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

Question 1

A company is using Vertex AI to deploy a text generation model for a chatbot. They want to reduce the response latency. Which configuration change is most effective?

Accepted Answer

Use a smaller model variant. Using a smaller model variant directly reduces the number of parameters and computational operations required per inference, which lowers latency. In Vertex AI, smaller models like `text-bison@002` have fewer layers and attention heads than larger counterparts, resulting in faster token generation without requiring hardware changes.

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Enable model quantization

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Increase the number of GPUs

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Use a larger batch size

Question 2

A data scientist needs to fine-tune a foundation model for a sentiment analysis task without managing infrastructure. Which Google Cloud service should they use?

Accepted Answer

Vertex AI Model Garden. Vertex AI Model Garden is the correct service because it provides a curated hub of foundation models that can be fine-tuned with managed infrastructure, eliminating the need for the data scientist to provision or manage servers. It supports one-click deployment and fine-tuning workflows for sentiment analysis, directly addressing the requirement to avoid infrastructure management.

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Compute Engine

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BigQuery ML

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Cloud Run

Question 3

An organization wants to ensure their generative AI application does not produce toxic or harmful content. Which Vertex AI feature should they implement?

Accepted Answer

Safety filters and content moderation. Safety filters and content moderation in Vertex AI allow organizations to define and enforce policies that block or flag toxic, harmful, or inappropriate content generated by the model. This feature uses pre-built and customizable classifiers to evaluate prompts and responses against safety attributes (e.g., hate speech, harassment, sexually explicit content) before returning them to the user, directly addressing the requirement to prevent harmful outputs.

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Explainable AI

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AutoML

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Model Monitoring

Question 4

A team uses PaLM 2 API to generate product descriptions, but the output sometimes contains factual inaccuracies. What is the best approach to improve accuracy?

Accepted Answer

Use grounding with Google Search. Grounding with Google Search is the correct approach because it allows the PaLM 2 API to retrieve real-time, verifiable information from the web, directly reducing factual inaccuracies in generated product descriptions. Unlike parameter adjustments, grounding provides an external knowledge source that the model can cite, ensuring outputs are based on current and accurate data rather than relying solely on its training data.

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Increase the temperature parameter

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Reduce the top_k value

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Set the max_output_tokens higher

Question 5

A developer wants to build a RAG application using Vertex AI. Which vector database is natively integrated with Vertex AI for storing embeddings?

Accepted Answer

Vertex AI Vector Search. Vertex AI Vector Search is the native vector database integrated with Vertex AI for storing and querying embeddings. It is purpose-built for high-dimensional vector similarity search, enabling efficient retrieval in RAG applications without requiring external infrastructure.

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Firestore

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Cloud SQL

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Bigtable

Question 6

During model evaluation, a team observes good performance on training data but poor on validation data. Which regularization technique is most appropriate to address this?

Accepted Answer

Apply dropout. The scenario describes overfitting, where the model memorizes training data but fails to generalize to unseen validation data. Dropout is a regularization technique that randomly deactivates a fraction of neurons during training, forcing the network to learn more robust features and reducing co-adaptation, which directly mitigates overfitting.

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Add more training data

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Increase the learning rate

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Use a larger batch size

Question 7

A financial institution deploys a chatbot using Gemini Pro in Vertex AI. Compliance requires logging all user inputs and model outputs for audit. Which approach meets this requirement?

Accepted Answer

Enable Vertex AI Endpoint request-response logging. Vertex AI Endpoint request-response logging captures both the user's input prompt and the model's generated output, which is precisely what compliance auditing requires. This feature logs the exact payloads sent to and received from the deployed model, ensuring a complete audit trail without additional configuration.

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Capture logs via Cloud Monitoring

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Use Cloud Logging sink with a filter for Vertex AI requests

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Enable Vertex AI Model Registry logging

Question 8

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 9

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 10

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 11

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. 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 12

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. 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 13

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. 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 14

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 15

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