Generative AI Leader Practice Test 22 — 15 Questions

Question 1

During a proof-of-concept for a GenAI document summarization tool, the team wants to evaluate whether the summaries are accurate and retain key information before scaling. Which evaluation approach is most appropriate for this stage?

Choose an answer to begin — your selection is scored in the full session.

Accepted Answer

Run an A/B test with a small user group and have domain experts manually review a sample of summaries for accuracy. A/B testing with manual review by domain experts provides qualitative and quantitative feedback on accuracy and completeness, which is crucial for a pilot. Automated metrics alone may not capture business relevance.

Answer

Measure latency and cost as the primary evaluation metrics

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Deploy to all users and collect feedback via a survey

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Use ROUGE scores exclusively to compare summaries against human-written ones

Question 2

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

Accepted Answer

Provide context and examples in the prompt. Clear and specific instructions help guide the model, and providing context and examples improves output quality. Options B, D, and E are not recommended.

Answer

Append random noise to prompts to improve creativity

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Always use the maximum possible number of tokens

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Use negative prompts to discourage undesired outputs

Question 3

A data science team wants to build a custom model for generating product descriptions that adhere to specific brand guidelines. They have 5,000 high-quality examples. Which approach balances cost and accuracy?

Accepted Answer

Fine-tune a foundation model (e.g., PaLM 2) using Vertex AI Model Garden. Fine-tuning a foundation model on the examples yields high accuracy with moderate cost. Training from scratch is overkill; prompt engineering may not capture all nuances.

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Use a pre-built API with prompt engineering and few-shot examples

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Use Vertex AI Agent Builder with a custom prompt

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Train a model from scratch using TensorFlow on Vertex AI

Question 4

A company is deploying a generative AI system for medical diagnosis support. To comply with Google's AI Principles and regulatory requirements, which TWO actions are essential? (Select 2)

Accepted Answer

Implement a human-in-the-loop review for all diagnostic suggestions. High-stakes medical decisions require human oversight (Principle: be accountable to people) and data privacy (Principle: incorporate privacy design principles). The other options are beneficial but not essential for compliance.

Answer

Publish a Model Card for the model

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Use SynthID to watermark all output

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Use a larger model to improve accuracy

Question 5

A project manager wants to automatically generate weekly status reports from meeting notes and project data. The team uses Google Workspace. Which built-in capability is the QUICKEST to implement?

Accepted Answer

Use Gemini for Workspace (Duet AI) in Google Docs and Google Meet to generate summaries and reports. Gemini for Workspace (Duet AI) can generate summaries directly in Google Docs and Meet, leveraging existing data with no custom development. Custom API integration or Model Garden would require more effort. Apps Script is for custom automation, not built-in.

Answer

Select a model from Model Garden and deploy it as a private endpoint for report generation

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Use Vertex AI Studio to design a custom prompt and call the Gemini API from a custom app

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Write a Google Apps Script to call the Gemini API and format the report

Question 6

A data scientist is evaluating a generative AI model for potential gender bias in its outputs. They use a diverse test set that includes names, pronouns, and occupations across genders. Which Google's AI Principle does this practice primarily support?

Accepted Answer

Avoid creating or reinforcing unfair bias

Answer

Incorporate privacy design principles

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Be built and tested for safety

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Be socially beneficial

Question 7

Which Vertex AI feature allows a business user to explore and test over 300 foundation models from Google and partners, including Gemma, Llama, and Claude, without writing code?

Accepted Answer

Model Garden. Model Garden is the central hub in Vertex AI that provides access to a wide variety of foundation models for exploration and testing.

Answer

Vertex AI Pipelines

Answer

Vertex AI Studio

Answer

Vertex AI Agent Builder

Question 8

An e-commerce company is using a generative AI model to write product descriptions. They want to ensure that the model does not generate harmful content such as hate speech or violence. Which Google Cloud feature should they configure?

Accepted Answer

Vertex AI Safety Filters. Google Cloud's Vertex AI provides built-in safety filters that can be configured to block harmful content categories like hate speech, violence, sexual content, and dangerous instructions.

Answer

Cloud DLP (Data Loss Prevention)

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Vertex AI Explainable AI

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Vertex AI Model Monitoring

Question 9

A healthcare organization wants to use generative AI to draft email responses to patient inquiries. They need to ensure that the model never generates medical advice and always includes a disclaimer. Where should they enforce these constraints?

Accepted Answer

Configure safety settings and system instructions in the Vertex AI API call. Safety settings and system instructions are applied at the API call level to constrain model behavior. Fine-tuning would embed the rules but is heavy. Grounding doesn't enforce constraints. Model Garden is for model selection.

Answer

Select a model from Model Garden that is pre-trained on medical data

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Fine-tune the model with a dataset that only includes disclaimers

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Use Grounding with Google Search to restrict knowledge to approved sources

Question 10

A developer is using Vertex AI Studio to experiment with prompts. They want to ensure that the model's responses are grounded in factual information from a trusted knowledge base. Which feature should they enable?

Accepted Answer

Grounding with a Vertex AI Search data store. Vertex AI's grounding feature allows the model to cite sources from a provided knowledge base, improving factual accuracy and verifiability.

Answer

Safety filters

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Temperature setting reduction

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Chain-of-thought prompting

Question 11

A developer wants to add a GenAI feature to their existing web application. They need to integrate with the app's backend using REST APIs. Which integration pattern is MOST appropriate?

Accepted Answer

Integrate via Vertex AI API or Gemini API. API-first integration using Vertex AI API or Gemini API is the standard way to add GenAI to existing applications. Workspace add-ons are for Google Workspace apps. Apps Script is for automating Workspace. Agent Builder is for building conversational agents, not general API integration.

Answer

Use Apps Script to call the Gemini API

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Use Vertex AI Agent Builder to create an agent and embed it via iframe

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Build a Google Workspace add-on

Question 12

A company is developing an AI-powered interview assistant that screens job applicants. The responsible AI team wants to ensure the model does not discriminate based on gender, race, or age. Which TWO practices should they implement?

Accepted Answer

Regularly evaluate the model's outputs for bias using intersectional test sets.

Answer

Deploy the model without human oversight to ensure consistency.

Answer

Use SynthID watermarking on all model outputs.

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Remove all demographic attributes from the training data to ensure fairness.

Question 13

A company deploys a GenAI chatbot for customer support using Vertex AI Agent Builder. The chatbot sometimes gives incorrect answers. The team wants to improve accuracy without retraining the underlying model. Which THREE actions should they take? (Choose 3)

Accepted Answer

Enable Grounding with company knowledge base documents. Grounding with company documents (RAG) provides authoritative knowledge. Prompt tuning guides the model. Adding a human-in-the-loop fallback catches errors. Fine-tuning would require retraining. Increasing temperature would worsen accuracy.

Answer

Increase the model's temperature setting for more creative responses

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Fine-tune the underlying model on past conversations

Question 14

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?

Accepted Answer

RLHF is more effective at aligning the model with human values, including reducing toxicity. RLHF aligns the model with human preferences by rewarding desired behaviors (e.g., non-toxic, helpful). Supervised fine-tuning teaches format but not safety; in-context learning can steer but is less reliable for safety.

Answer

Supervised fine-tuning and RLHF achieve the same results, but RLHF is faster

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Supervised fine-tuning is better for reducing toxicity because it uses labeled safe examples

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In-context learning is always superior to both for safety, as it can adapt on the fly

Question 15

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?

Accepted Answer

Low-Rank Adaptation (LoRA). LoRA (Low-Rank Adaptation) is an adapter-based method that trains only a small number of added parameters, making it efficient and less prone to catastrophic forgetting compared to full fine-tuning. Supervised fine-tuning full model is expensive; RLHF is for alignment after fine-tuning; in-context learning requires no training but may not suffice.

Answer

Reinforcement Learning from Human Feedback (RLHF)

Answer

Full supervised fine-tuning of all model parameters

Answer

In-context learning with few-shot examples