Generative AI Leader Practice Exam 3 — 20 Questions

Question 1

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?

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

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

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Vertex AI Agent Builder - Agent evaluation

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Model Garden - Model comparison view

Question 2

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

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Use a private endpoint

Question 3

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

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Reduce the top_k value

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Set the max_output_tokens higher

Question 4

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 5

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 6

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 7

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

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Slide layout optimization

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Summation of slide content

Question 8

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

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Vertex AI Agent Builder

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Vertex AI Model Garden

Question 9

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

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

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

Question 10

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.

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

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

Question 11

Which TWO factors are most critical when deciding to build a custom GenAI model vs. using a pre-built API? (Select two.)

Accepted Answer

Availability of in-house ML talent. Building a custom GenAI model requires specialized machine learning expertise, including proficiency in frameworks like PyTorch or TensorFlow, experience with distributed training (e.g., using Horovod or DeepSpeed), and the ability to fine-tune architectures like transformers. Without in-house ML talent, the organization cannot effectively manage data curation, hyperparameter tuning, or model evaluation, making a pre-built API the more viable choice. This factor directly determines whether the organization has the technical capacity to undertake custom development.

Answer

Number of layers in the model

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Brand reputation of the model provider

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Volume of expected inference requests

Question 12

A developer is using Vertex AI PaLM API to generate code snippets. The responses sometimes contain security vulnerabilities. What is the best practice to mitigate this?

Accepted Answer

Implement input validation and output filtering with safety attributes. Input validation and output filtering with safety attributes directly address security vulnerabilities by sanitizing user inputs and filtering model outputs for harmful content. The Vertex AI PaLM API provides safety attribute scores (e.g., toxicity, harassment) that allow developers to programmatically block or flag responses that exceed defined thresholds, reducing the risk of generating insecure code snippets.

Answer

Disable safety filters to allow more output

Answer

Increase the max output tokens

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Set safety settings to block all categories

Question 13

A healthcare company wants to use generative AI to summarize patient records. They are concerned about data privacy and HIPAA compliance. Which Google Cloud feature should they use to protect patient data?

Accepted Answer

Customer-managed encryption keys (CMEK) with VPC Service Controls. D is correct because Customer-managed encryption keys (CMEK) with VPC Service Controls provide a defense-in-depth approach for HIPAA compliance. CMEK allows the healthcare company to control and manage the encryption keys used to protect patient data at rest, while VPC Service Controls prevent data exfiltration by restricting data movement outside a defined service perimeter. This combination ensures that even if an attacker gains access, they cannot decrypt the data or move it out of the controlled environment, directly addressing data privacy and HIPAA requirements.

Answer

Cloud Audit Logs

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Confidential VMs

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Cloud Data Loss Prevention (DLP) API

Question 14

Which TWO options are best practices for deploying generative AI models on Vertex AI? (Choose two.)

Accepted Answer

Enable automatic scaling to handle variable traffic. Vertex AI's automatic scaling dynamically adjusts the number of serving instances based on incoming traffic, ensuring low latency during spikes and cost efficiency during lulls. This is a best practice for production workloads where traffic patterns are unpredictable, as it eliminates the need for manual capacity planning.

Answer

Disable logging to reduce cost

Answer

Manually scale instances based on expected load

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Serve the model directly without optimization

Question 15

Which TWO features are available in Vertex AI Studio for prompt engineering? (Choose two.)

Accepted Answer

Side-by-side comparison of model outputs. Vertex AI Studio provides a side-by-side comparison feature that allows prompt engineers to evaluate outputs from multiple model configurations or parameter settings simultaneously. This enables direct visual comparison of responses, helping to identify the most effective prompt phrasing or parameter combination without manual switching.

Answer

One-click deployment to a Vertex AI endpoint

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Fine-tuning models directly in the interface

Answer

Building conversational agents with drag-and-drop

Question 16

A developer is using Vertex AI Studio to prototype a chat application. They want to provide the model with a system instruction to set the tone and style. How should they configure this in the Vertex AI Studio interface?

Accepted Answer

Use the 'System Instruction' field in the model configuration. Vertex AI Studio provides a dedicated 'System Instruction' field in the model configuration panel, which allows developers to set the tone, style, and behavioral guidelines for the model without mixing them into the user prompt. This field is specifically designed to hold system-level instructions that are prepended to the conversation context, ensuring consistent behavior across multiple turns.

Answer

Add the instruction as part of the prompt text

Answer

Set the temperature parameter to a high value

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Add the instruction in the 'Context' parameter

Question 17

Why is the model responding in English despite the prompt asking for French translation?

Accepted Answer

The system instruction to translate to French was not set; the user prompt alone is not sufficient. In Google Cloud's Vertex AI and Generative AI offerings, the system instruction is a separate, persistent directive that sets the model's behavior, such as language output. The user prompt alone, even if it asks for a French translation, is not sufficient to override the default language of the model; the system instruction must explicitly specify the target language. Without this instruction, the model defaults to its training language (typically English), regardless of the user's request.

Answer

The model endpoint is configured for English only

Answer

The temperature is too high, causing random outputs

Answer

The maxOutputTokens is too low to complete the translation

Question 18

Which TWO features are available in Vertex AI Agent Builder to enhance the conversational abilities of an agent? (Choose TWO.)

Accepted Answer

Slot filling. Slot filling is correct because it allows the agent to collect required parameters (slots) from the user during a conversation, enabling multi-turn interactions to fulfill complex requests. In Vertex AI Agent Builder, slot filling is a core feature for conversational agents, as it systematically prompts for missing information (e.g., date, location) until all necessary slots are filled, enhancing the agent's ability to handle dynamic user inputs.

Answer

Sentiment analysis

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Code execution

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Knowledge base integration

Question 19

Which THREE factors should be considered when choosing between Gemini 1.5 Pro and Gemini 1.5 Flash for a customer-facing chatbot? (Choose three.)

Accepted Answer

Cost constraints: Flash is more cost-effective per token. Gemini 1.5 Flash is designed as a cost-optimized model, offering significantly lower per-token pricing compared to Gemini 1.5 Pro. For a customer-facing chatbot with high query volumes, cost efficiency is a primary consideration, making Flash the more economical choice for routine interactions.

Answer

Safety filters: Pro has stricter safety defaults

Answer

Multimodal capability: Flash does not support image input

Question 20

Which TWO options are benefits of using Vertex AI Model Garden compared to using raw pre-trained models from external sources? (Choose two.)

Accepted Answer

Integration with Vertex AI tools like evaluation and monitoring. Vertex AI Model Garden is deeply integrated with the Vertex AI ecosystem, providing native access to tools like Vertex AI Evaluation for model performance assessment and Vertex AI Monitoring for drift detection and observability. This integration eliminates the need for custom pipelines to connect external models with these managed services, streamlining the MLOps workflow.

Answer

Lower cost compared to using generic APIs

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Ability to fine-tune models on custom data

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Guaranteed data privacy and no data sharing