Generative AI Leader Practice Test 2 — 30 Questions

Question 1

Which THREE are valid methods to reduce bias in generative AI outputs?

Accepted Answer

Using a more diverse training dataset. Training on a more diverse dataset reduces representational bias by exposing the model to a wider range of demographics, cultures, and perspectives. This directly mitigates the model's tendency to overrepresent majority groups or underrepresent minorities, which is a root cause of biased outputs in generative AI.

Answer

Using only English prompts

Answer

Increasing model size

Question 2

A company is deploying a generative AI application that generates medical reports. They need to ensure the output is factual and minimizes hallucinations. Which approach is most effective?

Accepted Answer

Implement retrieval-augmented generation (RAG) with a curated knowledge base. Retrieval-Augmented Generation (RAG) is the most effective approach because it grounds the model's output in a curated, authoritative knowledge base of medical data. By retrieving relevant, verified documents at inference time, RAG directly reduces the model's reliance on its parametric memory, which is the primary source of hallucinations in generative AI. This is especially critical in high-stakes domains like medical reporting, where factual accuracy is paramount.

Answer

Fine-tune the model with RLHF

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Set the temperature to 0.0

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Use prompt engineering to instruct the model to be accurate

Question 3

A data scientist is fine-tuning a large language model for a specialized domain using limited labeled data. To avoid catastrophic forgetting and reduce computational cost, which approach is recommended?

Accepted Answer

Adapter-based fine-tuning using LoRA. Adapter-based fine-tuning using LoRA (Low-Rank Adaptation) is recommended because it freezes the pre-trained model weights and injects trainable low-rank matrices into the transformer layers. This approach drastically reduces the number of parameters to update (often by 10,000x), lowering memory and compute requirements, while preserving the original knowledge to prevent catastrophic forgetting on limited domain data.

Answer

Using prompt engineering with in-context learning

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Full fine-tuning of all model parameters

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Training a new model from scratch on the domain data

Question 4

A company uses Gemini for Google Workspace to help employees draft emails. After rollout, the IT team notices that the Smart Compose suggestions are sometimes inappropriate for external communications. What is the BEST way to address this while maintaining productivity gains?

Accepted Answer

Train employees to write more specific prompts and provide few-shot examples for desired tone. Providing few-shot examples or using custom prompts for tone can guide the model to produce more appropriate suggestions. Disabling the feature or using a generic model would not leverage Workspace integration.

Answer

Switch to a third-party email assistant not integrated with Workspace

Answer

Disable Smart Compose for all external email accounts

Answer

Fine-tune the Gemini model on the company's past external emails

Question 5

A healthcare startup wants to generate synthetic patient notes for training medical residents. They need the output to follow a strict template with sections: Chief Complaint, History, Assessment, Plan. Which prompt engineering strategy should they use to ensure consistent structure?

Accepted Answer

Provide a few-shot example of the template filled out and instruct the model to follow that format for new cases. Structured output via few-shot examples showing the exact template ensures the model learns the required format. Adding instructions alone may not enforce the structure as reliably.

Answer

Use a zero-shot prompt asking for a patient note in bullet points

Answer

Set temperature to 0 and max tokens to a high value

Answer

Use a system prompt that lists the sections as instructions

Question 6

A developer deployed a large language model on Vertex AI for real-time chat. Users report slow response times. The model generates sentences one word at a time. Which optimization should be applied to reduce latency?

Accepted Answer

Use streaming responses to start output earlier.. Streaming responses allow the model to send tokens to the client as they are generated, rather than waiting for the full sequence to complete. This reduces perceived latency significantly in real-time chat, as users see the first word appear almost immediately, even though the total generation time remains similar.

Answer

Batch multiple user queries together.

Answer

Deploy the model with more accelerators.

Answer

Enable prompt caching to reuse previous queries.

Question 7

A sales team wants to use Gemini for Google Workspace to draft emails in Gmail. Which feature should they enable to generate full email drafts from a brief prompt?

Accepted Answer

Help me write. 'Help me write' is the specific Gemini for Google Workspace feature designed to generate full email drafts from a brief prompt. It leverages the underlying large language model to compose complete, context-aware email content, unlike features that only suggest short phrases or replies.

Answer

Translate

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Smart Reply

Answer

Smart Compose

Question 8

A developer is using Vertex AI Gemini API for a chatbot. The chatbot sometimes outputs harmful content. What is the best first step to mitigate this?

Accepted Answer

Use safety filters and safety settings in the API request. The Vertex AI Gemini API provides built-in safety filters and configurable safety settings (e.g., `safety_settings` parameter with categories like `HARM_CATEGORY_HARASSMENT` and thresholds like `BLOCK_ONLY_HIGH`) that allow developers to block harmful outputs at inference time without retraining. This is the fastest and most direct first step to mitigate harmful content, as it requires no additional infrastructure or model modification.

Answer

Fine-tune the model on curated safe data

Answer

Add a human-in-the-loop review

Answer

Switch to a smaller model

Question 9

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.

Answer

Increasing model size to learn more patterns

Answer

Relying solely on post-hoc filters

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Limiting the model to only factual prompts

Question 10

According to Google's AI Principles, AI systems should be 'accountable to people.' What does this principle primarily require?

Accepted Answer

Organizations should provide avenues for feedback and recourse when AI systems cause harm. Accountability means providing mechanisms for feedback and recourse when AI systems cause harm or make mistakes.

Answer

AI systems should always have a human monitor present during operation

Answer

The AI system should be able to explain all its decisions to end users

Answer

AI systems should be designed to maximize shareholder value

Question 11

A data scientist wants to compare the performance of three different foundation models for a text summarization task. They have a labeled dataset of summaries. Which Vertex AI tool should they use to perform this evaluation?

Accepted Answer

Vertex AI Studio - Evaluation. Vertex AI Studio provides a model evaluation interface where you can compare model outputs using automatic metrics. Model Garden is a model hub. Agent Builder is for building agents. RAG Engine is for retrieval.

Answer

Vertex AI RAG Engine - Retrieval evaluation

Answer

Vertex AI Agent Builder - Agent evaluation

Answer

Model Garden - Model comparison view

Question 12

A security team wants to prevent prompt injection attacks on their generative AI application hosted on Vertex AI. Which best practice should they implement?

Accepted Answer

Implement input validation and output filtering. Prompt injection attacks exploit the model's inability to distinguish between user instructions and untrusted input. Implementing input validation (e.g., sanitizing special characters or known injection patterns) and output filtering (e.g., using a classifier to detect and block malicious responses) directly mitigates this risk by controlling what the model processes and returns. On Vertex AI, this can be enforced via custom safety attributes or integration with services like Cloud DLP for data loss prevention.

Answer

Use a custom model instead of a foundation model

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Disable all logging

Answer

Use a private endpoint

Question 13

A team uses PaLM 2 API to generate product descriptions, but the output sometimes contains factual inaccuracies. What is the best approach to improve accuracy?

Accepted Answer

Use grounding with Google Search. Grounding with Google Search is the correct approach because it allows the PaLM 2 API to retrieve real-time, verifiable information from the web, directly reducing factual inaccuracies in generated product descriptions. Unlike parameter adjustments, grounding provides an external knowledge source that the model can cite, ensuring outputs are based on current and accurate data rather than relying solely on its training data.

Answer

Increase the temperature parameter

Answer

Reduce the top_k value

Answer

Set the max_output_tokens higher

Question 14

A retail company has deployed a customer support chatbot using Vertex AI Agent Builder. The chatbot is configured with a knowledge base stored in BigQuery (user manuals) and Cloud Storage (product images). The agent uses a Gemini 1.5 Pro model for response generation. Users report that the chatbot frequently gives incorrect answers and sometimes does not reference the knowledge base at all. Logs show high latency (average response time > 10 seconds) and many responses are generic or hallucinated. The agent's grounding configuration currently uses the default settings. The development team is considering the following actions: A) Switch to a smaller model like Gemini 1.5 Flash to reduce latency. B) Increase the context window of the model to allow more knowledge base content. C) Enable Vertex AI Search for grounding and configure a search aggregation strategy that retrieves relevant documents from the knowledge base. D) Fine-tune the Gemini model with the company's historical chat logs to improve domain-specific responses. Which action should the team take FIRST to address the issues?

Accepted Answer

Enable Vertex AI Search for grounding and configure a search aggregation strategy that retrieves relevant documents from the knowledge base.. The core issue is that the chatbot is not grounding its responses in the provided knowledge base, leading to hallucinations and generic answers. Enabling Vertex AI Search for grounding directly addresses this by forcing the model to retrieve and cite relevant documents from BigQuery and Cloud Storage before generating a response. This is the foundational step to fix the accuracy problem, as no amount of model tuning or context window expansion will help if the model is not consulting the correct data sources.

Answer

Switch to a smaller model like Gemini 1.5 Flash to reduce latency.

Answer

Increase the context window of the model to allow more knowledge base content.

Answer

Fine-tune the Gemini model with the company's historical chat logs to improve domain-specific responses.

Question 15

A company is using Vertex AI to generate personalized marketing emails. The model sometimes produces biased content. What is the most effective way to detect and mitigate bias?

Accepted Answer

Use Vertex AI Explainable AI to analyze predictions and detect bias in training data. Vertex AI Explainable AI provides feature attributions and model explanations that help identify which input features (e.g., demographic attributes, phrasing patterns) contribute most to biased outputs. By analyzing these attributions against training data, you can pinpoint and mitigate bias at the source, rather than relying on post-hoc manual review or model swapping.

Answer

Add more diverse training data

Answer

Manually review all generated emails before sending

Answer

Switch to a different generative model

Question 16

A company wants to generate marketing images for a new product launch using GenAI. Which Google Cloud service should they use?

Accepted Answer

Vertex AI Imagen. Imagen on Vertex AI is Google Cloud's image generation model, available through Vertex AI APIs or Model Garden.

Answer

Gemini for Google Workspace

Answer

Vertex AI Agent Builder

Answer

Vertex AI Model Garden

Question 17

A company wants to generate images for slide decks using Gemini in Google Slides. Which Gemini feature in Google Slides should they use?

Accepted Answer

Image generation from text prompts. Gemini in Slides can generate images from text prompts, allowing users to create visuals directly within the presentation.

Answer

Speaker notes generation

Answer

Slide layout optimization

Answer

Summation of slide content

Question 18

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?

Accepted Answer

Vertex AI Studio. Vertex AI Studio provides a no-code/low-code environment with pre-trained foundation models and prompt templates, enabling rapid generation of product descriptions from keywords without any ML expertise. It offers the fastest time-to-market because it eliminates the need for custom model training, infrastructure setup, or coding, directly leveraging Google's generative AI capabilities through a simple interface.

Answer

Vertex AI Workbench with custom training

Answer

Vertex AI Agent Builder

Answer

Vertex AI Model Garden

Question 19

An e-commerce company uses a generative AI model to generate product descriptions. They observe that descriptions for high-end products use more sophisticated language compared to budget products, potentially reinforcing class stereotypes. What is the most likely cause, and what should they do to mitigate it?

Accepted Answer

The training data reflects real-world associations; fine-tune with a balanced dataset that includes diverse product descriptions across price ranges. The bias stems from training data correlations. Fine-tuning on balanced, diverse data can reduce stereotypical associations. The other options either do not address the root cause or are less effective.

Answer

The safety filters are too aggressive; relax them

Answer

The temperature parameter is set too low; increase it to introduce more randomness

Answer

The model architecture is biased; switch to a different base model

Question 20

A company deploys a Gemini model on Vertex AI for a customer-facing chatbot. They observe the chatbot occasionally produces toxic language. Which TWO measures should they implement immediately to reduce toxic outputs?

Accepted Answer

Enable the 'block offensive content' flag in the model's safety configuration.. Enabling the 'block offensive content' flag directly activates Gemini's built-in safety filters, which are designed to detect and suppress toxic language at the model's output layer. This is an immediate, configuration-level measure that requires no additional training or external services, making it the fastest way to reduce harmful responses in a production chatbot.

Answer

Increase the model's temperature to make outputs more conservative.

Answer

Use a separate language model to rephrase the outputs before sending to users.

Answer

Fine-tune the model on a curated dataset of polite conversations.