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Quiz Google - Authoritative Generative-AI-Leader - Exam Google Cloud Certified -
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Google Cloud Certified - Generative AI Leader Exam Sample Questions (Q12-Q17):NEW QUESTION # 12
A finance team wants to use Gemma to help with daily tasks so that the financial analysts can focus on other work. Which business problem can Gemma most efficiently address?
- A. The struggle to accurately extract key financial figures and insights from a variety of document formats, such as balance sheets and income statements, for quick reporting.
- B. The complexity of building and deploying sophisticated internal knowledge bases to answer employees' finance-related questions with accurate and up-to-date information.
- C. The challenge of efficiently producing high-quality written summaries and initial drafts of financial communications.
- D. The difficulty in analyzing large datasets of financial transactions and market data to identify anomalies and predict future financial performance.
Answer: C
Explanation:
Gemma is a family of lightweight, open-source Large Language Models (LLMs) from Google that are based on the same research and technology as the Gemini models. As an LLM, its core strength lies in language-based tasks, particularly the generation and summarization of text.
The problem that Gemma, or any pure LLM, can most efficiently address is:
Generating text: creating new content quickly (Option D).
Summarizing text: condensing long communications or documents (Option D).
Option D, producing high-quality written summaries and initial drafts, is a natural language generation task that aligns perfectly with the core function of an LLM like Gemma. It is a key productivity booster for analysts needing to draft reports or emails quickly.
Option B (Analyzing large datasets/predicting performance) requires traditional machine learning (ML) models or analytical tools like BigQuery ML, as LLMs are not specialized for numerical predictive modeling.
Option C (Extracting key financial figures from documents) is a task for a highly specialized tool like Google's Document AI.
Option A (Building internal knowledge bases for Q&A) is a broader use case that is best solved with a platform solution using RAG, such as Vertex AI Search, not just a base model.
(Reference: Google's description of the Gemma model family emphasizes its role as a flexible, open LLM that excels at language fundamentals, making it ideal for content creation, summarization, and other text generation tasks.)
NEW QUESTION # 13
An organization wants to use generative AI to create a marketing campaign. They need to ensure that the AI model generates text that is appropriate for the target audience. What should the organization do?
- A. Adjust the temperature parameter.
- B. Use few-shot prompting.
- C. Use role prompting.
- D. Use prompt chaining.
Answer: C
Explanation:
Role prompting is a technique where you instruct the generative AI model to "act as" a specific persona or character. By assigning the model a role (e.g., "Act as a marketing expert writing for a young, tech-savvy audience"), you can guide its tone, style, and content to be appropriate for the target audience of the marketing campaign.
________________________________________
NEW QUESTION # 14
A security team needs a centralized platform to gain a comprehensive overview of their organization's security health across their entire Google Cloud environment, including potential threats to their generative AI deployments. Which Google Cloud security offering is specifically for this purpose?
- A. Secure-by-design infrastructure
- B. Identity and Access Management
- C. Security Command Center
- D. Workload monitoring tools
Answer: C
Explanation:
Security Command Center is Google Cloud's comprehensive security management and data risk platform. It provides centralized visibility into security posture, identifies vulnerabilities, detects threats, and helps manage compliance across the entire Google Cloud environment, includingservices and deployments like generative AI.
________________________________________
NEW QUESTION # 15
An organization is collecting data to train a generative AI model for customer service. They want to ensure security throughout the ML lifecycle. What is a critical consideration at this stage?
- A. Establishing ethical guidelines for AI model responses to ensure fairness and avoid harm.
- B. Applying the latest software patches to the AI model on a regular basis.
- C. Monitoring the AI model's performance for unexpected outputs and potential errors.
- D. Implementing access controls and protecting sensitive information within the training data.
Answer: D
Explanation:
The stage mentioned is Data Collection/Training Data Preparation. In the machine learning lifecycle, this initial stage is where raw data is ingested and processed. If the model is being trained for customer service, the data (e.g., customer transcripts) is highly likely to contain sensitive information (like Personally Identifiable Information or PII).
Therefore, the most critical security and privacy consideration at this stage is protecting the integrity and confidentiality of the data itself.
Implementing strong access controls and protecting sensitive information (A) is the essential first step in a secure AI pipeline, aligning with Google's Secure AI Framework (SAIF). If data access is not controlled and sensitive data is not de-identified or redacted before it is used for training, the resulting model could leak that sensitive information to users.
Options B, C, and D are all important controls, but they occur at later stages of the ML lifecycle:
B (Software patches/latest versions) is part of deployment and management.
C (Ethical guidelines/fairness) is a Responsible AI goal implemented via guardrails and testing (later stages).
D (Monitoring) is an MLOps step that happens after deployment.
The critical consideration at the data collection stage is ensuring the data's security and privacy before it influences the model.
(Reference: Google Cloud guidance on securing generative AI emphasizes that one of the most significant risks is data leakage, making safeguarding training data and implementing identity and access control the foundational steps in the data ingestion and preparation phases.)
NEW QUESTION # 16
An organization wants to quickly experiment with different Gemini models and parameters for content creation without a complex setup. What service should the organization use for this initial exploration?
- A. Google AI Studio
- B. Gemini for Google Workspace
- C. Vertex AI Studio
- D. Vertex AI Prediction
Answer: C
Explanation:
The requirement is for a tool that facilitates quick experimentation with Gemini models and parameters without requiring significant technical setup, specifically targeting content creation (prompting/tuning) within the enterprise environment.
Vertex AI Studio (C) is the low-code, web-based UI component of Google Cloud's unified ML platform (Vertex AI). It is explicitly designed for non-technical users, developers, and data scientists to:
Quickly prototype and test different Foundation Models (including Gemini, Imagen, and Codey).
Experiment with model parameters (like Temperature, Top-P, and Max Output Tokens) through a user-friendly interface.
Refine prompts and set up initial tuning or grounding configurations before moving to large-scale production deployment.
Google AI Studio (A) is a very similar tool, but it's generally associated with non-enterprise/public prototyping for Google's models, whereas Vertex AI Studio is the enterprise-ready environment for Gen AI development on Google Cloud, which is the context of the exam.
Vertex AI Prediction (B) is the service for deploying and serving models for inference, not for initial experimentation.
Gemini for Google Workspace (D) is an application that uses Gen AI to boost productivity within apps like Docs and Gmail, but it does not provide the interface needed to experiment with models and tune parameters.
(Reference: Google Cloud documentation positions Vertex AI Studio as the low-code/no-code interface for rapidly prototyping, testing, and customizing Google's Foundation Models (like Gemini) before full production deployment.)
NEW QUESTION # 17
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