Generative AI Leader Fundamentals of Generative AI Practice Test 5 — 15 Questions

Question 1

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 2

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 3

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.

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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 4

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. 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 5

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. 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 6

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 7

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 8

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. 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

Question 9

A company is deploying a chatbot that uses a foundation model. They want to minimize latency for user queries. Which action is most effective?

Accepted Answer

Use a smaller distilled model. Distilled models are smaller, faster versions of larger foundation models, trained to mimic their behavior while requiring fewer computational resources. This directly reduces inference latency because fewer parameters mean faster forward passes through the network, which is critical for real-time chatbot responses.

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Use a larger model with more parameters

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Disable safety filters

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Increase the number of tokens

Question 10

A team is evaluating generative AI models on Vertex AI. They need to compare models based on specific criteria. Which TWO criteria are most important for selecting a model for a text summarization task?

Accepted Answer

ROUGE scores. ROUGE scores are the standard evaluation metric for text summarization tasks, measuring the overlap of n-grams, word sequences, and word pairs between generated summaries and reference summaries. This directly quantifies summary quality, making it the most important criterion for model selection.

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Training dataset size

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Cost per token

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Model size in parameters

Question 11

A company is fine-tuning a Gemma model using Vertex AI. They observe that the model overfits. Which TWO actions should they take to mitigate overfitting?

Accepted Answer

Use more diverse data. Introducing more diverse data helps the model generalize better by exposing it to a wider variety of patterns, reducing the risk of memorizing noise from a limited dataset. Option E is correct because dropout randomly deactivates a fraction of neurons during fine-tuning, which prevents co-adaptation and acts as a regularization technique to combat overfitting in transformer-based models like Gemma.

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Use a larger batch size

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Increase the number of training epochs

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Reduce the learning rate

Question 12

Which THREE components are core to a typical Retrieval Augmented Generation (RAG) system?

Accepted Answer

Vector database. A vector database (B) is core to a RAG system because it stores and indexes embeddings of external knowledge chunks, enabling efficient similarity search to retrieve the most relevant context for a user query. This retrieved context is then provided to the LLM to ground its response in factual data, reducing hallucinations and improving accuracy.

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Classifier

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Rewriter

Question 13

Refer to the exhibit. A developer creates a model resource with this YAML config but gets an error that the model is not deployable. What is missing?

Accepted Answer

container_spec. The error 'model is not deployable' occurs because the YAML config lacks a `container_spec` field. In Vertex AI, a model must specify how to serve predictions—either via a pre-built container (using `container_spec`) or a custom container. Without this, the model has no runtime environment and cannot be deployed to an endpoint.

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model_type

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artifact_uri

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description

Question 14

Refer to the exhibit. A developer sees this error when trying to deploy a model from Vertex AI Model Registry. What is the most likely cause?

Accepted Answer

The developer used the model display name instead of the full resource name. The error occurs because Vertex AI Model Registry requires the full resource name (e.g., 'projects/{project}/locations/{region}/models/{model_id}') to deploy a model, not just the display name. The display name is a human-readable label that is not unique within a project, while the full resource name uniquely identifies the model version. Using the display name causes the API to fail with a 'not found' or 'invalid argument' error.

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The region is not supported

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The model is not published

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The model is in a different project

Question 15

Refer to the exhibit. A developer runs this command but forgets to specify the model name. What will happen?

Accepted Answer

The command will fail with an error. In the context of the `gcloud ai models upload` command (or similar model deployment commands in Vertex AI), the model name is a required positional argument. If omitted, the CLI will fail with an error because it cannot proceed without a unique identifier to register the model in the model registry. The command does not default to any name or prompt interactively; it strictly validates required parameters before execution.

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The command will prompt for a name

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The model will be uploaded with a default name

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The command will succeed but the model will be unlisted