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

Question 1

Refer to the exhibit. This IAM policy is applied to a Vertex AI project. A user 'test@example.com' reports they cannot create a ModelEvaluationPipelineJob. Which action should the administrator take?

Accepted Answer

Add the user to the roles/aiplatform.admin role at the project level.. Roles/aiplatform.user does not have the permissions to create pipeline jobs; it only allows viewing and using models and endpoints. Roles/aiplatform.admin has full control, so adding the user to this role is the simplest fix. There is no roles/aiplatform.specialist; removing the service account would not help; and granting at the model level is insufficient for creating pipeline jobs.

Answer

Grant the user roles/aiplatform.specialist at the project level.

Answer

Add the user roles/aiplatform.user at the model level to allow pipeline creation.

Answer

Remove the service account from roles/aiplatform.admin.

Question 2

Refer to the exhibit. A developer sees this error when trying to call a Vertex AI endpoint for online prediction. What permission does the requesting identity need to be granted?

Accepted Answer

aiplatform.endpoints.predict. The error occurs when calling a Vertex AI endpoint for online prediction, which requires the `aiplatform.endpoints.predict` permission. This permission is specifically scoped to the endpoint resource, allowing the identity to send prediction requests to a deployed model endpoint. The correct IAM role binding must include this permission for the requesting identity to successfully invoke the endpoint.

Answer

aiplatform.prediction.predict

Answer

aiplatform.endpoints.use

Answer

aiplatform.models.predict

Question 3

A startup wants to use a pre-trained model to generate product descriptions without training. Which Google Cloud service should they use?

Accepted Answer

Vertex AI Generative AI Studio. Vertex AI Generative AI Studio is the correct service because it provides a no-code interface to access and experiment with pre-trained generative models, including text generation for product descriptions, without requiring any training or custom model development. It allows users to directly prompt models like PaLM 2 or Gemini for inference tasks, making it ideal for generating content from a pre-trained model without training.

Answer

Vertex AI Prediction

Answer

AI Platform Training

Answer

Cloud AutoML

Question 4

A data scientist fine-tunes a large language model on Vertex AI but gets poor results on validation data. What is the most likely cause?

Accepted Answer

Insufficient training data. Fine-tuning a large language model on Vertex AI with poor validation results is most likely due to insufficient training data. Large language models have billions of parameters and require a substantial amount of high-quality, task-specific data to effectively adapt to a new domain or task; without enough examples, the model cannot learn the desired patterns and will perform poorly on unseen data.

Answer

Incorrect learning rate

Answer

Using wrong model family

Answer

Overfitting due to too many epochs

Question 5

A gen AI application produces hallucinations (factually incorrect outputs). Which mitigation strategy is LEAST effective?

Accepted Answer

Increasing model temperature. Increasing model temperature makes the model more random and creative, which directly increases the likelihood of hallucinations. It does not constrain or ground the output in factual data, making it the least effective mitigation strategy among the options.

Answer

Using prompt templates with constraints

Answer

Using grounding with a knowledge base

Answer

Implementing retrieval-augmented generation

Question 6

What is the purpose of grounding in Vertex AI?

Accepted Answer

To connect model outputs to verifiable sources. Grounding in Vertex AI connects model outputs to verifiable, external sources of information (such as Google Search, enterprise data sources, or third-party databases) to reduce hallucinations and improve factual accuracy. By referencing grounded sources, the model can provide citations and allow users to verify claims, which is critical for enterprise applications requiring trust and compliance.

Answer

To improve training speed

Answer

To reduce model size for faster inference

Answer

To enable multi-modal inputs

Question 7

A company wants to build a chatbot that answers questions based on internal documents. Which approach is most appropriate?

Accepted Answer

Use a prompt with the documents in the context. Retrieval-Augmented Generation (RAG) allows the chatbot to dynamically include relevant internal documents in the prompt context without modifying the underlying model. This approach leverages the pre-trained model's language understanding while grounding answers in specific, up-to-date internal data, avoiding the cost and latency of fine-tuning or retraining.

Answer

Use a pre-trained model without any customizations

Answer

Train a custom model from scratch

Answer

Fine-tune a model on the documents

Question 8

A developer uses Vertex AI to generate code but the output is not syntactically correct. Which parameter should be adjusted?

Accepted Answer

temperature. Temperature controls the randomness of token selection during generation. A high temperature increases the likelihood of less probable tokens, which can lead to syntactically incorrect code. Lowering temperature makes the model more deterministic and conservative, favoring higher-probability tokens that are more likely to form valid syntax.

Answer

candidate_count

Answer

max_output_tokens

Answer

top_k

Question 9

Which Google Cloud product provides access to pre-trained foundation models like Gemini?

Accepted Answer

Vertex AI Generative AI Studio. Vertex AI Generative AI Studio is the correct answer because it is the Google Cloud service specifically designed to provide access to pre-trained foundation models like Gemini, allowing users to test, customize, and deploy them via a managed interface. Unlike other services, Generative AI Studio directly integrates with Gemini's API and offers prompt engineering, tuning, and model evaluation capabilities.

Answer

Dataflow

Answer

Cloud Translation

Answer

Vertex AI Model Registry

Question 10

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.

Answer

Data leakage

Answer

Model quantization

Answer

Underfitting

Question 11

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.

Answer

Always set temperature to 0

Answer

Avoid using examples in the prompt

Answer

Use very long prompts to include all possible instructions

Question 12

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

Accepted Answer

No need for training. 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.

Answer

Higher accuracy on all tasks

Answer

Reduced model size

Answer

Lower latency

Question 13

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

Accepted Answer

Using a more diverse training dataset. 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.

Answer

Using only English prompts

Answer

Increasing model size

Question 14

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.

Answer

Dataflow

Answer

Pipeline templates

Answer

Cloud Functions

Question 15

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.

Answer

The user is not authorized to use Vertex AI at all

Answer

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

Answer

The user needs the roles/aiplatform.admin role