Generative AI Leader Fundamentals of Generative AI Practice Test 4 — 15 Questions

Question 1

A company fine-tunes a model using Vertex AI and notices the model's performance drops on the original training task (e.g., language understanding) after fine-tuning for a new task (e.g., summarization). What could be the cause?

Accepted Answer

Catastrophic forgetting. Catastrophic forgetting occurs when a neural network loses previously learned knowledge upon being fine-tuned on a new task. In this scenario, fine-tuning the model for summarization overwrites the weights responsible for language understanding, causing performance degradation on the original task. This is a well-known limitation of sequential fine-tuning in deep learning.

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Data leakage

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Model quantization

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Underfitting

Question 2

Which of the following is a best practice when using Vertex AI for prompt engineering?

Accepted Answer

Use consistent formatting and delimiters. Consistent formatting and delimiters (e.g., using triple backticks, XML tags, or clear section headers) help the model parse instructions and context reliably, reducing ambiguity and improving output quality. This is a core best practice in prompt engineering on Vertex AI because it leverages the model's attention mechanisms to focus on distinct prompt segments, leading to more predictable and accurate responses.

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Always set temperature to 0

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Avoid using examples in the prompt

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Use very long prompts to include all possible instructions

Question 3

Which TWO are benefits of using retrieval-augmented generation (RAG) over fine-tuning?

Accepted Answer

No need for training. Option A is correct because RAG does not require any training or fine-tuning of the underlying model. It works by retrieving relevant documents from an external knowledge base at inference time and providing them as context to the model, which generates an answer based on that context. This eliminates the need for costly and time-consuming model retraining or parameter updates.

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Higher accuracy on all tasks

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Reduced model size

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Lower latency

Question 4

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

Accepted Answer

Using a more diverse training dataset. Option C is correct because 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.

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Using only English prompts

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Increasing model size

Question 5

Which TWO are components of the Vertex AI Generative AI Studio?

Accepted Answer

Model Garden. Model Garden is a core component of Vertex AI Generative AI Studio that provides a curated repository of foundation models, including Google's PaLM and Gemini models, as well as third-party models. It allows users to discover, compare, and deploy these models directly within the studio environment, making it essential for generative AI workflows.

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Dataflow

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Pipeline templates

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Cloud Functions

Question 6

Refer to the exhibit. A developer runs this command. What is the primary purpose?

Accepted Answer

Upload a model artifact to Model Registry. The command shown in the exhibit is `az ml model create --name my-model --path ./model.pkl --registry-name myregistry`. This command uploads a local model artifact (model.pkl) to the Azure Machine Learning Model Registry, which is a centralized repository for versioning and managing trained models. It does not initiate training, deployment, or pipeline creation; its sole purpose is to register the model artifact for later use.

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Create a training pipeline

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Deploy a model to an endpoint

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Train a model

Question 7

Refer to the exhibit. A user with this IAM role tries to deploy a model to a Vertex AI Endpoint but fails. What is the most likely reason?

Accepted Answer

The user needs the roles/aiplatform.deployer role. The user has an IAM role but lacks the specific permission `aiplatform.deployments.create` required to deploy a model to a Vertex AI Endpoint. The `roles/aiplatform.deployer` role includes this permission, while the user's existing role does not, causing the deployment to fail. Even if the user can use other Vertex AI services, deploying a model to an endpoint is a distinct action that requires this specific role.

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The user is not authorized to use Vertex AI at all

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The model artifact is not in the same region as the endpoint

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The user needs the roles/aiplatform.admin role

Question 8

Refer to the exhibit. What is the most likely cause of this error?

Accepted Answer

The user does not have the required IAM role. The error shown in the exhibit is an HTTP 403 Forbidden response, which indicates that the server understood the request but refuses to authorize it. In Google Cloud, this is most commonly caused by the user's identity lacking the necessary IAM role or permission to call the specific API or access the resource. Even if the project ID is correct and the network is functional, a missing IAM role (e.g., `aiplatform.user` or `roles/aiplatform.user`) will result in this exact error.

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The model is too large

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The network is down

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The project ID is incorrect

Question 9

A company wants to use generative AI to summarize customer support tickets. Which Google Cloud tool is best suited for this task?

Accepted Answer

Vertex AI Text Generation (Gemini). Vertex AI Text Generation (Gemini) is the correct choice because it is a generative AI service specifically designed for natural language understanding and generation tasks, such as summarizing customer support tickets. Gemini models can process long-form text and produce concise, coherent summaries by leveraging transformer-based architectures fine-tuned for instruction-following and text completion. This makes it ideal for extracting key information from support conversations and generating actionable summaries.

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Dialogflow CX

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Document AI

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AutoML Tables

Question 10

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. Option C is correct because 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.

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Fine-tune the model on curated safe data

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Add a human-in-the-loop review

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Switch to a smaller model

Question 11

A company has a large dataset of proprietary documents and wants to build a Q&A system using a foundation model without exposing the documents to the model. Which approach is most appropriate?

Accepted Answer

Use RAG with Vertex AI Vector Search and embeddings. Option A is correct because Retrieval-Augmented Generation (RAG) with Vertex AI Vector Search allows the system to retrieve relevant document chunks via embeddings without exposing the full documents to the foundation model. The model only sees the retrieved context in the prompt, ensuring proprietary data remains isolated and not used for training or memorization.

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Use a zero-shot model with context in prompt

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Fine-tune the model on the documents

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Use prompt engineering to instruct the model

Question 12

A data scientist notices that a Gemini model generates inconsistent responses to similar prompts. What is the likely cause?

Accepted Answer

The top_p or temperature parameters are set too high causing randomness. Option D is correct because high temperature (e.g., >1.0) or high top_p (e.g., >0.9) increases the randomness of token sampling, causing the model to select less probable tokens. This directly leads to inconsistent responses for similar prompts, as the model's output distribution becomes more uniform and less deterministic.

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Model is not fine-tuned enough

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The prompt is too short

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The temperature setting is too low

Question 13

A company wants to generate images from text descriptions using Google Cloud. Which service should they use?

Accepted Answer

Vertex AI Imagen. Vertex AI Imagen is Google Cloud's purpose-built service for generating high-fidelity images from text descriptions using diffusion models. It directly addresses the requirement of text-to-image generation, offering capabilities like image editing, upscaling, and style transfer, which are not available in other Vertex AI or Vision services.

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Vertex AI Gemini

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Cloud Vision API

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AutoML Vision

Question 14

A team is building a medical diagnosis assistant using a foundation model. To comply with regulations, they need to ensure the model does not make up facts. What is the best approach?

Accepted Answer

Use grounding with Vertex AI Search. Grounding with Vertex AI Search is the best approach because it connects the foundation model's outputs to a verifiable, curated knowledge base, ensuring factual accuracy and compliance with regulations that prohibit hallucination. By retrieving information from a trusted source (e.g., medical databases) in real time, the model can cite evidence and avoid generating unverified claims.

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Use a small model to hallucinate less

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Reduce temperature to 0

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Fine-tune on medical journals

Question 15

After fine-tuning a foundation model on company emails, the model outputs confidential information. What is the most likely cause?

Accepted Answer

The fine-tuning dataset was not anonymized. Option C is correct because the most likely cause of a fine-tuned model outputting confidential information is that the fine-tuning dataset contained sensitive data that was not anonymized. During fine-tuning, the model learns patterns and can memorize specific sequences, including confidential details like names, addresses, or proprietary information, which it then reproduces in responses. This is a well-known data leakage risk in fine-tuning workflows.

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The prompt is too vague

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The model is too large

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Overfitting to the training data leading to memorization