Generative AI Leader Practice Test 20 — 15 Questions

Question 1

A company wants to adopt GenAI for internal knowledge management. They plan to start with a small pilot team, gather feedback, and then expand. Which change management approach is MOST aligned with this strategy?

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

Accepted Answer

Pilot with a small group, identify AI champions, collect feedback, and iteratively expand. Iterative rollout with a pilot group, AI champions, and measuring adoption is a proven change management pattern. Starting with the entire organization is risky. Mandatory training may cause resistance. Focusing only on technical deployment ignores the human side.

Answer

Conduct mandatory training for all employees before the rollout

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Deploy the solution to the entire organization at once with a communication campaign

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Focus solely on the technical deployment without change management activities

Question 2

A company wants to deploy a chatbot for customer support. To comply with the AI Principle of being 'accountable to people', which practice should they implement?

Accepted Answer

Provide human review for high-stakes decisions made by the chatbot. Providing human oversight for high-stakes decisions ensures accountability, aligning with Google's AI Principle that AI systems should be accountable to people.

Answer

Allow the chatbot to escalate all conversations to human agents

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Ensure the chatbot can override human agents' decisions

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Disable logging of chatbot interactions to protect user privacy

Question 3

A startup wants to generate images from text descriptions for their marketing materials. They prefer a managed service that requires minimal coding. Which Google Cloud generative AI offering should they use?

Accepted Answer

Vertex AI Imagen. Vertex AI Imagen is Google Cloud's managed generative AI service specifically designed for text-to-image generation. It requires minimal coding, as users can interact with it via the Cloud Console, API calls with simple prompts, or through Vertex AI's built-in tools, making it ideal for a startup needing to generate marketing images from text descriptions without extensive development effort.

Answer

Natural Language API

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Document AI

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Cloud Speech-to-Text

Question 4

A company with limited AI expertise wants to adopt gen AI. They need a solution that integrates with existing data and applications. Which Google Cloud offering is best?

Accepted Answer

Vertex AI Agent Builder. Vertex AI Agent Builder is a Google Cloud offering designed to help organizations build and deploy generative AI agents and applications that integrate with enterprise data and existing applications, with minimal AI expertise required. It provides pre-built connectors, grounding with enterprise data, and managed infrastructure, making it the best fit for a company with limited AI expertise that needs integration.

Answer

Apigee

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Colab Enterprise

Answer

BigQuery ML

Question 5

An organization is developing a GenAI strategy for multiple business units. Which THREE steps should they take to ensure alignment? (Select three.)

Accepted Answer

Establish common data governance policies. Establishing common data governance policies (C) ensures that all business units adhere to consistent standards for data quality, privacy, and security, which is critical for training and deploying reliable GenAI models. Without unified governance, disparate data practices can lead to model bias, compliance violations, and integration failures across the organization.

Answer

Implement a chargeback model for usage costs

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Allow each business unit to independently choose models

Question 6

Which Google Cloud feature enables you to experiment with different prompts and model parameters interactively, and also supports model tuning without writing code?

Accepted Answer

Vertex AI Studio. Vertex AI Studio is the correct answer because it provides an interactive, code-free environment for experimenting with prompts and model parameters, and also supports model tuning through a graphical interface. This aligns directly with the question's requirement for interactive experimentation and no-code tuning. Option A (Vertex AI Agent Builder) is designed for building conversational agents, not for prompt experimentation or tuning. Option B (Google Cloud Console) is a general management interface but lacks specific interactive experimentation features for generative AI. Option C (Model Garden) allows discovery and selection of models but does not support interactive prompt testing or tuning without code.

Answer

Vertex AI Agent Builder

Answer

Google Cloud Console

Answer

Model Garden

Question 7

A retail company has deployed a generative AI chatbot for customer support. They notice that the model sometimes provides incorrect product information. The team wants to ground the model's responses in their product catalog to improve accuracy. Which Vertex AI feature should they enable?

Accepted Answer

Use Vertex AI RAG Engine. Vertex AI RAG Engine enables retrieval-augmented generation by integrating with a private data store (e.g., the company's product catalog). It fetches relevant documents from the catalog at inference time, grounding the model's responses in verified internal data and reducing hallucinations. Option B (Grounding with Google Search) retrieves from public web data, not a private catalog, so it cannot fulfill the requirement for accurate product-specific information. Options C and D do not address dynamic retrieval from a catalog: temperature affects creativity, not accuracy, and fine-tuning provides only static knowledge updates.

Answer

Enable Grounding with Google Search

Answer

Increase the model's temperature setting

Answer

Fine-tune the model with product catalog updates

Question 8

A healthcare organization deploys a generative AI model to assist in diagnosing rare diseases from medical images. To comply with the EU AI Act's requirements for high-risk AI systems, what is the MOST critical control they must implement?

Accepted Answer

Implement a human-in-the-loop review process with the ability to override AI recommendations. The EU AI Act requires human oversight for high-risk AI systems, including the ability for humans to override or stop the system's decisions.

Answer

Use a model with a high F1 score to minimize the need for human review

Answer

Ensure the model is trained on synthetic data only to avoid privacy concerns

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Deploy the model in a sandbox environment without real patient data

Question 9

A team uses Vertex AI Generative AI Studio to tune a model via RLHF. After tuning, the model outputs are bland. What likely went wrong?

Accepted Answer

Reward model overfits to generic responses. When the reward model overfits to generic responses, it assigns high rewards to safe, non-committal outputs, causing the RLHF-tuned model to converge toward bland, uninformative text. This happens because the reward model learns to prefer patterns that are statistically common in the training data rather than genuinely high-quality or diverse responses, directly leading to the 'bland' output described.

Answer

Insufficient training data

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Too many training steps

Answer

Low temperature during evaluation

Question 10

A law firm uses a generative model to analyze contracts and extract key clauses. The model often outputs irrelevant clauses or misses important ones. They want to improve the relevance of the outputs without retraining the entire model. Which approach is best?

Accepted Answer

Implement Retrieval-Augmented Generation (RAG) with a curated legal clause database and a reranker to select the most on-topic passages.. Retrieval-Augmented Generation (RAG) with a curated legal clause database and a reranker is the best approach because it grounds the model's outputs in a trusted, external knowledge base, ensuring that only the most relevant clauses are retrieved and used for generation. This directly addresses the problem of irrelevant outputs and missed clauses without requiring retraining, as the model can dynamically fetch and rank the most on-topic passages from the curated database.

Answer

Increase the input token limit to provide the entire contract in the prompt.

Answer

Decrease the temperature to make outputs more deterministic.

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Fine-tune the base model on a labeled dataset of contract-clause pairs.

Question 11

A financial services firm is deploying a GenAI-powered contract analysis tool. The tool must extract key clauses and flag risky language. Which strategy BEST ensures structured, machine-readable output that downstream systems can parse?

Accepted Answer

Use a few-shot prompt with examples of JSON output containing the desired fields. Using a few-shot prompt with JSON output examples directly instructs the model to produce structured, machine-readable data. This approach leverages the model's in-context learning ability to follow a specific schema, ensuring downstream systems can parse the extracted clauses without additional transformation. It balances flexibility and precision without requiring costly fine-tuning or relying on unreliable free-form text.

Answer

Ask the model to write a summary of the contract in natural language

Answer

Fine-tune the model on a dataset of contracts with clause labels

Answer

Rely on the model's pre-trained ability to extract clauses without any formatting instructions

Question 12

What is the primary purpose of a system instruction in the Gemini API?

Accepted Answer

Define the overall behavior and constraints for the model. The system instruction in the Gemini API is the primary mechanism to define the overall behavior, persona, constraints, and guardrails for the model across all interactions. Unlike per-query parameters, it sets a persistent context that shapes how the model interprets every user prompt, ensuring consistent adherence to rules such as tone, format, or safety policies.

Answer

Set the model's temperature and top_p

Answer

Provide few-shot examples for each query

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Set the maximum output length

Question 13

Which of the following best describes the primary benefit of using Grounding with Google Search when building a GenAI chatbot?

Accepted Answer

It allows the model to access real-time information from the internet to reduce hallucinations. Grounding with Google Search connects the GenAI chatbot to real-time internet data, allowing it to retrieve current facts and events that the model was not trained on. This reduces hallucinations by ensuring responses are based on verified, up-to-date information rather than relying solely on the model's static training data.

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It reduces the model's latency by caching responses

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It enables the model to generate images based on text descriptions

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It provides fine-tuning capabilities for domain-specific data

Question 14

A developer is using the Gemini API to generate creative marketing copy. They want the output to be more diverse and unexpected. Which parameter should they increase?

Accepted Answer

Temperature.. Increasing the temperature parameter makes the model's output probabilities more uniform, encouraging it to sample less likely tokens and produce more diverse, unexpected, and creative text. A higher temperature (e.g., >1.0) flattens the probability distribution, so the model is more likely to choose surprising word combinations rather than the most probable ones.

Answer

Presence penalty.

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Top-p.

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Frequency penalty.

Question 15

A multinational corporation wants to deploy a generative AI chatbot for its global customer service. They must ensure that data residency requirements are met for EU customers, meaning all prompts and responses from EU users must be processed and stored within the European Union. Which Google Cloud capability should they use?

Accepted Answer

Configure data residency controls in Vertex AI to process and store data in a specific region. Vertex AI allows you to configure data residency controls that ensure prompts and responses are processed and stored within a specific geographic region, such as the European Union. This directly meets the requirement for EU customer data to remain within EU boundaries, as it leverages Google Cloud's regionalization capabilities for AI workloads.

Answer

Implement Cloud Armor to block non-EU traffic

Answer

Use Cloud Data Loss Prevention (DLP) to mask EU data

Answer

Enable VPC Service Controls to restrict data flow