Generative AI Concepts and Technologies

A data scientist needs to generate high-quality images from text prompts using Google Cloud. Which service should they use?

A company is building a chatbot that must answer questions based on a large internal knowledge base that is updated weekly. They want to avoid retraining the model frequently. Which technique should they use?

A financial services firm wants to use Gemini to analyze customer support transcripts and generate summaries. Compliance requires that the model never output any personally identifiable information (PII). Which combination of techniques should they implement?

A developer is building a code generation assistant using Codey. They notice that the generated code sometimes contains deprecated API calls. What is the most likely cause?

A company wants to use Gemini to process invoices that contain both text and images (scanned documents). The invoices vary in layout. Which Gemini model version should they use?

A startup wants to build a text-to-speech application for generating audiobooks. Which Google Cloud generative AI service is best suited for this task?

A machine learning engineer is fine-tuning a large language model using LoRA (Low-Rank Adaptation) to reduce memory usage. During training, they notice that the model's performance on the downstream task is not improving. What is the most likely issue?

A company is using Gemini to generate marketing copy. They want the outputs to be more creative and varied. Which generation parameters should they adjust?

What is the primary benefit of using embeddings and vector search in a generative AI application?

A developer is using Gemini 1.5 Pro and needs to process a 2-hour video to answer questions about its content. The video is stored in Cloud Storage. What is the most efficient approach?

A company wants to use generative AI to create short product videos from text descriptions. Which Google Cloud service should they consider?

An AI team is choosing between supervised fine-tuning and reinforcement learning from human feedback (RLHF) for a chatbot. They want the model to follow instructions closely and avoid toxic outputs. Which statement correctly compares these approaches?

A data scientist wants to build a question-answering system over a large corpus of scientific papers. They want to minimize hallucinations and keep the knowledge current. Which TWO techniques should they combine?

A company is deploying a generative AI application that must comply with GDPR. They need to ensure user data is not used for model training and that responses do not contain personal data. Which THREE measures should they implement?

A developer wants to use Google Cloud generative AI to build a multimodal application that can answer questions about images and text. Which TWO services are most appropriate?

A company wants to build a customer service chatbot that answers questions about their internal policy documents. The documents are updated monthly, and the team cannot afford to retrain a model each time. Which approach is MOST appropriate?

What is the primary purpose of the temperature parameter when using a generative language model?

A developer is using Gemini 1.5 Flash for a real-time chat application and notices that responses are sometimes too slow. Which model parameter or configuration change would MOST likely reduce latency without significantly harming quality?

A financial services firm needs to deploy a generative AI model that generates reports from structured and unstructured data. The solution must ensure that outputs never contain sensitive customer information. Which combination of Google Cloud services should they use?

A research team wants to generate high-quality images from text descriptions for a marketing campaign. They need the ability to edit specific regions of generated images while preserving the rest. Which Google Cloud AI service should they use?

A data scientist is fine-tuning a foundation model for a specialized legal document summarization task. The labeled dataset is only 5,000 examples. Which fine-tuning technique would be MOST efficient to adapt the model without catastrophic forgetting and with minimal computational cost?

What is the fundamental difference between a foundation model and a fine-tuned model?

A company uses Gemini 1.5 Pro to analyze customer call transcripts and generate summaries. They notice that the summaries occasionally include fabricated details that were not in the transcript. Which technique is specifically designed to reduce such hallucinations?

A developer is building a multimodal application that needs to analyze images, understand spoken language, and generate text responses. Which Google Cloud generative AI model is BEST suited for this task?

An organization wants to use generative AI to automatically generate code snippets from natural language descriptions. The solution must be integrated into their existing CI/CD pipeline on Google Cloud. Which service should they use?

Which of the following best describes how large language models (LLMs) generate text?

A company is deploying a generative AI application that must comply with GDPR's right to explanation. The application must be able to justify its decisions. Which model or approach provides the MOST inherent interpretability?

A company is deploying a chatbot using Gemini 1.5 Pro. They want to reduce the risk of the chatbot generating toxic or harmful content. Which TWO techniques should they implement? (Choose two.)

A data scientist is evaluating the output quality of a text generation model. They observe that the model often repeats phrases and produces very generic responses. Which THREE parameter adjustments could help increase diversity and reduce repetition? (Choose three.)

An enterprise wants to use generative AI to help employees search through internal documents, including text, scanned PDFs, and images. They need to index the content and enable semantic search. Which TWO Google Cloud services should they use? (Choose two.)

A company wants to build a customer service chatbot that answers questions about their internal policy documents. The documents are updated monthly, and the team cannot afford to retrain a model each time. Which approach is MOST appropriate?

Which Google Cloud generative AI model is specifically designed for code generation tasks?

A healthcare startup needs to generate synthetic patient records for research. They require accurate output that adheres to medical syntax and semantics, and they must be able to explain why the model produces certain outputs for regulatory compliance. Which combination of techniques should they use?

A developer is using the Gemini API to generate product descriptions. They want the output to be more focused and less random. Which parameter adjustment would BEST achieve this?

A company wants to generate high-quality images from text descriptions for their marketing materials. They need the ability to edit specific regions of an image without regenerating the entire image. Which Google Cloud service should they use?

What is the primary function of embeddings in the context of generative AI?

A company is building a legal document review assistant using Gemini 1.5 Pro. They want to ensure the model can handle large documents of up to 500 pages in a single prompt. Which feature of Gemini 1.5 Pro is MOST important for this requirement?

A financial services firm is deploying a generative AI chatbot for customer inquiries. Due to regulatory requirements, all answers must be traceable to specific source documents and must not include information beyond those documents. Which approach BEST satisfies these requirements?

A research team wants to use Google's AI to generate video content from text prompts for a creative project. Which Google Cloud generative AI model should they use?

Which of the following best describes the transformer architecture's key innovation that enabled modern large language models?

A company is using a fine-tuned LLM for generating technical support responses. After deployment, they notice that the model sometimes produces incorrect but plausible-sounding answers (hallucinations). They have a large repository of verified technical manuals. Which technique would BEST reduce hallucinations while minimizing the need for additional training?

What is the primary purpose of the 'top-p' (nucleus sampling) parameter in text generation?

A data scientist wants to improve the performance of a text classification model for customer feedback. They have a small labeled dataset of 500 examples and a large unlabeled corpus of 100,000 feedback messages. Which TWO strategies would be most effective? (Choose 2)

A company wants to build a multimodal AI application that accepts text and image inputs and provides text responses. They need to process sensitive customer data and require that the model be hosted within their own Google Cloud project for data residency. Which TWO components are essential? (Choose 2)

A developer is using the Gemini API to generate marketing copy. They want the output to be diverse and creative but still relevant to the topic. Which THREE parameter adjustments would help achieve this? (Choose 3)

What is the primary purpose of the transformer architecture in large language models (LLMs)?

A healthcare company needs to generate synthetic medical images for research while ensuring compliance with patient privacy regulations. Which Google Cloud generative AI service should they use?

An enterprise is deploying a customer-facing chatbot using a foundation model on Vertex AI. They need to ensure the model does not produce toxic outputs. Which combination of settings and features should they implement?

A data science team wants to compare semantic similarity between thousands of customer reviews to identify emerging themes. Which Google Cloud service and approach should they use?

A developer is building an application that generates code snippets based on natural language descriptions. They want to minimize latency and cost while maintaining high accuracy. Which Google Cloud service should they choose?

A company is fine-tuning a large language model for a domain-specific legal document summarization task. They have limited labeled data but want to adapt the model efficiently without catastrophic forgetting. Which technique is most suitable?

What is the main advantage of using a model with a larger context window?

A developer is using the Gemini API for text generation and finds that the outputs are too repetitive. Which parameter adjustment is most likely to increase output diversity?

A company wants to generate high-quality product images from text descriptions for an e-commerce catalog. They need photorealistic results. Which model and approach should they choose?

An organization is building a RAG system using Vertex AI Vector Search. They notice that the retrieved documents are not relevant to the user's query. What is the most likely cause?

What is the primary benefit of using foundation models (like Gemini) as opposed to training a model from scratch?

A developer is using the Gemini 1.5 Pro model via Vertex AI and needs to process a large PDF document (500 pages) to generate a summary. The developer tries to send the entire PDF in a single prompt but gets an error. What is the most likely cause?

A company wants to build a customer service chatbot that answers questions about their internal policy documents. The documents are updated monthly, and the team cannot afford to retrain a model each time. Which approach is MOST appropriate?

What is the primary purpose of the temperature parameter in a generative language model?

A data scientist wants to generate photorealistic images of products from text descriptions for an e-commerce catalog. The images must be brand-consistent and avoid generating distorted product features. Which Google Cloud generative AI service should they use?

A developer is using the Gemini API to classify customer emails. They want to ensure that the model always returns one of three predefined labels: 'complaint', 'inquiry', or 'feedback'. Which model configuration is MOST appropriate?

Which Google Cloud service is specifically designed for generating code from natural language descriptions?

A researcher wants to adapt a large language model for a specialized medical terminology domain without retraining the entire model. Which fine-tuning method is MOST parameter-efficient?

An enterprise deploys a generative AI chatbot that must comply with GDPR right to deletion. Users can request deletion of their personal data. The chatbot uses a RAG pipeline with a vector database. What is the MOST effective way to handle deletion requests?

A startup wants to generate realistic product videos from text descriptions for social media ads. Which Google Cloud service should they use?

What is the key advantage of using vector search for retrieval in a RAG system compared to keyword search?

A team is building a multilingual customer support chatbot using Gemini. They notice that for low-resource languages, the model frequently produces grammatically incorrect responses. Which strategy would MOST effectively improve quality for these languages without sacrificing latency?

Which Google AI model was the first to demonstrate that transformers could be pre-trained bidirectionally on a large corpus, leading to major improvements in language understanding?

A company uses a Gemini 1.5 Pro model with a 1 million token context window. They want to process a large 500-page PDF for Q&A. What is the MAIN advantage of using the long context window over a RAG approach?

A machine learning engineer wants to reduce the latency of a Gemini-based chatbot running in production. Which TWO strategies would be MOST effective?

A financial institution wants to deploy a generative AI system for automated report generation. They require that the model does NOT expose sensitive information from its training data and that outputs are factually accurate. Which THREE techniques should they combine?

A team is deciding between using fine-tuning and in-context learning for a document classification task. They have 500 labeled examples and need low latency. Which TWO statements are accurate?

A company wants to build a customer service chatbot that answers questions about their internal policy documents. The documents are updated monthly, and the team cannot afford to retrain a model each time. Which approach is MOST appropriate?

What is the primary purpose of the temperature parameter when generating text with an LLM?

A data scientist is using Vertex AI to fine-tune a Gemini model for a specialized legal document summarization task. They have a small set of labeled examples (200 pairs). Which fine-tuning method is MOST cost-effective and likely to perform well?

A financial services firm is deploying a Gemini-based application that must comply with GDPR. The application processes customer queries and may include personal data. Which Google Cloud capability should the firm use to ensure that the model does not expose personally identifiable information (PII) in its responses?

In the transformer architecture, what is the role of the attention mechanism?

A developer is using Gemini 1.5 Pro and needs to process a large PDF (500 pages). The model's context window is 1 million tokens. However, the API returns an error that the input exceeds the context window. What is the most likely cause?

An e-commerce company wants to generate realistic product images from text descriptions using Google Cloud AI. Which service should they use?

A team is building a multi-modal agent that needs to accept a user's image of a handwritten note, convert it to text, and then run a sentiment analysis. They want to minimize latency and cost. Which approach is best?

What is the primary advantage of using embeddings and vector search for semantic search over traditional keyword search?

A company has a Gemini-based application that sometimes produces factually incorrect answers. They want to improve accuracy without retraining the model. Which technique should they implement?

A researcher wants to use Google's AlphaFold for a project. What is the primary capability of AlphaFold?

A startup wants to integrate a GenAI assistant into Google Workspace (Docs, Gmail, Sheets) to help employees draft emails and create charts. Which Google AI offering is designed for this purpose?

A company is building a customer support agent that can answer questions about product manuals and also generate images of the products from descriptions. Which TWO Google Cloud services should they combine? (Select 2)

A company is deploying a Gemini-based application and needs to ensure low latency for real-time user interactions. They also want to reduce cost. Which THREE strategies should they consider? (Select 3)

A data scientist wants to apply reinforcement learning from human feedback (RLHF) to improve a chatbot's helpfulness. Which TWO steps are part of the RLHF process? (Select 2)

A developer wants to generate high-quality images from text descriptions using Google Cloud. Which service should they use?

A company is using a large language model for a customer-facing chat application. They notice that the model sometimes generates plausible-sounding but incorrect information. Which strategy is most effective to reduce this issue?

A data scientist is fine-tuning a large language model for a specialized domain using limited labeled data. To avoid catastrophic forgetting and reduce computational cost, which approach is recommended?

An organization needs to deploy a generative AI application with strict compliance requirements, including data residency and auditability of model decisions. Which Google Cloud feature should they prioritize?

A developer wants to generate Python code to extract data from a CSV file using a generative AI model on Google Cloud. Which model is specifically designed for code generation?

A company is building a multilingual customer support chatbot that needs to understand and respond in 20 languages. Which Google model is most suitable for this task?

Which parameter controls the randomness of a language model's output?

A machine learning engineer wants to convert text into numerical vectors for similarity search. Which Google Cloud service should they use?

A team is using a generative AI model to create marketing copy. They want the responses to be more focused and less random. Which parameter should they adjust?

An enterprise wants to use Gemini 1.5 Flash for a real-time chat application with low latency. Which trade-off should they expect compared to Gemini 1.5 Pro?

Which Google AI milestone introduced the Transformer architecture that underpins modern LLMs?

A company wants to generate a video from a text description using Google Cloud. Which service is designed for this?

A data scientist is building a RAG pipeline for a legal document retrieval system. Which TWO components are essential for this system? (Select two.)

A company needs to deploy a generative AI application on Google Cloud that meets data residency requirements. Which THREE features should they enable? (Select three.)

A team is evaluating whether to use reinforcement learning from human feedback (RLHF) or in-context learning for a chatbot. Which TWO statements correctly describe trade-offs? (Select two.)

A company wants to build a customer service chatbot that answers questions about their internal policy documents. The documents are updated monthly, and the team cannot afford to retrain a model each time. Which approach is MOST appropriate?

What is the primary purpose of the temperature parameter when configuring a generative AI model?

A research team is using Imagen to generate images for a marketing campaign. They notice that the generated images sometimes contain distorted faces or unnatural object placements. They want to improve consistency without sacrificing image diversity. Which approach should they try first?

An enterprise is deploying a generative AI solution that must comply with GDPR data residency requirements. They plan to use Vertex AI with Gemini. Which configuration is necessary?

What is the key advantage of using adapter-based fine-tuning methods like LoRA compared to full fine-tuning of a large language model?

A developer is building a code‑generation assistant using the Codey API on Vertex AI. The assistant should generate Python functions based on natural language descriptions. However, the generated code sometimes contains syntax errors. Which parameter adjustment would MOST directly help reduce syntax errors?

A data scientist is using Vertex AI to fine‑tune a PaLM 2 model for a legal document summarization task. They have 10,000 labeled document‑summary pairs. After supervised fine‑tuning, the model performs well on the training set but often hallucinates names and dates on unseen documents. Which next step is MOST likely to improve factual accuracy?

A financial institution wants to use Gemini to analyze customer support transcripts and generate summaries. They need to ensure that personally identifiable information (PII) is not included in the summaries. Which approach should they take?

Which statement best describes the difference between the Gemini Flash and Gemini Pro models on Vertex AI?

A startup is building a multimodal application that allows users to upload a photo of a plant and ask questions about its care. They want to use Google Cloud generative AI services. Which combination of services is MOST suitable?

A company is deploying a generative AI application using Vertex AI. They need to minimize latency for real‑time inference while maintaining high quality. Which TWO actions are most effective?

A data scientist is evaluating how to ground a generative AI model to reduce hallucinations when answering questions about a private knowledge base. Which TWO techniques are most suitable?

Which THREE of the following are generative AI modalities supported by Google Cloud services?

A company is fine‑tuning a large language model for a domain‑specific task. They have a limited budget and want to minimize the cost of fine‑tuning. Which TWO approaches are most cost‑effective?

An organization is building a multi‑agent workflow on Vertex AI where one agent analyzes an image (e.g., a scanned contract), another agent extracts text from the image, and a third agent answers questions about the contract. The solution must be low‑latency. Which THREE services are most appropriate?

A company wants to build a customer service chatbot that answers questions about their internal policy documents. The documents are updated monthly, and the team cannot afford to retrain a model each time. Which approach is MOST appropriate?

A data scientist is fine-tuning a large language model for a legal document summarization task. The dataset contains only 500 examples, and the model must not forget its general language capabilities. Which fine-tuning method is most suitable?

A media company wants to generate video content from text descriptions. They need a Google Cloud solution that can produce high-quality videos with realistic motion. Which TWO services should they consider?

A developer is using the Gemini API to generate text summaries. They want to control the creativity and diversity of the output. Which THREE parameters can they adjust?

A healthcare organization wants to use generative AI to draft patient education materials. They are concerned about the model generating incorrect medical information. Which combination of Google Cloud services should they use to ground the model's responses in trusted medical literature?