Free Generative-AI-Leader Practice Questions – 2026 Updated

The solution described is a comprehensive system that perceives a student's state (knowledge), makes decisions (recommends materials), and takes actions (generates exercises) to achieve a specific goal (personalized learning). This autonomous, goal-oriented behavior is best encapsulated by the term "AI agent." A customized learning agent would integrate various AI components, such as a recommendation engine and a generative model (like an LLM), into a cohesive system that manages the entire personalized learning journey for a student. It is the most holistic description of the required AI solution.

A. An AI-powered recommendation system is only one feature of the described platform; it does not assess knowledge or generate new exercises.

B. A large language model is a powerful component for generating content but is not the entire solution; it lacks the overarching structure for progress tracking and lesson management.

C. A learning management system (LMS) is a platform for delivering educational content but typically lacks the dynamic, AI-driven personalization and content generation capabilities described.

1. Russell

S. J.

& Norvig

P. (2021). Artificial Intelligence: A Modern Approach (4th ed.). Pearson. In Chapter 2

"Intelligent Agents

" an agent is defined as a system that perceives its environment and acts to achieve goals. A learning agent

specifically

can improve its performance over time

which directly aligns with the described educational solution.

2. Google Cloud. (2024). Agents. Vertex AI Search and Conversation documentation. Retrieved from https://cloud.google.com/dialogflow/cx/docs/concept/agent. This documentation defines an agent as a module that "manages end-user conversations

" handling tasks and answering questions. The educational solution acts as a specialized agent managing the "conversation" of learning with a student.

According to Google Cloud's recommended practices, the most critical first step in selecting a generative AI use case is to clearly define the business problem you are trying to solve and the desired outcomes. This problem-first approach ensures that the technology is applied to create tangible business value and that the project is aligned with strategic objectives. Identifying a specific, high-impact business need provides the necessary foundation and justification for the project, guiding all subsequent technical and implementation decisions. Without a clear business driver, a gen AI project risks becoming a "solution in search of a problem," leading to wasted resources and minimal impact.

A. The availability of pre-trained models is a technical feasibility assessment that should occur after a specific business problem has been identified.

B. The frequency of model updates is a detailed operational consideration, not the primary strategic factor for initially choosing a use case.

D. The number of employees to be trained is part of the implementation and change management plan, which is developed after the use case is selected.

1. Google Cloud AI Adoption Framework: The framework's first phase

"Identify

" emphasizes starting with business objectives. "The first step is to identify potential use cases by understanding your business objectives and challenges to pinpoint areas where generative AI can deliver the most value." This directly supports prioritizing business problems first. (Source: Google Cloud

"The AI Adoption Framework: A guide to scaling AI successfully

" Page 5

"Phase 1: Identify").

2. Google Cloud Skills Boost Courseware: In the "Introduction to Generative AI" course

the module on "Generative AI Use Cases" consistently frames the adoption process around solving specific business challenges

such as improving customer service or accelerating content creation. The principle is to match the technology's capability to a pre-defined business need. (Source: Google Cloud Skills Boost

"Introduction to Generative AI

" Module: "Generative AI Use Cases").

3. Google Cloud for Leaders Guide: "To get started with generative AI

leaders should first identify the key business challenges and opportunities where this technology can have the most significant impact." This guidance reinforces that the starting point is always the business context

not the technology itself. (Source: Google Cloud

"A C-suite guide to generative AI

" Section: "How to get started with generative AI").

The tasks described—summarizing security case information, researching threats, and creating detection rules—are core functions of cybersecurity operations. A "Security agent" is a specialized generative AI tool designed specifically for these security-centric use cases. It integrates with security platforms like Security Information and Event Management (SIEM) and Security Orchestration, Automation, and Response (SOAR) to automate and augment the work of security analysts, directly addressing the CISO's requirements.

B. Data agent: This is too general. It focuses on broad data analysis and querying across various domains, not the specialized field of threat intelligence and response.

C. Code agent: This is specialized for software development tasks like writing, debugging, and explaining code, not for security-specific rule creation or threat research.

D. Customer service agent: This is designed for customer interactions and support, which is entirely unrelated to the internal security operations described in the scenario.

1. Google Cloud. (n.d.). Chronicle Security Operations. Retrieved from https://cloud.google.com/chronicle. The documentation for Chronicle CyberShield

a core component of the platform

details its generative AI capabilities: "Chronicle’s Gen AI capabilities can help you with natural language search

case summarization

and remediation recommendations

" which are the exact functions of a security agent as described in the question.

2. Google Cloud Blog. (2023

August 29). Introducing the Google Cloud Security AI Workbench. Retrieved from https://cloud.google.com/blog/products/security/introducing-google-cloud-security-ai-workbench. This official announcement describes the workbench's purpose

which embodies a security agent: "Security AI Workbench uses a specialized

security-trained large language model (LLM)

Sec-PaLM 2... to help with threat research

summarizing case information

and recommending actions."

3. Google Cloud. (n.d.). Security AI. Retrieved from https://cloud.google.com/security/ai. This page outlines Google's vision for AI in security

stating it helps "automatically generate detections... summarize event data... and recommend responses." These are the defining characteristics of a security-focused AI agent.

Vertex AI Agent Builder is the optimal solution as it is specifically designed for creating generative AI applications, such as conversational agents and search engines, over private data with minimal coding. The pharmaceutical company can ingest its scientific papers into a data store connected to Agent Builder. This allows researchers to ask complex questions in natural language, receive summarized insights, and get answers grounded in the provided documents. This directly fulfills the requirements for a low-code solution that can query and summarize a specific corpus of documents to accelerate research.

A. Use Gemini for Google Workspace to facilitate collaborative document review.

This is a personal productivity assistant within Google's apps (Docs, Gmail). It is not designed to build a dedicated, queryable knowledge base over a large, private corpus of scientific papers.

B. Use Vertex AI Search to index the papers and enable keyword-based searches.

This is too limited. While Vertex AI Search (a component of Agent Builder) can index documents, the need is for advanced Q&A and summarization, not just keyword-based retrieval.

C. Use Vertex AI AutoML to train a model that classifies papers into predefined research areas.

This only addresses document classification. It does not fulfill the primary requirements of answering questions about the content or providing summarized insights from the papers.

1. Google Cloud Documentation

"Vertex AI Agent Builder overview." "Vertex AI Agent Builder lets developers

even those with limited machine learning (ML) expertise

tap into Google's foundation models

search expertise

and conversational AI technologies to create enterprise-grade generative AI applications." This aligns with the low-code requirement and the goal of creating a custom AI application.

2. Google Cloud Documentation

"Introduction to Vertex AI Search." "You can build a search engine on your website data or on your unstructured data... Your users can get summary responses generated from the content of your data store and the documents that the summary was generated from." This directly supports the need for summarization and Q&A over the company's papers. Vertex AI Search is a core part of the Agent Builder platform.

3. Google Cloud Documentation

"Introduction to Vertex AI." Under the "Build and use generative AI" section

it describes Agent Builder for creating "search and conversational AI experiences." This positions it as the correct tool for the described use case

as opposed to AutoML

which is listed under "Train and tune models."

Vertex AI Search is the most suitable solution as it is specifically designed to allow organizations to create powerful, customized search engines and conversational AI applications using their own data. It can ingest and index diverse data types, including product catalogs and support documents, which directly addresses the e-commerce company's need. By leveraging Google's advanced search technology and generative AI models, it can provide highly relevant results and conversational summaries, significantly improving the user experience, increasing customer satisfaction, and reducing the burden on support teams.

B. Vertex AI Platform: This is a broad MLOps platform for building, training, and deploying custom machine learning models, not a ready-to-use enterprise search solution.

C. Google Shopping: This is a consumer-facing service that allows users to search for products across different online retailers, not a tool for a company to implement on its own website.

D. Google Search: This is the public web search engine. It cannot be configured to index and provide search capabilities for a company's private, internal data stores.

1. Google Cloud Documentation

"What is Vertex AI Search." This document states

"Vertex AI Search lets developers...tap into the power of Google's foundation models

search expertise

and conversational AI technologies to create enterprise-grade generative AI applications." It explicitly mentions creating search engines for websites and unstructured data. (Source: Google Cloud

Vertex AI Search Documentation

"Overview").

2. Google Cloud Solutions

"Retail Search." This page

part of the Vertex AI Search for retail documentation

highlights its purpose: "Retail Search...delivers Google-quality search on your website and mobile applications. Retail Search is built upon Google's deep understanding of user intent and context..." This directly supports its application in an e-commerce scenario. (Source: Google Cloud

Vertex AI Search for retail Documentation

"Retail Search").

3. Google Cloud Documentation

"Vertex AI overview." This source clarifies the purpose of the Vertex AI Platform as a unified environment for the entire ML workflow: "Vertex AI brings together the Google Cloud services for building ML under one

unified UI and API." This distinguishes it from the specific application-focused nature of Vertex AI Search. (Source: Google Cloud

Vertex AI Documentation

"Overview").

Document AI is a purpose-built platform for understanding and extracting structured data from unstructured or semi-structured documents. It combines Optical Character Recognition (OCR) with natural language processing and pre-trained models specifically designed for documents like forms, invoices, and applications. It can accurately identify and extract key-value pairs (e.g., "Applicant Name: John Doe") and tabular data, even when the layout varies across documents. This directly addresses the financial company's need to automate data extraction from loan applications to improve speed and accuracy.

A. Natural Language API: This service analyzes text that has already been extracted. It does not perform OCR or process the structure of a scanned document or PDF.

B. Dataflow: This is a data processing service for building ETL (Extract, Transform, Load) pipelines. It does not have native capabilities for document scanning or data extraction.

C. Vision AI: While Vision AI provides powerful OCR to extract raw text from images, it lacks the specialized, built-in understanding of document structure needed to reliably extract key-value pairs from forms.

1. Google Cloud Documentation

"Document AI overview": "Document AI is a document understanding platform that takes unstructured data

such as documents...and makes the data easier to understand

analyze

and consume... Use cases include...loan processing." This directly aligns with the scenario. (Source: https://cloud.google.com/document-ai/docs/overview

Section: "What is Document AI?")

2. Google Cloud Blog

"Choosing an OCR option: Cloud Vision API vs. Cloud Document AI": "While Vision AI is a great choice for detecting text in images

Document AI is a better choice for extracting data from documents... Document AI is built for document processing and can extract structured data from documents

such as key-value pairs and tables." This reference explicitly differentiates the correct answer (D) from option C. (Source: https://cloud.google.com/blog/products/ai-machine-learning/choosing-an-ocr-option-cloud-vision-api-vs-cloud-document-ai

Paragraphs 2 and 3).

3. Google Cloud Documentation

"Cloud Natural Language overview": "You can use it to extract information about people

places

events...and analyze sentiment and syntax." This confirms the API works on pre-existing text

making it unsuitable for the initial extraction from a scanned document. (Source: https://cloud.google.com/natural-language/docs/overview

Section: "Cloud Natural Language features").

4. Google Cloud Documentation

"Dataflow overview": "Dataflow is a managed service for executing a wide variety of data processing patterns." This positions Dataflow as a tool for data transformation and movement

not for the initial data capture from a document image. (Source: https://cloud.google.com/dataflow/docs/concepts/overview

Section: "What is Dataflow?").

The term "Agentspace" conceptually refers to an ecosystem of AI agents designed for enterprise use. The primary business advantage of deploying such agents, built on platforms like Google's Vertex AI Agent Builder, is to enhance employee productivity. These AI assistants achieve this by streamlining how employees interact with complex enterprise data. They leverage advanced document analysis and natural language understanding to provide quick, accurate answers from internal knowledge bases and can automate repetitive tasks, freeing up employees to focus on higher-value work. This directly translates to improved operational efficiency and productivity.

A. Enhanced real-time communication and collaboration are primary benefits of tools like Google Workspace (Chat, Meet), not the core function of AI agents for enterprise search and automation.

B. Interoperability with legacy systems is a crucial technical capability that enables the AI agent to function, but it is not the ultimate business advantage itself.

D. Granular access control is a critical security and governance feature, but it is a supporting requirement rather than the primary business driver for adopting AI agents.

1. Google Cloud Blog (May 14

2024). In the announcement for Vertex AI Agent Builder

it is stated that "Agent Builder helps developers easily build and deploy enterprise-ready generative AI experiences... These agents can help employees and customers get the answers they need and take action

improving customer and employee satisfaction and productivity." This directly links the use of AI agents to improved productivity and data interaction.

Source: Google Cloud Blog

"Introducing Vertex AI Agent Builder: build and deploy enterprise-ready generative AI experiences".

2. Google Cloud Next '24 Keynote. Google CEO Sundar Pichai and Google Cloud CEO Thomas Kurian detailed the vision for AI agents across the enterprise. Kurian stated

"We are introducing a new set of agents for common business functions... to help employees with a variety of tasks

from creative and development to security operations." The entire presentation framed the value of these agents in terms of transforming business processes and radically improving productivity.

Source: Google Cloud Next '24 Opening Keynote

"The New Way to Cloud

" April 9

2024.

3. Vertex AI Agent Builder Documentation. The official documentation describes the service as a tool to "build and deploy enterprise-grade generative AI agents that have access to your enterprise data." The use cases

such as enterprise search and conversational agents

are fundamentally aimed at making information more accessible and actions more automated

which are direct drivers of productivity.

Source: Google Cloud

Vertex AI Agent Builder Documentation

"Overview of Vertex AI Agent Builder".

To ensure fairness and build trust in a high-stakes process like job application screening, transparency is paramount. Candidates need to trust that the process is equitable, and the HR team must be able to verify that the AI is not introducing or amplifying bias. By prioritizing transparency in how the AI evaluates applications and uses candidate data, the team addresses both goals directly. This approach aligns with core principles of responsible AI, such as accountability and fairness, by making the system's logic understandable and auditable.

A. Integrating with job boards is an operational goal focused on increasing the applicant pool, not on ensuring the fair and trustworthy evaluation of those applicants.

B. Minimizing processing time is an efficiency metric. Prioritizing speed over careful, transparent, and fair evaluation can undermine trust and lead to poor, biased outcomes.

D. Removing human review (human-in-the-loop) is contrary to responsible AI practices for critical decisions. It increases the risk of unchecked bias and errors, thereby eroding trust and fairness.

1. Google AI Principles: The official Google AI principles state

"We will design AI systems to be appropriately cautious and to seek human direction as appropriate." and "AI algorithms and datasets can reflect

reinforce

or reduce unfair biases... We will seek to avoid unjust impacts on people

particularly those related to sensitive characteristics..." Transparency is a key mechanism for ensuring these principles are met. (Source: Google AI

"Our Principles"

Section: "AI Principles"

Points 2 and 5).

2. Google Cloud

"AI adoption framework: Part 3—Governing AI": This official documentation emphasizes the need for AI governance

which includes "transparency and explainability." It states

"For AI systems to be trustworthy

it's important that stakeholders understand their output. Explainable AI (XAI) can help you understand and interpret predictions from your models." This directly supports the need for transparency in evaluation. (Source: Google Cloud Architecture Center

Document ID: ai-adoption-framework-governance

Section: "Establish AI principles").

3. Stanford University

Human-Centered AI (HAI)

"An AI Bill of Rights for A.I. that Serves the American People": The report highlights that individuals should "know that an automated system is being used and understand how and why it contributes to outcomes that impact you." This principle of "Notice and Explanation" is fundamental to building trust in AI systems used for critical decisions like hiring. (Source: Stanford HAI

"The Blueprint for an AI Bill of Rights

" Section: "Notice and Explanation").

Google Cloud addresses concerns about vendor lock-in by fostering an open and flexible AI ecosystem. This is primarily demonstrated through Vertex AI, which provides access to a wide variety of models, including Google's own foundation models (e.g., Gemini), third-party models, and numerous open-source models via the Model Garden. This approach allows customers to choose the best model for their specific use case without being confined to a single vendor's proprietary technology. Furthermore, support for popular open-source frameworks like TensorFlow and PyTorch ensures that customers can leverage existing skills and tools, enhancing portability and flexibility in their technology stack.

B. While some solutions are easy to deploy, eliminating customization is contrary to the flexibility needed to avoid vendor lock-in. Google's platform emphasizes extensive customization.

C. "Strict adherence to proprietary technologies" is the definition of vendor lock-in, which is the exact concern the company wants to mitigate. Google Cloud's strategy is the opposite.

D. This statement discusses a potential socioeconomic impact of AI and is irrelevant to the technical concern of vendor lock-in and technology stack flexibility.

1. Google Cloud

Vertex AI Model Garden Documentation: "Model Garden in Vertex AI is a platform that helps you discover

test

customize

and deploy Google's and select open-source (OSS) foundation models and enterprise-ready APIs... This gives you a choice of models to meet your needs."

Source: Google Cloud Documentation

"Model Garden overview".

2. Google Cloud

Vertex AI Product Page: "Vertex AI is an open platform

so you can use any ML framework or tool you prefer. It has extensive support for popular open-source frameworks such as TensorFlow

PyTorch

and scikit-learn..."

Source: Google Cloud

"Vertex AI: A unified AI/ML platform".

3. Google Cloud Blog

"Build with generative AI models on Vertex AI

your way" (July 18

2023): "We believe that providing customers with choice and flexibility is critical... Today

we are announcing that Llama 2 from Meta is now available on Google Cloud via Vertex AI’s Model Garden... This announcement is part of our commitment to being the most open cloud provider."

Source: Google Cloud Blog

"Build with generative AI models on Vertex AI

your way".

Generative AI is a category of artificial intelligence algorithms that learn from existing data to create new, original content. Its core function is generation, not just analysis or classification. These models can produce a wide variety of outputs, including text, images, music, and code, that are similar to, but distinct from, the data they were trained on. This creative capability is the defining feature that distinguishes generative AI from other types of AI, such as predictive or analytical AI.

A. This is a broad definition of machine learning or deep learning, which are foundational technologies for generative AI but do not capture its specific purpose of content creation.

C. This describes a neural network, which is a common architectural component of many AI systems, including generative models, but it is not the definition of generative AI itself.

D. This defines linear regression, a specific and relatively simple statistical algorithm used for predictive modeling, not for generating new, complex content.

1. Google Cloud Documentation. "What is generative AI?". Google. "Generative artificial intelligence (generative AI) is a type of artificial intelligence that can create new content and ideas

including conversations

stories

images

videos

and music." Retrieved from https://cloud.google.com/what-is-generative-ai.

2. Stanford University Human-Centered AI (HAI). "What Is Generative AI?". Stanford University. "Generative AI refers to deep-learning models that can generate high-quality text

images

and other content based on the data they were trained on." Retrieved from https://hai.stanford.edu/news/what-generative-ai.

3. Saleh

B.

& Potthast

M. (2024). A Survey of Generative AI. ACM Computing Surveys. "Generative AI is a subfield of artificial intelligence that focuses on creating new data that resembles a given training dataset. This can include generating images

text

music

or other forms of media." (This is a representative definition from academic literature). A similar conceptual definition can be found in many survey papers.