Generative AI Leader Practice Test 33 — 15 Questions

Question 1

A company is evaluating Google Cloud's AI portfolio versus competitors. They want to leverage Gemini's unique capabilities. Which THREE differentiators should they highlight when comparing to AWS Bedrock and Azure OpenAI? (Choose 3)

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

Accepted Answer

Grounding with Google Search for real-time, verifiable responses. Gemini's grounding with Google Search allows it to access and cite real-time, verifiable information from the web, reducing hallucinations and improving factual accuracy. This is a unique differentiator as AWS Bedrock and Azure OpenAI do not natively integrate a live search engine for grounding responses, requiring custom RAG implementations instead.

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Vertex AI as a unified ML platform

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Custom TPU hardware for training large models

Question 2

According to Google's AI Principles, which TWO of the following are core commitments? (Select 2)

Accepted Answer

Be built and tested for safety. Option D, 'Be built and tested for safety,' is correct because Google's AI Principles explicitly commit to avoiding unfair bias and ensuring AI systems are built and tested for safety, including rigorous testing for harmful outcomes and unintended behavior. Option E, 'Be socially beneficial,' is correct because the first of Google's AI Principles states that AI should be developed to benefit society broadly, considering a wide range of social and economic factors and minimizing negative impacts. These two commitments appear directly among Google's published AI Principles, which also include avoiding creating or reinforcing unfair bias, being accountable to people, incorporating privacy design principles, upholding high standards of scientific excellence, and being made available for uses that accord with these principles. Option A, 'Maximize shareholder value,' is not part of the AI Principles, as they focus on ethical and societal outcomes rather than financial returns. Option B, 'Always use the largest model available,' is not a commitment, since the principles emphasize appropriate and beneficial use of AI rather than model size. Option C, 'Achieve state-of-the-art performance on all benchmarks,' is not a stated commitment, as the principles prioritize safety, benefit, and responsibility over benchmark leadership.

Answer

Maximize shareholder value

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Always use the largest model available

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Achieve state-of-the-art performance on all benchmarks

Question 3

A marketing team is using a generative AI model to create ad copy. They notice that the outputs are often too creative and sometimes include exaggerated claims. They want to reduce creativity and make the outputs more predictable and factual. Which parameter should they adjust?

Accepted Answer

Temperature. Temperature is the parameter that scales the logits before softmax, directly affecting the probability distribution of the next token. A lower temperature makes the distribution sharper, so the model chooses more likely tokens, resulting in more predictable and less creative outputs. This aligns with the team's goal of reducing exaggerated claims and increasing factual consistency.

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Max output tokens

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Top-p

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Top-k

Question 4

Which TWO techniques effectively reduce bias in generative model outputs? (Choose two.)

Accepted Answer

Apply adversarial debiasing during training or fine-tuning.. Adversarial debiasing (A) directly reduces bias by training the model to minimize an adversary's ability to predict protected attributes from the model's outputs, forcing the model to learn representations that are invariant to those attributes. Fine-tuning on a balanced dataset (D) corrects representation bias by ensuring the model sees equal examples across groups, preventing overfitting to majority patterns. Both techniques actively address the root causes of bias in training data or model behavior.

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Increase the temperature parameter to introduce more variability.

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Use a larger model with more parameters.

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Reduce max output tokens to limit the model's expression.

Question 5

A developer is using Vertex AI Studio to design a prompt for a content moderation system. They need the model to return a structured JSON with fields 'category' and 'confidence_score'. Which prompt engineering technique should they use?

Accepted Answer

Use a response schema (structured output) in Vertex AI Studio. Vertex AI Studio supports response schemas (structured output) that constrain the model to return JSON matching a defined schema, guaranteeing fields like 'category' and 'confidence_score' with correct types. This is the purpose-built technique for enforcing structured JSON output rather than hoping the model infers the format.

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Set a role prompt instructing the model to act as a content moderator

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Use zero-shot prompting and rely on the model's ability to infer JSON

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Include a few-shot example of the desired JSON output in the prompt

Question 6

A healthcare startup is developing an AI system to assist radiologists in detecting tumors from X-ray images. Which Google AI Principle is MOST directly applicable to this use case?

Accepted Answer

Be built and tested for safety. The principle 'be built and tested for safety' directly applies to medical applications where incorrect detection could harm patients. The other principles are also relevant but safety is the most directly applicable to a diagnostic tool.

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Incorporate privacy design principles

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Be socially beneficial

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Avoid creating or reinforcing unfair bias

Question 7

A company wants to build a customer service chatbot that answers questions about their internal policy documents. The documents are updated monthly, and the team cannot afford to retrain a model each time. Which approach is MOST appropriate?

Accepted Answer

Use Retrieval-Augmented Generation (RAG) with the policy documents indexed in a vector store. Retrieval-Augmented Generation (RAG) is the most appropriate approach because it allows the chatbot to answer questions by retrieving relevant chunks from the policy documents stored in a vector store, without requiring model retraining. When documents are updated monthly, only the vector index needs to be refreshed, which is far more cost-effective and faster than fine-tuning or retraining a model. This approach leverages the base LLM's language understanding while grounding responses in the latest policy content.

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Fine-tune a base LLM on the policy documents monthly

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Use a larger foundation model with a longer context window and paste all documents into each prompt

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Train a custom model from scratch on the policy documents each month

Question 8

A company is planning an iterative rollout of a GenAI tool. Which change management practice is MOST critical for ensuring high adoption among employees?

Accepted Answer

Identify and train AI champions who can demonstrate value and support colleagues. AI champions serve as peer-level advocates who can demonstrate practical use cases, address context-specific concerns, and provide ongoing support, which is critical for overcoming the skepticism and learning curve associated with GenAI tools. Unlike top-down mandates, this grassroots approach builds trust and organic adoption by leveraging social proof and localized expertise.

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Implement a quick rollback plan in case employees resist

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Conduct mandatory training sessions for all employees before rollout

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Restrict access to all non-GenAI tools to force usage

Question 9

A startup is building a multimodal application that needs to process both images and text. They want to prototype quickly for free before moving to production with enterprise controls. Which TWO services should they use? (Choose 2)

Accepted Answer

Google AI Studio for prototyping. Google AI Studio is correct because it provides a free, browser-based environment for prototyping multimodal applications that process both images and text, using models like Gemini. It allows rapid experimentation without cost or infrastructure setup, making it ideal for quick prototyping before moving to production.

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Vertex AI for prototyping

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Google Colab for production

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Vision AI and Natural Language AI separately

Question 10

A company is deploying a large language model (LLM) for customer support using Vertex AI. Which TWO best practices should they follow to ensure high-quality and cost-effective responses?

Accepted Answer

Implement prompt optimization techniques to tailor responses. Option C is correct because prompt optimization techniques (such as prompt engineering, few-shot examples, and tuning) directly improve the relevance and quality of LLM responses while reducing token usage, which lowers cost per request. Option D is correct because Vertex AI Model Monitoring tracks input drift and response quality metrics, enabling the team to detect degradation in customer support answers and retrain or adjust prompts before quality and cost efficiency suffer. Option A is not appropriate because Spot VMs are preemptible and unsuitable for a production customer support LLM endpoint that requires high availability and low latency. Option B is not a best practice because storing prompts in plain text files lacks structured versioning, testing, and deployment controls; prompts should be managed with proper prompt management or CI/CD practices. Option E is incorrect because using a single large model for all query types increases cost and latency; routing simple queries to smaller, cheaper models while reserving the large model for complex cases is more cost-effective.

Answer

Deploy the model on Spot VMs to reduce infrastructure costs

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Store prompts in plain text files for easy version control

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Use a single large model for all query types to maintain consistency

Question 11

A marketing agency uses generative AI to create social media posts. They need the output to be in a specific JSON format for downstream processing. Which prompt technique should they use?

Accepted Answer

Specify the desired JSON schema in the prompt and use few-shot examples. Explicitly specifying the desired JSON schema in the prompt combined with few-shot examples provides the most reliable way to enforce structured output from a generative AI model. This technique leverages the model's pattern-matching ability by showing concrete input-output pairs, which is more effective than vague instructions or parameter adjustments alone for achieving exact JSON formatting.

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Set the temperature to 0.0

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Use a model fine-tuned for JSON generation

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Include a system instruction to output JSON

Question 12

A developer is configuring a Vertex AI Agent Builder agent to use grounding. They receive the following error when calling the API: `404 Not Found: Data store resource not found.` What is the most likely cause?

Accepted Answer

The data store has not been created in the specified project. The error message indicates that the data store resource cannot be found in the specified Google Cloud project. Vertex AI Agent Builder requires the data store to exist and be fully provisioned in the same project as the agent before grounding can be configured. If the data store ID or project ID is incorrect, or the data store has not been created, the API will return a 'not found' error.

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The data store was just created and is not yet propagated

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The agent is not authenticated to access the data store

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The grounding configuration is missing required permissions

Question 13

A company wants to deploy a GenAI code review assistant that integrates into their existing Git workflow. They want to use a managed Google Cloud service to minimize operational overhead. Which TWO services should they consider? (Choose two.)

Accepted Answer

Cloud Build. Cloud Build (A) is correct because it is a managed CI/CD service that natively integrates with Git repositories (via Cloud Source Repositories, GitHub, or Bitbucket) and can run build/review steps defined in cloudbuild.yaml, letting the code review assistant trigger automatically on commits and pull requests with minimal operational overhead. Vertex AI Agent Builder (D) is correct because it is a managed Google Cloud service for building, deploying, and orchestrating GenAI agents and applications, providing the LLM/reasoning layer needed for an AI code review assistant without managing infrastructure. Compute Engine (B) is not appropriate because it is raw IaaS VMs, which would require the company to manage OS patching, scaling, and runtime, contradicting the goal of minimizing operational overhead. Cloud Spanner (C) is a globally distributed relational database and does not provide CI/CD or GenAI agent capabilities. Cloud Run (E) is a managed serverless container platform, but by itself it is a generic compute runtime rather than the Git-integrated build pipeline or GenAI agent framework the scenario requires.

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Compute Engine

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Cloud Spanner

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Cloud Run

Question 14

A company notices that their AI chatbot occasionally generates incorrect information. Which technique can best reduce hallucinations without retraining?

Accepted Answer

Use system instructions to constrain the model to only answer from provided context. Constraining the model to answer only from provided context directly addresses the root cause of hallucinations—the model generating information not grounded in verified sources. This technique, often implemented via system instructions or retrieval-augmented generation (RAG) pipelines, forces the model to rely on a trusted knowledge base rather than its parametric memory, effectively eliminating unsupported fabrications without requiring retraining.

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Use a longer system prompt without examples

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Set top_p to 0.1

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Increase temperature to 0.9

Question 15

Which TWO of the following are valid Google Cloud AI APIs for natural language processing?

Accepted Answer

Translation API. The Translation API and Natural Language API are both valid Google Cloud AI APIs for natural language processing (NLP). The Translation API converts text between languages, which is a core NLP task, while the Natural Language API provides entity recognition, sentiment analysis, and syntax analysis, directly processing human language.

Answer

Speech-to-Text API

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Vision AI

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Document AI