Generative AI Leader Practice Test 40 — 15 Questions

Question 1

A company is deploying a chatbot using Vertex AI and wants to ensure that the model's responses are grounded in Google Search results to reduce hallucinations. Which feature should they enable?

Accepted Answer

Vertex AI Search for grounding. Google Search grounding in Vertex AI allows the model to retrieve real-time information from Google Search to ground answers. RAG uses private data, not web search. Vertex AI Agent Builder includes grounding but is for building agents, not specifically the grounding feature itself.

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Vertex AI Agent Builder

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Retrieval-Augmented Generation (RAG) with internal documents

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Gemini API with custom fine-tuning

Question 2

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

Accepted Answer

Veo. Veo is Google Cloud's advanced video generation model that can create high-quality, realistic videos from text prompts, making it the ideal choice for generating product videos for social media ads. Unlike other services, Veo is specifically designed for video synthesis, offering capabilities like style control and cinematic effects directly from text descriptions.

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Imagen

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Gemini Pro Vision

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Codey

Question 3

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

Accepted Answer

Controls the diversity of the output by scaling the log probabilities before sampling. Temperature controls the randomness of token selection. Higher temperature increases creativity/diversity; lower temperature produces more deterministic and focused responses.

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Adjusts the number of highest-probability tokens considered at each step

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Specifies the minimum probability threshold for token selection

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Sets the maximum number of tokens in the response

Question 4

A company wants to use GenAI to generate marketing content such as blog posts and social media updates. They need the content to be on-brand and factually accurate. Which TWO features should they use?

Accepted Answer

Provide few-shot examples in the prompt to set the brand tone. Few-shot examples in prompts help maintain brand voice. Grounding with Google Search ensures factual accuracy. Vertex AI Studio is for prompt design but not directly for accuracy. Fine-tuning may be overkill. Longer context may dilute the message.

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Use a longer context window to include all brand guidelines

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Fine-tune the model on a large corpus of past marketing content

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Use Vertex AI Studio to design prompts with no additional grounding

Question 5

You want to use a Google foundation model to generate text summaries of news articles. Which Vertex AI service should you use?

Accepted Answer

Vertex AI Generative AI Studio. Vertex AI Generative AI Studio (now part of Vertex AI Agent Builder) provides a no-code/low-code environment to access, test, and tune Google's foundation models, including PaLM 2 and Gemini, specifically for generative tasks like text summarization. It offers built-in prompt templates and safety settings tailored for summarization use cases, making it the correct service for this task.

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Vertex AI Prediction

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Vertex AI Model Registry

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Vertex AI Feature Store

Question 6

Which THREE are best practices for responsible deployment of generative AI in a customer-facing application?

Accepted Answer

Implement human-in-the-loop review for sensitive outputs. Human-in-the-loop (HITL) review ensures that sensitive outputs—such as those involving protected health information (PHI), personally identifiable information (PII), or high-stakes decisions—are vetted by a human before reaching the customer. This mitigates the risk of harmful or biased generations that automated guardrails might miss, aligning with responsible AI principles like accountability and safety.

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Train the model on all available data to maximize coverage

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Use only small models to reduce risk

Question 7

A legal firm wants to automate contract analysis. They need to extract key clauses (e.g., termination, indemnification) from scanned PDFs. The team expects high accuracy and must maintain data privacy. Which combination of services is most suitable?

Accepted Answer

Use Document AI for OCR and Vertex AI with a custom fine-tuned model for clause extraction. Document AI performs OCR and extracts text from scanned PDFs; Vertex AI with a custom fine-tuned model provides high accuracy for clause extraction while keeping data within the customer's project.

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Use AutoML Tables to train a classification model on text features

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Use Gemini API directly with a prompt to analyze PDFs

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Use AppSheet to create a form for manual entry and then use BigQuery ML

Question 8

A company is using Vertex AI to fine-tune a large language model on proprietary customer support transcripts. The transcripts contain personally identifiable information (PII) such as names and email addresses. What is the BEST practice to comply with Google's AI Principles on privacy?

Accepted Answer

De-identify the transcripts by removing or masking PII before fine-tuning. De-identifying the transcripts by removing or masking PII before fine-tuning is the best practice because it enforces data minimization and privacy-by-design, aligning with Google's AI Principles (which explicitly call for avoiding creating or reinforcing unfair bias and protecting privacy). Fine-tuning on raw PII risks the model memorizing and later regurgitating names, emails, or other sensitive data in generated outputs. Masking or tokenizing PII before training removes that exposure while preserving the linguistic patterns needed for the support use case.

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Use a model that has already been trained on similar data to avoid fine-tuning

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Obtain consent from all customers whose data appears in the transcripts

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Fine-tune the model directly on the transcripts, as the model will not memorize exact data

Question 9

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?

Accepted Answer

Gemini for Workspace. Gemini for Workspace is Google's AI offering specifically designed to integrate generative AI capabilities into Google Workspace applications like Docs, Gmail, Sheets, and Slides. It provides features such as 'Help me write' in Docs, smart reply and email drafting in Gmail, and formula/chart generation in Sheets. The other options are developer-focused or research-oriented tools not intended for direct Workspace integration.

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Colab Enterprise

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Vertex AI Agent Builder

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NotebookLM

Question 10

A company is building a search application that requires grounding answers in their internal knowledge base. They want to use Vertex AI Search and Conversation with a custom datastore. Which configuration is essential to ensure the model only answers based on their documents?

Accepted Answer

Configure the answer generation to use grounding with the enterprise datastore as the source.. Vertex AI Search and Conversation provides a built-in grounding capability that explicitly ties answer generation to a specified enterprise datastore. By configuring grounding with the custom datastore as the source, the model is constrained to retrieve and synthesize answers exclusively from the indexed documents, preventing reliance on its parametric knowledge or external sources.

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Enable streaming responses to get real-time answers.

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Fine-tune the model on the company's documents.

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Set the model's temperature to 0 to make responses deterministic.

Question 11

Which TWO techniques are effective for reducing bias in generative AI model outputs?

Accepted Answer

Training on diverse and representative datasets. Training on diverse and representative datasets directly reduces sampling bias and coverage gaps in the training distribution, which are primary sources of stereotypical or skewed outputs. By ensuring the model sees balanced examples across demographics, contexts, and edge cases, it learns more equitable representations and reduces the likelihood of generating biased content.

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Increasing model size to learn more patterns

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Relying solely on post-hoc filters

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Limiting the model to only factual prompts

Question 12

A team wants to reduce hallucinations in a question-answering model. Which THREE techniques should they consider?

Accepted Answer

Fine-tune the model on a curated factual dataset. Fine-tuning on a curated factual dataset directly adjusts the model's weights to prioritize accurate, domain-specific knowledge, reducing the likelihood of generating unsupported or hallucinated content. This technique anchors the model's output in verified data, making it more reliable for question-answering tasks.

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Reduce the number of tokens in output

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Increase the temperature parameter

Question 13

An enterprise is comparing Google Cloud Vertex AI vs AWS Bedrock vs Azure OpenAI for a generative AI application. Which unique Google differentiator allows the model to reference up-to-date web information and private data with managed retrieval?

Accepted Answer

Vertex AI Agent Builder with search grounding. Vertex AI Agent Builder with search grounding is the unique Google differentiator that enables a generative AI model to reference up-to-date web information and private data through managed retrieval. Search grounding in Vertex AI allows the model to retrieve relevant documents from Google Search or a private corpus (via Vertex AI Search) and incorporate that information into its responses, ensuring factual accuracy and recency. This is a managed service that abstracts away the complexity of building a retrieval-augmented generation (RAG) pipeline, making it a key differentiator for enterprise applications requiring dynamic, context-aware answers.

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TPU availability

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Integration with Google Workspace

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Multimodal understanding

Question 14

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?

Accepted Answer

Use few-shot prompting with the examples in the prompt.. Few-shot prompting is the correct approach because it allows the team to inject the desired style, tone, and length directly into the prompt using the 10 high-quality examples, without any model retraining. This technique leverages the in-context learning capability of large language models on Vertex AI, enabling consistent output from a small set of demonstrations. It is ideal for teams with limited ML expertise as it requires only prompt engineering, not fine-tuning or infrastructure changes.

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Use a pre-built template with no model input.

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Fine-tune the model on a large external dataset of product descriptions.

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Set the temperature to 0.9 to maximize creativity.

Question 15

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?

Accepted Answer

Use grounding with Vertex AI Search. Grounding with Vertex AI Search is the best approach because it connects the foundation model's outputs to a verifiable, curated knowledge base, ensuring factual accuracy and compliance with regulations that prohibit hallucination. By retrieving information from a trusted source (e.g., medical databases) in real time, the model can cite evidence and avoid generating unverified claims.

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Use a small model to hallucinate less

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Reduce temperature to 0

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Fine-tune on medical journals