Generative AI Leader Fundamentals of Generative AI Practice Test 6 — 15 Questions

Question 1

A research team is training a large language model from scratch using TPUs on Google Cloud. Which storage solution provides the highest throughput for training data?

Accepted Answer

Cloud Storage. Cloud Storage provides the highest throughput for training data because it is designed for high-bandwidth, parallel access from TPU pods via the Google Cloud Storage FUSE or gRPC-based data loading. TPUs benefit from Cloud Storage's ability to serve data at hundreds of GB/s when using the `tf.data` service with `tf.io.gfile` or the `gcloud storage` API, avoiding the I/O bottlenecks of block storage. Persistent Disk and Filestore have lower aggregate throughput limits and are not optimized for the distributed, streaming read patterns typical of large-scale training.

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Persistent Disk

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Cloud Filestore

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Cloud Spanner

Question 2

An MLOps engineer wants to implement continuous evaluation of a generative model in production. Which Vertex AI component should they use?

Accepted Answer

Vertex AI Model Monitoring. Vertex AI Model Monitoring is the correct component because it provides continuous evaluation of model performance in production, including detecting prediction drift, data drift, and feature attribution drift. For generative models, it can monitor output quality and safety metrics over time, alerting engineers to degradation or shifts in model behavior without requiring manual intervention.

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Vertex AI Feature Store

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Vertex AI Prediction

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Vertex AI Pipelines

Question 3

A company is designing a prompt engineering strategy for a customer service chatbot using Gemini. Which two practices are recommended for improving response quality? (Choose TWO)

Accepted Answer

Use chain-of-thought prompting. Chain-of-thought prompting (A) is recommended because it guides the model to reason step-by-step, improving accuracy on complex customer service queries by breaking down multi-step problems. This technique leverages Gemini's ability to follow logical sequences, reducing errors in tasks like troubleshooting or escalation decisions.

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Always provide multiple examples in the prompt

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Avoid any context in the prompt

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Set temperature to 1.0 for maximum creativity

Question 4

A developer is using Vertex AI Studio to test a text generation model. Which two actions can be performed in Vertex AI Studio? (Choose TWO)

Accepted Answer

Deploy a model to an endpoint. Option D is correct because Vertex AI Studio provides a direct interface to deploy a text generation model to an endpoint for serving predictions. This action is a core capability of the platform, allowing developers to test and then operationalize their models without leaving the Studio environment.

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Manage IAM roles

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Monitor model cost

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Create a dataset

Question 5

A company is migrating an on-premises NLP pipeline to Vertex AI. Which three capabilities of Vertex AI align with common MLOps best practices for generative AI? (Choose THREE)

Accepted Answer

Automatic model retraining based on performance degradation. Option A is correct because Vertex AI's model monitoring can automatically trigger retraining when performance metrics (e.g., prediction drift or data drift) degrade below a threshold. This aligns with MLOps best practices for maintaining generative AI model quality over time without manual intervention.

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Local on-premises execution

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Manual data labeling only

Question 6

Refer to the exhibit. A developer executed the command to list endpoints. They notice that two models are deployed to the same endpoint. What is the most likely reason for this configuration?

Accepted Answer

It is a canary deployment with traffic splitting. A is correct because deploying two models to the same endpoint with traffic splitting is a standard canary deployment strategy. In this configuration, a small percentage of inference requests are routed to the new model while the majority go to the stable model, allowing validation of the new model's performance before full rollout. This is commonly supported by model serving platforms like Amazon SageMaker, where you can specify a production variant with a traffic weight (e.g., 90%) and a canary variant with a lower weight (e.g., 10%).

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The endpoint is misconfigured and will cause conflicts

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The models are from different frameworks

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It is a batch prediction endpoint

Question 7

Refer to the exhibit. An administrator creates this IAM policy for a Vertex AI project. What is the effect of this policy?

Accepted Answer

Alice can deploy pre-trained models; Bob can create and manage custom model code. Option B is correct because the IAM policy grants Alice the `aiplatform.models.get` permission (allowing her to view and deploy pre-trained models) and grants Bob the `aiplatform.models.create` and `aiplatform.models.update` permissions (allowing him to create and manage custom model code). The policy uses separate bindings for each user, with specific roles that align with these actions.

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Alice can view models; Bob can delete models

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Both have full access to all Vertex AI resources

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Alice can train models; Bob can deploy models

Question 8

Refer to the exhibit. A machine learning engineer is configuring a model using this YAML. What is the purpose of the 'tuningPipeline' field?

Accepted Answer

It specifies a pipeline to fine-tune the base model. The 'tuningPipeline' field in this YAML configuration specifies a dedicated pipeline for fine-tuning the base model, which is a common practice in MLOps frameworks like Vertex AI Pipelines or Kubeflow. It allows the engineer to define a separate workflow for parameter-efficient fine-tuning (e.g., LoRA) or full fine-tuning, distinct from training from scratch or serving. This field is essential for orchestrating the fine-tuning process, including data preprocessing, training, and evaluation steps, without affecting the base model's original weights.

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It configures the model for online prediction

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It defines the hyperparameters for training from scratch

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It sets the model for batch prediction

Question 9

A company wants to use a pre-trained language model for customer support summarization. They need to ensure responses are concise and accurate. Which prompt engineering technique is most effective?

Accepted Answer

Few-shot prompting with examples. Few-shot prompting (B) is most effective because it provides the model with a small set of example input-output pairs (e.g., a customer query and its concise summary), which guides the model to produce outputs that match the desired format, length, and accuracy. This technique is particularly useful for summarization tasks where consistency and adherence to a specific style are critical, as it reduces ambiguity without requiring fine-tuning.

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Zero-shot prompting

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Chain-of-thought prompting

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Negative prompting

Question 10

A developer is using Vertex AI Generative AI Studio to fine-tune a PaLM 2 model for code generation. After training, they notice the model generates plausible but incorrect code. What is the most likely cause?

Accepted Answer

Overfitting to training data. When a PaLM 2 model generates plausible but incorrect code after fine-tuning, the most likely cause is overfitting to the training data. Overfitting occurs when the model memorizes specific code patterns, syntax, or even bugs from the fine-tuning dataset rather than learning generalizable programming logic. This results in outputs that look syntactically correct and contextually relevant but fail to execute properly or solve the intended problem, because the model has not learned the underlying algorithmic principles.

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Insufficient training steps

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Hallucination due to lack of grounding

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Prompt format mismatch

Question 11

A company wants to build a chatbot that answers questions using their internal knowledge base. Which approach is most suitable?

Accepted Answer

Use Retrieval-Augmented Generation (RAG). Retrieval-Augmented Generation (RAG) combines retrieval of relevant documents from a knowledge base with generative responses, making it ideal for this use case.

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Fine-tune a model on the knowledge base

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Train a new model from scratch

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Use zero-shot prompting with no context

Question 12

A company is deploying a generative AI model for medical advice. What is the most important consideration?

Accepted Answer

Safety and fairness. In medical advice applications, a generative AI model's outputs can directly impact patient health, making safety and fairness the paramount consideration. Incorrect or biased advice could lead to misdiagnosis or harm, outweighing performance metrics like latency or cost. Regulatory frameworks such as HIPAA and FDA guidelines for clinical decision support further mandate rigorous validation of model safety and fairness before deployment.

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Model latency

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Model size

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Cost of inference

Question 13

A team uses Vertex AI to host a large language model. They want to reduce latency for real-time applications. What is the best strategy?

Accepted Answer

Use model quantization. Option C is correct because model quantization reduces the precision of the model's weights (e.g., from FP32 to INT8), which decreases memory footprint and computational requirements, directly lowering inference latency for real-time applications on Vertex AI. This is a standard optimization technique for deploying large language models with minimal accuracy loss while meeting latency SLAs.

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Increase number of replicas

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Switch to a smaller model

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Use batch prediction instead of online

Question 14

A company has fine-tuned a foundation model on proprietary data. During evaluation, they find the model performs well on seen examples but poorly on unseen but similar tasks. What is the problem?

Accepted Answer

Domain shift between fine-tuning and deployment. Option D is correct because the model performs well on seen examples (fine-tuning distribution) but poorly on unseen but similar tasks (deployment distribution), which is a classic symptom of domain shift. This occurs when the fine-tuning data does not fully represent the deployment environment, causing the model to fail on inputs that differ in subtle but systematic ways from the training distribution. The model has not generalized to the target domain despite being well-fitted to the source domain.

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Underfitting

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Catastrophic forgetting

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Distribution shift

Question 15

Which Google Cloud service provides a managed environment for prompt engineering and model evaluation?

Accepted Answer

Vertex AI Generative AI Studio. Vertex AI Generative AI Studio is the correct answer because it is a managed service within Vertex AI specifically designed for prompt engineering, model tuning, and evaluation of generative AI models. It provides a no-code interface for testing prompts, comparing model outputs, and iterating on prompt design, directly supporting the workflow described in the question.

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AI Platform Notebooks

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Dialogflow CX

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Cloud Composer