Generative AI Leader Practice Test 6 — 10 Questions

Question 1

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.

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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 2

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

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Increase the max output tokens

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Set safety settings to block all categories

Question 3

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 4

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

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Manually scale instances based on expected load

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Serve the model directly without optimization

Question 5

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.

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One-click deployment to a Vertex AI endpoint

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Fine-tuning models directly in the interface

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Building conversational agents with drag-and-drop

Question 6

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

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Set the temperature parameter to a high value

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Add the instruction in the 'Context' parameter

Question 7

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.

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The model endpoint is configured for English only

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The temperature is too high, causing random outputs

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The maxOutputTokens is too low to complete the translation

Question 8

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.

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Sentiment analysis

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Code execution

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Knowledge base integration

Question 9

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

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Multimodal capability: Flash does not support image input

Question 10

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.

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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