrificing performance on the original task. Which approach is most appropriate?

A multimodal generative AI system processes both image and text inputs to produce captions. During inference, the image encoder sometimes produces noisy or missing features. Which architectural design decision best handles such input degradation without retraining?

An organization wants to use a generative model to automatically generate legal contracts. The model must produce clauses that are not only grammatically correct but also legally enforceable and consistent with current jurisdiction laws. Which combination of techniques best ensures legal compliance?

Which TWO of the following are key differences between generative AI and discriminative AI? (Choose two.)

Which THREE of the following are common techniques to reduce harmful biases in generative AI models? (Choose three.)

Which THREE of the following are key considerations when deploying a generative AI model in a production environment with strict latency requirements? (Choose three.)

You are a generative AI lead at a healthcare startup developing a system to summarize patient medical records for quick review by doctors. The system uses a fine-tuned LLM. After deployment, doctors report that the summaries often miss critical details like medication dosages and allergy information. The current pipeline preprocesses patient records by extracting text from EHR, feeding it to the LLM, and outputting a summary. The team has limited time and budget. They cannot retrain the model because it is hosted as a managed API. Which action should you take to most effectively improve the summarization quality without changing the model?

You are the lead AI engineer at a financial services firm. You have fine-tuned a large language model on historical trade reports to generate daily market summaries. The model is deployed on Google Cloud's Vertex AI using a custom container. A few weeks after deployment, the operations team notices that inference latency has increased by 300%, causing timeouts. You investigate and find that the model's memory consumption has grown unexpectedly, and the GPUs are idling due to high data transfer wait times. The model architecture and code have not changed. Which action is most likely to resolve the latency issue?

You are a generative AI architect at a social media company. You are tasked with building a content moderation system that uses a generative model to flag toxic comments. The system must have very low false positive rates (i.e., not flag harmless comments) to avoid user backlash, but it must catch nearly all toxic comments. You have a large dataset of labeled toxic and non-toxic comments. You plan to use a pre-trained LLM and fine-tune it for classification. During experimentation, you notice that the model's recall for toxic comments is high (95%) but its precision is low (60%), leading to many false positives. You need to improve precision without substantially reducing recall. Which approach should you try first?

You are the AI lead at an e-commerce company that uses a generative model to write product descriptions from images and key attributes. The model is a multimodal transformer that encodes both image and text (attributes) and decodes a description. Recently, your team deployed a new version of the image encoder that uses a more powerful backbone (ViT-L instead of ViT-B). After deployment, the generated descriptions became longer but often include irrelevant visual details (e.g., background objects) and occasionally misrepresent the product's main features. The model was fine-tuned on the same dataset as before. The descriptions from the old model were concise and focused. What is the most likely cause of the degradation and the best fix?

A company is building a conversational AI using the Gemini API on Vertex AI. They want to reduce the chance of generating toxic content while still allowing creative and engaging responses for their gaming community. Which TWO safety settings should they adjust in the safety_settings parameter?

A startup is developing a customer support chatbot using Vertex AI PaLM 2 API. They notice that the model sometimes generates plausible-sounding but factually incorrect information about company policies. The chatbot currently uses no external data. To reduce these hallucinations without retraining the model, the team needs a solution that can be implemented quickly and maintains low latency. They have access to the company's internal policy database stored in Cloud SQL. Which approach should they take?

A marketing team wants to generate product descriptions using a text generation model on Vertex AI. They need consistent output style across all descriptions, including tone and length. They have a small set of 10 high-quality example descriptions that capture the desired style. The team has limited ML expertise and wants a quick solution that does not require model retraining. Which approach should they use?

A healthcare company is building a clinical decision support system using Gemini 1.5 Pro on Vertex AI. They need responses that are highly accurate and comply with medical regulations, including traceability to source documents. They have a large corpus of curated medical guidelines stored in PDFs in Cloud Storage. Their team has experience with both fine-tuning and prompt engineering. Which approach best ensures regulatory compliance and accuracy?

A company is deploying a Gemini 1.0 Ultra model for a code generation assistant. They have set up Vertex AI Model Evaluation with a custom evaluation dataset to measure pass@1 accuracy. The initial evaluation shows 65% pass@1. They want to improve to 80% without collecting more training data. They have already attempted basic prompt engineering (e.g., 'write correct code') with limited improvement. Which approach is most likely to achieve the desired improvement?

A large enterprise runs a production application that uses the Gemini API on Vertex AI for real-time content moderation. They are experiencing occasional 429 (Too Many Requests) errors during peak hours. Their current quota is 1000 requests per minute (RPM) and they are hitting around 950 RPM on average, with spikes up to 1050. They have already implemented exponential backoff and retry logic. They need to reduce the error rate without reducing the quality of moderation. Which additional measure should they take?

A financial services company is building a customer service agent using Vertex AI Agent Builder. They want the agent to only answer questions based on their approved policy documents, which are stored in Cloud Storage. They also need to ensure that the agent never reveals internal employee names or account numbers. They have set up grounding with the documents but find that the agent sometimes ignores the grounding and generates responses using the model's internal knowledge. What should they do to strictly constrain the agent to only use the provided documents?

A retail company uses the Vertex AI Gemini API to generate product descriptions. Recently, the model started producing factually incorrect statements about product specifications, such as wrong dimensions and materials. Which strategy should be implemented to improve factual accuracy?

A company is deploying a large language model (LLM) for customer support using Vertex AI. Which TWO best practices should they follow to ensure high-quality and cost-effective responses?

A developer runs the command above to test a text classification model deployed on a Vertex AI endpoint. The model returns an error. What is the most likely cause?

A healthcare company is using Vertex AI to build a generative AI assistant that helps doctors draft clinical notes. The assistant uses a fine-tuned PaLM 2 model deployed on a private endpoint. Recently, doctors have reported that the assistant takes over 30 seconds to respond, causing workflow delays. Additionally, the monthly Vertex AI costs have increased by 40% without a proportional increase in usage. The model responses are generally accurate but sometimes include irrelevant details. The company wants to improve response time and cost while maintaining acceptable quality. A review of logs shows that most requests are for similar note types (e.g., progress notes, discharge summaries) and that the same prompt is used repeatedly with minor variations. What should the company do first?