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 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?

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 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?

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

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?

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?

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

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. 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?

A developer runs this command: `gcloud ai models upload --region=us-central1 --display-name=my-model --artifact-uri=gs://my-bucket/model.pkl`. What is the primary purpose?

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 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?

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

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 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 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?

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 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?

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?

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 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?

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 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 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?

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 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 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?

A marketing team wants to use a generative AI model to create blog post drafts from short product descriptions. They need the model to produce varied, creative text each time they run it. Which configuration parameter should they adjust to control the randomness of the output?

A marketing team wants to generate product descriptions from a short list of features using a Google Cloud generative AI model. They have no labeled examples and want to avoid any model training. Which approach should they use?

A marketing team at a retail company is using a generative AI model on Vertex AI to produce product descriptions from short bullet lists. They observe that the model's outputs are fluent but frequently invent specifications, such as claiming a jacket is waterproof when no such attribute was provided. The team wants to reduce these fabrications without retraining the model. Which approach should they take?

A software team is using a generative AI model to write code snippets. They want to control the model's creativity and ensure it produces consistent, deterministic output for the same prompt. Which parameter should they adjust?

A marketing team wants to generate product descriptions from a short bullet list of features. They need a Google Cloud service that provides a web-based console for writing prompts, comparing model responses, and adjusting parameters like temperature without writing code. Which service should they use?

A support team is using a generative AI model to answer customer questions from an internal knowledge base. They notice that when the same question is asked twice, the model sometimes gives different answers, and occasionally includes details not found in the knowledge base. They want to reduce variability and keep responses closer to the source content. Which action should they take?

A retail company wants to use a generative AI model to create unique product descriptions for thousands of items. They need the model to produce varied, human-like text without being explicitly programmed for each product. Which core capability of generative AI does this scenario primarily rely on?

A marketing team wants to generate social media posts with Google Cloud's generative AI, but they have no machine learning experience. They need a no-code interface to experiment with prompts and tune settings like temperature. Which Google Cloud service should they use?

A marketing team is using Google Cloud's Vertex AI Studio to generate product descriptions. They want the model to produce output in a very specific JSON format so it can be parsed by their downstream application. Which feature should they use to reliably constrain the model's output to that structure?

An enterprise wants to use a foundation model through Google Cloud but must ensure that their prompts and responses are not used to train the underlying model and that data is encrypted in transit and at rest. They also want to avoid managing infrastructure. Which approach best meets these requirements?

A media company is using a generative AI model to create short video scripts. They notice that the model sometimes produces content that is factually incorrect or nonsensical. Which term best describes this phenomenon?

A retail company wants to build an internal tool that generates short product descriptions from bullet points. They have no machine learning engineers on staff and want to avoid managing any infrastructure. Which Google Cloud approach best fits their need?

A developer is building a customer support chatbot using Gemini models on Vertex AI. The chatbot must respond in a consistent, professional tone and avoid generating harmful or off-topic content. Which approach should the developer take to enforce these behavioral constraints?

A retail company wants to build an internal assistant that answers employee questions using the company's HR policy documents. The documents change frequently, and the company does not want to retrain a model every time a policy is updated. Which approach best meets these requirements on Google Cloud?

A team is designing prompts for a generative AI application that summarizes long legal contracts. They want outputs that are accurate, consistently formatted, and safe from leaking instructions. Which two prompt engineering practices should they apply? (Choose two.)

A healthcare organization wants to build a generative AI application that summarizes patient notes. They are concerned about the model generating harmful or inappropriate content. Which Google Cloud service should they use to filter out such content in real time?

A retail company wants to build an internal assistant that answers employee HR policy questions using their existing policy documents, without exposing sensitive data to external APIs. They need a managed Google Cloud service that supports retrieval-augmented generation (RAG) and integrates with their existing data in Cloud Storage. Which Google Cloud service should they use?

A developer is experimenting with a Gemini model in Vertex AI Studio and notices that for the same prompt, the model produces different answers each time. The developer needs the output to be as consistent and deterministic as possible for a classification task. Which parameter change should they make?

A media company is using a foundation model in Vertex AI to summarize long articles. They notice that summaries sometimes omit key details from the middle of very long articles. Which action should they take to improve summary completeness while staying within the model's context window?

A retail company wants to build a generative AI application that creates personalized product descriptions. They need the model to stay current with weekly inventory changes and brand voice guidelines without retraining the model. Which approach should they use?

A financial services firm is deploying a generative AI model to answer customer queries about investment products. They want to ensure the model's responses are based on the most current and authoritative internal documents. Which technique should they implement?

A financial analyst wants to quickly generate a first draft of a market commentary from a few key statistics using a Google Cloud generative AI model. They have no coding experience and want to see the model's response immediately while adjusting the prompt. What should they do first?

A marketing team is using Vertex AI's text generation model to create product descriptions. They want to control the randomness of the output to ensure consistent, focused messaging for a new product line. Which parameter should they adjust to reduce randomness and make the output more deterministic?

A financial services company wants to build a generative AI application that drafts personalized emails to clients. They require the model to be accessible via a fully managed API with minimal infrastructure management. Which Google Cloud service should they use?

A financial services firm wants to deploy a generative AI assistant for employees. The assistant must not reveal any customer data and must comply with internal policies. Which Google Cloud capability should they use to control the assistant's responses based on defined safety and privacy rules?

A media company is deploying a generative AI application that must handle bursts of traffic while keeping costs predictable. They want to pay only for the compute resources actually consumed and avoid managing infrastructure. Which Google Cloud option best matches these requirements for serving a foundation model?

A marketing team wants to use a generative AI model to create product descriptions from a short list of features. They need the output to be creative but also follow a specific brand voice. Which approach should they take?

A marketing team is using a generative AI model to create ad copy. They notice that the outputs are often too creative and sometimes include exaggerated claims. They want to reduce creativity and make the outputs more predictable and factual. Which parameter should they adjust?

A company is evaluating Google Cloud's generative AI offerings to build a custom chatbot that can answer questions about their proprietary product manuals. They want to ensure the solution can be grounded in their own data and can be integrated into their existing applications. Which two Google Cloud services should they consider? (Choose two.)

A marketing team is using a generative AI model to create ad copy. They want to ensure the outputs align with their brand voice and avoid offensive language. Which two practices should they adopt? (Choose two.)

A marketing team is using a generative AI model to create ad copy. They notice that the outputs sometimes include made-up statistics and false claims about their products. They want to reduce these hallucinations without retraining the model. What should they do?

A healthcare company is evaluating foundation models for a patient triage assistant. They need to ensure the model is appropriate for medical use and that they can control costs. Which two factors should they consider when selecting and using a foundation model in Vertex AI? (Choose two.)

A team is preparing to use Vertex AI Studio to prototype a generative AI application. They want to understand which factors directly influence the quality and safety of the model's responses. (Choose two.)

A retail company wants to use a generative AI model to create unique product descriptions for thousands of items. They need the model to produce varied, creative text without requiring them to provide any examples. Which type of model should they use?

A healthcare company is using a generative AI model to summarize patient records. They are concerned about the model generating incorrect medical information. They want to ensure that the summaries are grounded in the provided records and do not include hallucinations. Which technique should they implement?

A data scientist is using Vertex AI to generate product descriptions from a list of features. They notice that the model sometimes omits key features or invents details not present in the input. They want to reduce hallucinations and ensure all provided features are included. Which technique should they apply?

A retail company wants to use a generative AI model to create product descriptions from a list of attributes. They want a fully managed service that allows them to quickly experiment with prompts and adjust parameters without managing infrastructure. Which Google Cloud service should they use?

A financial services company is using a large language model on Vertex AI to generate summaries of earnings reports. They notice that the model sometimes includes information not present in the source reports, leading to compliance risks. They want to reduce hallucinations by ensuring the model's output is grounded in the provided documents. Which technique should they implement?

A team is designing prompts for a generative AI model to summarize legal documents. They want to improve the quality and relevance of the summaries. Which two prompt engineering best practices should they follow? (Choose two.)

A marketing team wants to use a generative AI model to create dozens of unique social media captions for a new product launch. They need the outputs to vary in tone and wording each time they run the prompt, while staying on topic. Which model parameter should they adjust to directly control this variability?

A media company is using a generative AI model to produce news summaries. They notice that the summaries sometimes include fabricated details not present in the source articles. Which approach should they take to reduce these hallucinations?

A financial services firm is deploying a generative AI model to answer customer questions about investment products. They need to ensure that the model's responses comply with regulatory requirements and do not provide personalized financial advice. Which approach should they take?

A marketing team wants to use a generative AI model to create ad copy in multiple languages. They are new to Google Cloud and want the fastest way to start experimenting without writing code. Which tool should they use?

A developer is deploying a generative AI model on Vertex AI for a production application that requires low latency and high throughput. They need to choose an appropriate endpoint type. Which deployment option should they use?

A media company is using a generative AI model to create summaries of news articles. They notice that the summaries sometimes include information not present in the original articles, leading to factual errors. They want to reduce the likelihood of such hallucinations. Which approach should they take?

A marketing analyst at a retail company wants to use a generative AI model to draft product descriptions for 500 new items. The analyst expects the model to understand the context of each product from a short prompt and produce varied, coherent text. Which core capability of generative AI is the analyst primarily relying on?

A developer is building a generative AI application using Vertex AI and wants to ensure that the model's responses are safe and aligned with their company's content policies. They need to filter out harmful content such as hate speech and harassment from both user inputs and model outputs. Which Vertex AI feature should they configure?

A retail company wants to build an assistant that can answer customer questions about its product catalog, but the catalog changes daily. The team wants the model to reference the latest product information without retraining. Which approach should they use?

A company is deploying a generative AI model on Vertex AI for real-time chat. They observe that the model sometimes generates toxic or biased responses. They want to implement a safety mechanism to filter out such content before it reaches users. Which Google Cloud feature should they use?

A media company wants to generate personalized video summaries for users based on their viewing history. They plan to use a generative AI model on Vertex AI. Which technique should they use to ensure the summaries are tailored to each user's preferences?

A financial services company is deploying a generative AI model to summarize sensitive customer emails. The security team requires that no email content is used to train or improve the underlying foundation model. Which Google Cloud approach ensures this requirement is met?

A marketing team is using a generative AI model to create ad copy. They want to control the model's creativity so that outputs are more focused and deterministic for a formal campaign. Which parameter should they adjust?

A marketing team wants to generate product descriptions from a short bullet list of features. They need the model to produce creative, varied phrasing rather than a single deterministic output, while keeping the text grammatically correct. Which Vertex AI generative AI parameter should they adjust to introduce randomness into the model's word choices?

A marketing team uses a generative AI model to produce ad copy. They notice that when they set the temperature parameter to 0.1, the outputs are very similar across runs, but when they set it to 0.9, the outputs vary widely. They want a balance between creativity and consistency for a campaign that requires some variation but also brand alignment. Which temperature value should they choose?

A financial services firm is deploying a generative AI chatbot on Vertex AI. They need to ensure that the model's responses comply with strict regulatory requirements and do not include personally identifiable information (PII). They want to automatically filter out any PII from both user inputs and model outputs. Which Google Cloud service should they integrate?

A developer is using Vertex AI's text generation model to create product descriptions. They want to ensure the output adheres to a specific brand voice and style. Which approach is most effective without retraining the model?

A junior developer is experimenting with a large language model and notices that the same prompt produces different outputs each time it is run. Which characteristic of generative AI models explains this behavior?

A marketing team is using a generative AI model to create ad copy. They want to control the randomness of the output so that the same prompt produces consistent results for A/B testing. Which parameter should they adjust?

A hospital wants to use a generative AI model to draft patient discharge summaries. The model must be able to understand medical terminology and generate coherent, context-aware text. Which type of generative AI model is most suitable?

A media company is using a large language model to generate scripts for animated shorts. They notice that the model sometimes produces content that closely resembles existing copyrighted scripts from well-known movies. Which technique should they implement to reduce the risk of generating such content?

A retail company wants to build an internal assistant that answers employee questions using the company's own HR policy documents. The team has no machine learning engineers and wants a managed Google Cloud approach that grounds responses in those documents without training a new foundation model. Which Google Cloud capability best fits this need?

A developer is using a generative AI model to summarize long articles. They want to ensure the summaries are concise and do not exceed a certain length. Which parameter should they adjust to control the maximum length of the generated summary?

A healthcare company is building a generative AI assistant to answer patient questions about medications. The team wants to ensure the model's responses are grounded in approved clinical guidelines and avoid fabricated information. Which approach best addresses this requirement?

A financial services firm is designing a generative AI assistant for advisors. Compliance requires that every response be traceable to an approved source document and that no response rely on the model's pretrained knowledge alone. The team plans to use Gemini models on Vertex AI. Which design choice most directly satisfies the traceability requirement?

A financial services firm is deploying a generative AI chatbot to answer employee questions about internal policies. The firm must ensure that the chatbot does not reveal sensitive information from other departments. Which technique should they implement?

A healthcare startup is building a generative AI application to draft patient education materials. They need to ensure the outputs are accurate, up-to-date, and tailored to each patient's condition. Which two techniques should they use to ground the model's responses in reliable medical knowledge? (Choose two.)

A team is designing a generative AI application that must avoid generating harmful or biased content. They are considering various techniques to implement safety measures. Which two approaches are recommended for mitigating harmful outputs in generative AI models? (Choose two.)

A retail company wants to use a generative AI model to create personalized product descriptions for thousands of items. They need the descriptions to be consistent in style and format, but they also want to avoid the model inventing false features. Which approach best balances consistency and accuracy?

A product team is new to generative AI and wants to understand which characteristics distinguish foundation models from traditional task-specific ML models. Which two statements accurately describe foundation models? (Choose two.)

A healthcare company is using a generative AI model to summarize patient notes. The model occasionally includes hallucinated medical details. Which strategy best reduces hallucinations?

A company wants to use a generative AI model to create product descriptions from a list of features. They need the model to consistently follow a specific format: a headline, followed by three bullet points, and a closing sentence. Which technique should they use to guide the model's output structure?

A marketing team is using a generative AI model to create ad copy. They want to ensure the output is creative and diverse, but also relevant to the product. They decide to adjust the model's parameters. Which parameter should they increase to make the output more diverse?

A healthcare analytics team is evaluating a generative AI model for summarizing clinical notes. They observe that summaries are fluent but occasionally invent details not present in the source notes. They want a practical mitigation that reduces fabricated content without retraining the model. Which approach should they apply first?

A financial analyst uses a generative AI model to answer questions about recent market trends. They notice that the model sometimes provides outdated information. They want to ensure the model's responses are based on the most current data. Which approach should they take?

A product team is designing a generative AI application to create personalized email campaigns. They want to ensure the model produces high-quality, relevant content while minimizing risks such as bias and harmful outputs. Which two practices should the team implement? (Choose two.)