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.
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 domainA 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 healthcare organization is developing a generative AI system to assist doctors with clinical decision support. They are concerned about regulatory compliance (e.g., HIPAA) and potential liability. What is the most important business strategy to mitigate these risks?
Limit the system to non-critical administrative tasks only.
Use an open-source model to avoid vendor lock-in and reduce costs.
Fully automate the system to reduce human error.
Implement a human-in-the-loop review process with clear accountability for AI-generated recommendations.
Human oversight ensures compliance and provides a clear chain of responsibility.
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 startup is building a generative AI content creation tool. They want to minimize operational costs while maintaining low latency for end users. Which deployment strategy should they adopt?
Deploy the model on edge devices to reduce cloud dependency.
Build an on-premises infrastructure to avoid cloud egress fees.
Use a serverless inference endpoint that scales to zero when not in use.
Serverless aligns cost with usage and auto-scales to meet demand.
Provision dedicated GPU instances for consistent performance.
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.
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Practice this domainA 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 domainA 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 company uses a text-to-image model to generate marketing visuals. The outputs often contain distorted human faces. Which technique is most likely to improve face generation?
Fine-tune the model on a curated dataset of human faces
Fine-tuning specializes the model for better face generation.
Increase the output resolution
Increase the number of inference steps
Reduce the classifier-free guidance scale
A team is deploying a large language model for legal document summarization. They find the model occasionally omits critical legal clauses. Which improvement technique would be most effective?
Design a prompt that explicitly lists required sections
A structured prompt with requirements improves completeness.
Increase the top_p value to 1.0
Fine-tune the model on legal summaries
Lower the temperature to 0.1
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 team notices their text generation model repeats phrases excessively. Which technique would most directly reduce repetition?
Use beam search with a beam width of 5
Apply a repetition penalty of 1.2
Repetition penalty directly discourages repeated tokens.
Increase top_k to 100
Lower temperature to 0.5
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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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