Generative AI Leader Practice Test 48 — 15 Questions

Question 1

A company with limited AI expertise wants to adopt gen AI. They need a solution that integrates with existing data and applications. Which Google Cloud offering is best?

Accepted Answer

Vertex AI Agent Builder. Vertex AI Agent Builder is designed for building conversational AI agents with easy integration to enterprise data sources. Option A is wrong because Colab Enterprise is a notebook environment, not a full solution. Option B is wrong because Apigee is an API management platform. Option C is wrong because BigQuery ML is for SQL-based ML, not gen AI agents.

Answer

Apigee

Answer

Colab Enterprise

Answer

BigQuery ML

Question 2

Refer to the exhibit. A team has deployed a model to an endpoint with the configuration shown. They notice that during peak traffic, the endpoint frequently returns 429 (Too Many Requests) errors. Which action should they take to resolve this issue?

Accepted Answer

Increase MIN_REPLICA_COUNT to 5. Increasing MIN_REPLICA_COUNT ensures a minimum number of replicas are always available to handle traffic bursts, reducing 429 errors. Other options would not help or would worsen the problem.

Answer

Change MACHINE_TYPE to n1-highmem-4

Answer

Decrease MAX_REPLICA_COUNT to 1

Answer

Disable autoscaling by setting MIN_REPLICA_COUNT equals MAX_REPLICA_COUNT

Question 3

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.

Answer

Fine-tune a model on the knowledge base

Answer

Train a new model from scratch

Answer

Use zero-shot prompting with no context

Question 4

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 5

You want to use a Google foundation model to generate text summaries of news articles. Which Vertex AI service should you use?

Accepted Answer

Vertex AI Generative AI Studio. Vertex AI Generative AI Studio (now part of Vertex AI Agent Builder) provides a no-code/low-code environment to access, test, and tune Google's foundation models, including PaLM 2 and Gemini, specifically for generative tasks like text summarization. It offers built-in prompt templates and safety settings tailored for summarization use cases, making it the correct service for this task.

Answer

Vertex AI Prediction

Answer

Vertex AI Model Registry

Answer

Vertex AI Feature Store

Question 6

A company deploys a fine-tuned text generation model on Vertex AI Endpoints. They want to monitor for data drift and performance degradation over time. Which GCP service should they integrate?

Accepted Answer

Vertex AI Model Monitoring. Vertex AI Model Monitoring is the correct choice because it is specifically designed to detect data drift (changes in input data distribution) and feature attribution drift in deployed models, including fine-tuned text generation models on Vertex AI Endpoints. It provides automated alerts when model performance degrades due to shifts in production data, enabling proactive retraining or intervention.

Answer

Cloud Monitoring

Answer

Cloud Logging

Answer

Vertex AI Experiments

Question 7

A developer is using the Vertex AI PaLM API and receives a 429 Resource Exhausted error. What is the most likely cause?

Accepted Answer

The user has exceeded the allowed number of requests per minute. 429 errors indicate rate limiting or quota exhaustion for the API.

Answer

The request payload is too large

Answer

The model is not available in the current region

Answer

The API key is invalid

Question 8

Refer to the exhibit. A team's IAM policy for Vertex AI includes the following binding. They can deploy models but cannot create tuning jobs. Which statement is true?

Accepted Answer

The aiplatform.user role lacks permission to create tuning jobs. The roles/aiplatform.user role does not include permission to create tuning jobs (aiplatform.tuningJobs.create). The modelUser role does not override the user role, admin role is not needed, and specialist role doesn't exist.

Answer

The developer needs the aiplatform.admin role

Answer

The aiplatform.user role overrides the modelUser role

Answer

The policy is missing the aiplatform.specialist role

Question 9

A startup wants to quickly prototype a multimodal AI application that can process images and text using Gemini. They have minimal budget and need a free tier for initial development. Which access tier should they use?

Accepted Answer

Google AI Studio. Google AI Studio is the correct choice because it offers a free tier specifically designed for rapid prototyping with Gemini models, including multimodal capabilities for processing images and text. It provides a web-based interface and API access without requiring a billing account, making it ideal for startups with minimal budget. Vertex AI, while powerful, requires a paid Google Cloud project and is intended for production deployment, not free-tier prototyping.

Answer

Vertex AI

Answer

Cloud TPU

Answer

TensorFlow Hub

Question 10

A content generation model for e-commerce product descriptions repeats the same phrases across multiple descriptions (e.g., 'high-quality', 'best-in-class'). The team wants more varied and engaging output. Which parameter adjustment is most appropriate?

Accepted Answer

Increase the frequency penalty parameter to 1.0.. Increasing the frequency penalty to 1.0 penalizes tokens that have already appeared in the generated text, directly reducing repetition of phrases like 'high-quality' and 'best-in-class'. This encourages the model to use more diverse vocabulary and sentence structures, leading to varied and engaging product descriptions.

Answer

Decrease the max output tokens to 50.

Answer

Increase the temperature parameter to 1.5.

Answer

Set the top-p value to a very small number like 0.1.

Question 11

Refer to the exhibit. A data scientist sends a prediction request to a text generation model with the following parameters and receives repetitive output. Which parameter should be changed?

Accepted Answer

Increase temperature to 0.5. Temperature 0.0 makes the model deterministic, leading to repetitive text. Increasing temperature to 0.5 introduces randomness. Decreasing topP may help but temperature is the direct cause. Increasing topK adds diversity but less effect, decreasing max tokens doesn't fix repetition.

Answer

Decrease topP to 0.5

Answer

Increase topK to 100

Answer

Decrease maxOutputTokens

Question 12

A healthcare startup has fine-tuned a Vertex AI PaLM 2 model on a dataset of medical records to generate patient summaries. The model produces fluent text but occasionally fabricates diagnoses not present in the input. The team has already tried increasing the training data size by 20% and adjusting the temperature from 0.7 to 0.2, but hallucinations persist. The summaries must be factually accurate for regulatory compliance. What should the team do next?

Accepted Answer

Implement a RAG pipeline using Vertex AI Search to retrieve relevant medical documents before generation.. Implementing a Retrieval-Augmented Generation (RAG) pipeline with Vertex AI Search grounds the model's output in retrieved, authoritative medical documents. This directly addresses the root cause of hallucination—the model's reliance on its parametric memory—by providing factual context at inference time, which is far more effective for regulatory compliance than adjusting generation parameters or training data size alone.

Answer

Increase the maximum output tokens to allow the model to generate more detailed summaries.

Answer

Add more few-shot examples to the prompt for each generation.

Answer

Switch the base model to Gemini 1.5 Pro without additional changes.

Question 13

A data scientist wants to train a large transformer model from scratch on custom data. They anticipate the training will require thousands of TPU-v5e chips for several weeks. Which Google Cloud infrastructure component is designed for this scale?

Accepted Answer

TPU v5e Pod slice (e.g., 256-chip pod). A is correct because a TPU v5e Pod slice (e.g., a 256-chip pod) is specifically designed for large-scale distributed training, providing high-bandwidth inter-chip interconnect (ICI) and collective communication optimizations that enable efficient scaling across thousands of chips for weeks-long training runs. This infrastructure component is purpose-built for the massive parallelism and fault tolerance required when training large transformer models from scratch on custom data.

Answer

BigQuery ML

Answer

Single TPU v5e device

Answer

Compute Engine with A100 GPU clusters

Question 14

A data scientist is fine-tuning a generative AI model for customer sentiment analysis. To ensure the fine-tuned model does not inadvertently memorize and reproduce personally identifiable information (PII) from the training data, which THREE practices should they follow? (Select 3)

Accepted Answer

Apply differential privacy during fine-tuning. Differential privacy limits memorization; PII removal prevents it from being present; data minimization reduces risk. Quantization and k-fold cross-validation do not directly address PII memorization.

Answer

Apply model quantization to reduce model size

Answer

Use k-fold cross-validation to evaluate the model

Question 15

Which Google initiative provides a set of interactive, open-source tools to help UX designers and product managers build human-centered AI products?

Accepted Answer

People + AI Guidebook (PAIR). The People + AI Guidebook (PAIR) is a Google initiative that provides guidelines, case studies, and design patterns for building human-centered AI.

Answer

TensorFlow Privacy

Answer

Model Cards

Answer

Datasheets for Datasets