Generative AI Leader Practice Test 3 — 25 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.

Answer

Number of layers in the model

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Brand reputation of the model provider

Answer

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

Answer

Increase the max output tokens

Answer

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

Answer

Manually scale instances based on expected load

Answer

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.

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 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

Answer

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.

Answer

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.

Answer

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

Answer

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.

Answer

Lower cost compared to using generic APIs

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Ability to fine-tune models on custom data

Answer

Guaranteed data privacy and no data sharing

Question 11

An organization is deploying a summarization model on Vertex AI and needs to ensure that the model's responses are consistent and avoid hallucinations. They have a labeled dataset of source documents and human-written summaries. Which approach would best align the model with their quality requirements?

Accepted Answer

Perform supervised fine-tuning using their labeled dataset. Supervised fine-tuning (D) directly optimizes the model's weights using the labeled dataset of source documents and human-written summaries, which teaches the model to produce consistent, factual outputs and reduces hallucinations. This approach aligns the model's behavior with the specific quality requirements by learning from ground-truth examples, unlike prompt engineering or parameter adjustments that do not modify the underlying model.

Answer

Deploy the model with a larger max_output_tokens

Answer

Use prompt engineering with few-shot examples

Answer

Increase the temperature to 0.9

Question 12

A company is deploying a generative AI system for medical diagnosis. Which TWO measures are essential for responsible AI in this high-stakes domain?

Accepted Answer

Ensure a human medical professional reviews all AI-generated diagnoses. High-stakes domains require human oversight and transparency. Human review ensures accountability, and Model Cards communicate capabilities and limitations to stakeholders.

Answer

Allow the AI to make autonomous decisions in time-sensitive emergencies

Answer

Publish all patient data used for training to ensure transparency

Answer

Use the AI only for administrative tasks, not diagnosis

Question 13

A team deployed a custom generative AI model using KServe on Google Kubernetes Engine (GKE) with the above configuration. They notice that the model is taking longer than expected to respond. What is the most likely cause?

Accepted Answer

The model requires more than 1 GPU for acceptable performance. The configuration specifies 1 GPU, but the model requires more than 1 GPU for acceptable performance. KServe on GKE allocates GPU resources based on the `limits` field; if the model's inference workload exceeds the memory bandwidth or compute capacity of a single GPU, latency increases due to queuing and serialization. This is the most likely cause of the slow response time, as GPU-bound models are sensitive to under-provisioning.

Answer

The CPU resource limits are too low

Answer

The model is crashing due to insufficient memory

Answer

The container image is too large and takes time to pull

Question 14

A company uses a generative model to produce product descriptions. The descriptions are factually inconsistent with the product specs. Which technique would best ensure factual accuracy?

Accepted Answer

Implement retrieval-augmented generation (RAG) with product database. Retrieval-augmented generation (RAG) is the best technique because it dynamically retrieves relevant, up-to-date product specifications from a trusted database at inference time, grounding the model's output in verified facts. This directly addresses factual inconsistency by ensuring the generated description is based on authoritative source data rather than relying solely on the model's parametric memory.

Answer

Enhance the system prompt with product details

Answer

Lower the temperature to 0.0

Answer

Fine-tune the model on product descriptions

Question 15

A financial services firm needs to deploy a large language model (LLM) for analyzing sensitive client documents. They require the model to run within their Virtual Private Cloud (VPC) with no internet access and must comply with data residency regulations. Which Google Cloud generative AI offering should they use?

Accepted Answer

Vertex AI Model Garden with private endpoints and VPC Service Controls. Vertex AI Model Garden with private endpoints and VPC Service Controls allows the LLM to be deployed entirely within the customer's VPC, with no internet egress, and enforces data residency by restricting data movement to the configured VPC boundary. Private endpoints use Private Service Connect to route inference traffic through internal IPs, while VPC Service Controls prevent data exfiltration and ensure compliance with residency regulations.

Answer

Vertex AI Search

Answer

Cloud Run

Answer

Vertex AI Workbench

Question 16

A retail company wants to build a customer service chatbot that can handle returns, order status, and FAQs. They need to integrate with their existing backend systems. Which Google Cloud service should they use?

Accepted Answer

Vertex AI Agent Builder. Vertex AI Agent Builder is the correct choice because it provides a low-code platform specifically designed for building conversational AI agents (chatbots) that can be integrated with enterprise backend systems via APIs, connectors, and custom tools. It supports grounding in enterprise data, multi-turn dialogue management, and seamless integration with existing systems for handling returns, order status, and FAQs, making it the most suitable service for this use case.

Answer

Vertex AI Model Garden

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Vertex AI Search

Answer

Vertex AI Codey API

Question 17

Refer to the exhibit. The endpoint is experiencing high latency during traffic spikes. The team wants to improve response time by reducing queueing. Which change to the configuration would be most effective?

Accepted Answer

Increase maxReplicaCount to 10. Increasing maxReplicaCount to 10 allows the autoscaler to provision more replicas during traffic spikes, distributing the incoming requests across additional endpoints. This directly reduces queueing at each replica because the load is spread over more instances, lowering per-instance latency. The change targets the root cause—insufficient capacity to handle peak load—rather than adjusting thresholds or model versions.

Answer

Decrease minReplicaCount to 0

Answer

Change the model version to '2'

Answer

Decrease the target value in autoscaling metric to 50

Question 18

A retail company wants to deploy a generative AI chatbot to assist customers with product recommendations. The chatbot must align with the company's brand voice and provide accurate, up-to-date information. Which strategy should the company prioritize when developing this solution?

Accepted Answer

Ground the model with proprietary product data and brand guidelines in a retrieval-augmented generation (RAG) architecture.. Retrieval-augmented generation (RAG) allows the chatbot to ground its responses in the company's proprietary product data and brand guidelines, ensuring factual accuracy and brand consistency. By retrieving relevant information from a curated knowledge base at inference time, the model can provide up-to-date recommendations without requiring retraining, which is critical for a retail environment with frequently changing inventory.

Answer

Use a generic pre-trained model without customization to reduce development time.

Answer

Deploy a large language model with a feedback loop to iteratively improve responses.

Answer

Train the model on public customer reviews to capture common preferences.

Question 19

A data analyst wants to experiment with ML models using a free, cloud-based Jupyter notebook environment with GPU support. Which Google tool should they use?

Accepted Answer

Google Colab. Google Colab is a free, cloud-based Jupyter notebook environment that provides access to GPUs (e.g., NVIDIA T4 or V100) without requiring any setup or billing. It is specifically designed for experimentation and learning with ML models, making it the correct choice for a data analyst seeking a free, GPU-enabled notebook environment.

Answer

Cloud Shell

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BigQuery Studio

Answer

Vertex AI Workbench

Question 20

Which TWO Google Cloud services can be used together to implement a RAG (retrieval-augmented generation) pipeline? (Select 2)

Accepted Answer

Vertex AI Vector Search. Vertex AI Vector Search (option B) is correct because it provides a managed vector database for storing and querying embeddings, which is essential for the retrieval step in a RAG pipeline. It enables semantic similarity search over large datasets, allowing the system to fetch relevant context documents based on a user query.

Answer

Cloud SQL

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Bigtable

Answer

Cloud Functions