Google Cloud · Free Practice Questions · Last reviewed May 2026
24real exam-style questions organised by domain, each with the correct answer highlighted and a plain-English explanation of why it's right — and why the others are wrong.
30% of exam · 6 sample questions below
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?
Enable Vertex AI Grounding with a custom enterprise data store containing the documentation.
Grounding ties responses to specific documents, reducing hallucinations.
Use the Codey API for text generation.
Use the base model without any grounding to maximize flexibility.
Fine-tune the model on the documentation and deploy.
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?
The max_output_tokens is too low; increase it to allow more diverse output.
The top_p value is too high; reduce top_p to limit token sampling.
Reducing top_p narrows the token pool, reducing repetition.
The model is overfitted; switch to a smaller model.
The temperature is too low; increase temperature to add randomness.
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?
Vertex AI Grounding with their compliance database.
Prompt engineering with instructions to avoid guarantees.
Safety filters with a custom blocklist that includes phrases like 'guaranteed return'.
Safety filters can block defined categories or custom phrases.
Reinforcement learning from human feedback (RLHF) on the model.
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?
Decrease the temperature parameter.
Lower temperature reduces randomness, making output more factual.
Increase the max_output_tokens parameter.
Increase the top_p parameter.
Add a post-processing step to verify facts using a database.
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?
Increase the number of training epochs.
Reduce the model size or add dropout regularization.
Regularization techniques combat overfitting.
Increase the learning rate.
Switch to a smaller batch size.
A developer wants to generate product descriptions from a list of features using Vertex AI. Which model type is best suited for this task?
An embedding model (e.g., textembedding-gecko@001).
A chat model (e.g., chat-bison@001).
A text generation model (e.g., text-bison@001).
Text generation models are ideal for generative tasks from prompts.
A code generation model (e.g., code-bison@001).
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Practice this domain15% of exam · 6 sample questions below
A retail company wants to deploy a generative AI chatbot to assist customers with product recommendations. The chatbot must align with the company's brand voice and provide accurate, up-to-date information. Which strategy should the company prioritize when developing this solution?
Ground the model with proprietary product data and brand guidelines in a retrieval-augmented generation (RAG) architecture.
RAG with curated data ensures responses are accurate, up-to-date, and on-brand.
Use a generic pre-trained model without customization to reduce development time.
Deploy a large language model with a feedback loop to iteratively improve responses.
Train the model on public customer reviews to capture common preferences.
A global financial services firm wants to deploy generative AI for personalized investment recommendations. They must comply with regulations in multiple jurisdictions, including GDPR and the SEC's Marketing Rule. The solution must also be auditable. Which approach best balances regulatory compliance, scalability, and cost?
Build a centralized model in a cloud region with the most stringent regulations and apply it globally.
Use a single global model with a unified compliance layer applied post-generation.
Deploy separate, jurisdiction-specific models with tailored guardrails and audit trails for each region.
This ensures compliance with local regulations and provides auditable logs.
Rely on a third-party API with built-in compliance for all regions.
A company is evaluating whether to build a custom generative AI solution from scratch or use a pre-built API from a cloud provider. Which factor most strongly supports the build-from-scratch approach?
The team has limited machine learning expertise.
Speed to market is the top priority.
Minimizing initial development cost is critical.
The solution requires deep integration with proprietary data and unique domain-specific outputs.
Custom models can be fine-tuned on proprietary data for unique needs.
A media company uses generative AI to produce personalized news summaries. They notice that summaries occasionally contain factual errors and biased language. What business strategy should they implement to address these issues while maintaining user engagement?
Disable personalization and serve generic summaries to all users.
Allow users to flag errors and manually correct summaries in real-time.
Implement a human review layer for high-risk topics and use automated fact-checking for all content, with a feedback loop for model improvement.
This ensures accuracy and allows continuous improvement.
Replace AI with entirely human-written summaries.
A manufacturing company wants to use generative AI to create maintenance manuals from sensor data. The manuals must be accurate and reflect the latest equipment configurations. Which approach best ensures data freshness and consistency?
Train the model in real-time as sensor data streams in.
Periodically retrain the model with the latest sensor data.
Have human technicians review and update the manuals manually.
Use a retrieval-augmented generation (RAG) system that queries a live database of sensor configurations.
RAG ensures responses are based on the most current data.
A company is adopting generative AI for customer support. Which TWO strategies should they implement to manage risks related to brand reputation?
Establish a human-in-the-loop escalation process for sensitive interactions.
Human oversight ensures appropriate handling of sensitive issues.
Publish a disclaimer that the AI may make mistakes.
Implement automated monitoring for toxic or off-brand language.
Monitoring helps catch issues before they reach customers.
Deploy the model without any content filters to maximize helpfulness.
Disable customer support AI entirely to avoid any risk.
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Practice this domain35% of exam · 6 sample questions below
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?
Vertex AI Model Garden with fine-tuning
Vertex AI Search with document grounding
Supports private document indexing and data residency controls.
Vertex AI Agent Builder with web search
Vertex AI Codey APIs
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?
Vertex AI Studio
No-code prompt engineering and testing.
Vertex AI Workbench with custom training
Vertex AI Agent Builder
Vertex AI Model Garden
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?
Switch to a smaller base model like PaLM 2 Bison
Enable context caching to reuse previous responses
Set max output tokens to a lower value and use more precise prompts
Directly reduces output tokens; precise prompts maintain accuracy.
Reduce temperature to 0.0
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?
Vertex AI Model Garden
Vertex AI Agent Builder
Provides tools for building chatbots with backend integration.
Vertex AI Search
Vertex AI Codey API
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?
Enable response caching
Increase the temperature parameter
Use Vertex AI grounding with a knowledge base of verified entities
Grounding supplies factual context to the model.
Decrease top_p to 0.3
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?
Set the temperature to 0.0
Adjust safety settings to block medium-probability harmful content
Stricter thresholds block more offensive outputs.
Enable context caching
Fine-tune the model on customer feedback data
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Practice this domain20% of exam · 6 sample questions below
A team is building a generative AI model for customer support. They notice the model often produces overly polite but unhelpful responses. Which technique would best improve response quality without sacrificing helpfulness?
Apply reinforcement learning from human feedback (RLHF)
RLHF tunes the model to align with desired response characteristics.
Increase the amount of training data
Lower the top_k sampling value
Increase the temperature parameter
A generative AI model for code generation sometimes produces syntactically incorrect code. The team wants to reduce syntax errors without retraining the entire model. Which approach is most effective?
Implement constrained decoding with grammar rules
Constrained decoding ensures output respects syntax rules.
Run a syntax checker after generation and regenerate
Add a system prompt that instructs the model to produce valid code
Increase beam search width
A generative AI model for chatbot responses sometimes produces toxic language. The team wants to reduce toxicity without significantly affecting the model's helpfulness. Which approach is best?
Increase the temperature parameter
Reduce the maximum output tokens
Fine-tune with a dataset of non-toxic responses and use RLHF
Fine-tuning combined with RLHF aligns model behavior effectively.
Apply a toxicity classifier as a post-processing filter
A company uses a generative model to produce product descriptions. The descriptions are factually inconsistent with the product specs. Which technique would best ensure factual accuracy?
Enhance the system prompt with product details
Implement retrieval-augmented generation (RAG) with product database
RAG grounds generation in factual data.
Lower the temperature to 0.0
Fine-tune the model on product descriptions
A team is fine-tuning a large language model for medical advice. Which TWO techniques are most effective for improving the safety and reliability of the model's outputs?
Constitutional AI
Constitutional AI uses predefined rules to guide model behavior.
Lowering the temperature to 0.0
Increasing training data size
Increasing top_p to 1.0
Reinforcement learning from human feedback (RLHF)
RLHF aligns model with human preferences for safety.
A team wants to reduce hallucinations in a question-answering model. Which THREE techniques should they consider?
Fine-tune the model on a curated factual dataset
Fine-tuning on factual data improves accuracy.
Use retrieval-augmented generation (RAG)
RAG grounds answers in retrieved documents.
Apply prompt engineering with specific instructions to cite sources
Prompts can encourage factual responses.
Reduce the number of tokens in output
Increase the temperature parameter
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Practice this domainThe Generative AI Leader exam has 50 questions and must be completed in 90 minutes. The passing score is 700/1000.
Scenario-based questions covering exam objectives with detailed answer explanations.
The exam covers 4 domains: Fundamentals of Generative AI, Business Strategies for Generative AI Solutions, Google Cloud's Generative AI Offerings, Techniques to Improve Generative AI Model Output. Questions are weighted by domain — higher-weight domains appear more on your actual exam.
No. These are original exam-style practice questions written against the official Google Cloud Generative AI Leader exam objectives. They are not copied from the real exam. Courseiva focuses on genuine understanding, not memorisation of braindumps.
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