Google Cloud Generative AI Leader Generative AI Leader Generative AI Leader Questions 676–683 | Page 10/10

676

MCQmedium

A healthcare company is developing a generative AI system to assist radiologists by highlighting potential abnormalities in X-ray images. They want to ensure the system's outputs are explainable and can be verified by medical professionals. Which combination of features should they use?

A.AutoML Tables and Vertex Explainable AI

B.Model Cards and Datasheets for Datasets

C.Vertex AI Model Registry and Feature Store

D.Grounding (citing sources) and chain-of-thought reasoning

AnswerD

Grounding provides source references; chain-of-thought shows reasoning steps, enabling radiologists to verify outputs.

Why this answer

Grounding (citing sources) and chain-of-thought reasoning provide explainability by showing the reasoning steps and source evidence. Confidence indicators help radiologists assess reliability. The other options either lack explainability or are less suitable for medical decision support.

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677

MCQhard

A large enterprise is deploying a generative AI-powered code assistant for their developers. The solution uses Vertex AI with a fine-tuned Codey model. The security team requires that all prompts and responses be logged for audit purposes, but the logs must not contain sensitive information such as API keys or passwords. The operations team is concerned about high latency during peak usage. You need to design a solution that meets security requirements without compromising performance. Which approach should you take?

A.Use Cloud Audit Logs to capture all API calls to Vertex AI, but do not log the actual prompts and responses

B.Enable Vertex AI model monitoring with Cloud Logging, and configure a log sink with a custom exclusion filter to redact sensitive patterns before storing

C.Log all prompts and responses to Cloud Storage and use a Cloud DLP job to scan and redact sensitive data periodically

D.Implement a custom proxy that logs all requests after stripping sensitive data, then forward to the model

AnswerB

This ensures all interactions are logged but sensitive data is removed, meeting security without major performance impact.

Why this answer

It uses Vertex AI model monitoring with Cloud Logging to capture prompts and responses, then applies a custom exclusion filter with a log sink to redact sensitive patterns (e.g., API keys, passwords) in real time before logs are stored. This meets the security requirement for audit logging without sensitive data while avoiding the latency overhead of post-processing or a custom proxy, thus satisfying the operations team's performance concern.

Exam trap

Google Cloud often tests the misconception that post-processing redaction (e.g., Cloud DLP) or custom proxies are acceptable for real-time logging, when in fact native streaming redaction via log sinks is required to meet both security and performance constraints.

How to eliminate wrong answers

Option A is wrong because Cloud Audit Logs capture only administrative actions (e.g., model deployment) and not the actual prompts and responses, failing the audit requirement. Option C is wrong because logging all data to Cloud Storage and running a periodic Cloud DLP job introduces significant latency and potential exposure window between logging and redaction, violating the performance requirement. Option D is wrong because implementing a custom proxy adds network hop latency and operational overhead, degrading performance during peak usage, and does not leverage native Vertex AI logging capabilities.

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678

MCQeasy

A data scientist wants to compare the performance of three different foundation models for a text summarization task. They have a labeled dataset of summaries. Which Vertex AI tool should they use to perform this evaluation?

A.Vertex AI RAG Engine - Retrieval evaluation

B.Vertex AI Agent Builder - Agent evaluation

C.Model Garden - Model comparison view

D.Vertex AI Studio - Evaluation

AnswerD

Vertex AI Studio includes evaluation capabilities to compare model responses against ground truth data.

Why this answer

Vertex AI Studio provides a model evaluation interface where you can compare model outputs using automatic metrics. Model Garden is a model hub. Agent Builder is for building agents.

RAG Engine is for retrieval.

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679

MCQmedium

A research team at a university is exploring protein folding predictions. They need access to state-of-the-art models and large-scale computational resources at no cost for academic use. Which Google Cloud offering should they use?

A.Colab notebooks with AlphaFold

B.Google Cloud TPU v5e pods via Cloud TPU API

C.Vertex AI Model Registry with AlphaFold as a pre-trained model

D.BigQuery ML with a custom protein folding model

AnswerA

AlphaFold notebooks are available on Colab, providing free GPU resources for protein folding predictions in an academic setting.

Why this answer

Google DeepMind's AlphaFold provides cutting-edge protein structure predictions. Colab offers free GPU notebooks, and AlphaFold is available as a Colab notebook for academic use.

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680

MCQeasy

Which Google DeepMind achievement is recognized for predicting protein structures and advancing drug discovery?

A.AlphaFold

B.AlphaGo

C.Gemini

D.AlphaCode

AnswerA

AlphaFold predicts protein 3D structures from amino acid sequences, revolutionizing biology and drug discovery.

Why this answer

AlphaFold is the correct answer because it is a groundbreaking AI system developed by Google DeepMind that predicts the 3D structure of proteins from their amino acid sequences with atomic-level accuracy. This achievement has revolutionized computational biology by solving a 50-year-old grand challenge in molecular biology, enabling significant advances in drug discovery, enzyme design, and understanding disease mechanisms.

Exam trap

The Generative AI Leader exam often tests the distinction between domain-specific AI achievements (like AlphaFold for biology) versus general-purpose AI models (like Gemini) or game-playing AI (like AlphaGo), so candidates may confuse the application area of each DeepMind project.

How to eliminate wrong answers

Option B is wrong because AlphaGo is an AI program that mastered the board game Go using deep reinforcement learning and Monte Carlo tree search, not protein structure prediction. Option C is wrong because Gemini is a multimodal large language model family designed for text, image, audio, and video understanding, not for structural biology or drug discovery. Option D is wrong because AlphaCode is an AI system for competitive programming that generates code solutions to algorithmic problems, not for predicting protein structures.

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681

MCQeasy

Which Google service provides free access to Jupyter notebooks with GPU support for prototyping ML models?

A.Colab

B.Kaggle Notebooks

C.Vertex AI Workbench

D.BigQuery Studio

AnswerA

Colab offers free notebooks with GPU support.

Why this answer

Google Colab is a free notebook service with GPU support, commonly used for prototyping.

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682

MCQeasy

A company wants to generate images for slide decks using Gemini in Google Slides. Which Gemini feature in Google Slides should they use?

A.Speaker notes generation

B.Slide layout optimization

C.Image generation from text prompts

D.Summation of slide content

AnswerC

Gemini in Slides can generate images based on text prompts, ideal for creating visuals.

Why this answer

Gemini in Slides can generate images from text prompts, allowing users to create visuals directly within the presentation.

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683

MCQhard

An e-commerce company uses a generative AI model to generate product descriptions. They observe that descriptions for high-end products use more sophisticated language compared to budget products, potentially reinforcing class stereotypes. What is the most likely cause, and what should they do to mitigate it?

A.The training data reflects real-world associations; fine-tune with a balanced dataset that includes diverse product descriptions across price ranges

B.The safety filters are too aggressive; relax them

C.The temperature parameter is set too low; increase it to introduce more randomness

D.The model architecture is biased; switch to a different base model

AnswerA

Fine-tuning with balanced data reduces stereotype reinforcement.

Why this answer

The bias stems from training data correlations. Fine-tuning on balanced, diverse data can reduce stereotypical associations. The other options either do not address the root cause or are less effective.

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Google Cloud Generative AI Leader Generative AI Leader (Generative AI Leader) — Questions 676–683