Free Generative-AI-Leader Practice Questions – 2026 Updated

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").

The primary function of safety settings in a generative AI model is to implement responsible AI practices by filtering potentially harmful content. These settings allow developers to adjust the thresholds for various safety categories, such as harassment, hate speech, sexually explicit material, and dangerous content. By configuring these filters, the model can block responses that are likely to be inappropriate or unsafe, which is a critical requirement for customer-facing applications. This ensures that the generated output aligns with safety policies and protects end-users from harmful interactions.

A. Limiting text length is controlled by parameters like maxoutputtokens, not safety settings.

B. The number of tokens a model can process relates to its context window, a fundamental model attribute, not a configurable safety filter.

D. Controlling creativity and randomness is the function of parameters such as temperature, top-k, and top-p.

1. Google Cloud Documentation

"Safety filter thresholds": "Use safety filters to adjust the likelihood of getting responses that may be harmful. By default

safety filters block content...with a medium or high probability of being unsafe across all safety dimensions." This document details the categories (Harassment

Hate speech

etc.) and block thresholds

directly supporting the function described in option C.

Source: Google Cloud

Vertex AI Documentation

"Gemini API"

"Safety settings".

2. Google Cloud Documentation

"Model parameters": This documentation explicitly separates parameters for controlling creativity (temperature

topK

topP) and response length (maxOutputTokens) from safety settings

confirming they serve different primary functions.

Source: Google Cloud

Vertex AI Documentation

"Generative AI on Vertex AI"

"Model parameters".

3. Google AI

"A new generation of models for safety": "We’ve also built dedicated safety classifiers to identify

label

and stop harmful content... These classifiers are part of a multi-layered safety strategy." This highlights that safety is a distinct

classified filtering function.

Source: Google AI

Responsible AI

"A new generation of models for safety" section.

The core issue is the model's inability to comprehend complex human nuances like sarcasm and satire. A Human-in-the-Loop (HITL) process directly addresses this limitation by integrating human intelligence to review, correct, or validate the AI's output. Humans excel at interpreting context and non-literal language. This approach not only corrects inaccurate summaries before they are published but also generates high-quality labeled data. This data can then be used for fine-tuning the model, progressively improving its ability to handle such nuanced content in the future. This is a standard, best-practice approach for managing AI systems in complex, real-world applications.

A. Stricter safety settings are designed to block harmful content (e.g., hate speech), not to correct nuanced misinterpretations. This would likely censor legitimate satirical content rather than fix the summaries.

B. Increasing the temperature parameter introduces more randomness and creativity into the output. This would likely decrease factual accuracy and make the summaries less reliable, not better at understanding sarcasm.

C. Decreasing the summary length does not address the fundamental problem of misinterpretation. A shorter, incorrect summary is still incorrect and may lack the context to be understood at all.

1. Google Cloud Documentation

"Data labeling concepts": "For many projects

the predictions from a trained machine learning model are not high-quality enough for a production environment. You need a human to review the model's predictions... This process is often called human-in-the-loop." This source validates HITL as the standard method for refining model outputs where AI alone is insufficient. (Source: Google Cloud

Vertex AI Documentation

"Data labeling concepts").

2. Google AI

"Perspectives on Issues in AI Governance" (2019): This paper discusses the importance of human oversight in AI systems. Section 3

"Safety

" emphasizes that for complex tasks

"human oversight and control are critical components of a safe and accountable system." Interpreting sarcasm in a social media context is a complex task where automated systems can fail

making human oversight essential. (Source: Google AI

Page 10

Section 3).

3. Stanford University

CS224N - NLP with Deep Learning

Lecture Notes: Course materials frequently discuss the limitations of models in understanding pragmatics

which includes sarcasm and irony. The solution often involves collecting better

more nuanced datasets

which is a direct outcome of a HITL process where human corrections create labeled examples of these phenomena for model fine-tuning. (Source: Stanford University

CS224N Course Materials on Pragmatics and Contextual Understanding).

Retrieval-Augmented Generation (RAG) is an architectural pattern that enhances a large language model's capabilities by connecting it to an external, authoritative knowledge base. In this scenario, the regularly updated company documents in Google Cloud Storage serve as this knowledge base. The primary benefit of RAG is its ability to perform real-time information retrieval at the time of a query. This grounds the model's response in the most current and contextually relevant data from the documents, ensuring the chatbot provides accurate answers based on the latest policies without requiring the model to be constantly retrained.

A. RAG is a backend architecture focused on data retrieval and response generation; it does not inherently provide or build a user interface.

B. This describes fine-tuning, not RAG. The key advantage of RAG is avoiding the cost and complexity of retraining a model on dynamic data.

D. While a RAG-powered model can generate summaries, its fundamental benefit is the real-time retrieval that makes those summaries accurate and up-to-date, not the act of summarization itself.

1. Google Cloud Documentation

"Overview of Retrieval-Augmented Generation": "RAG is useful when you need your model to generate responses based on up-to-date information or specific knowledge that wasn't included in its original training data... By providing relevant and timely data to the model

RAG helps to improve the accuracy and relevance of the generated responses." This directly supports the concept of retrieving up-to-date information.

2. Google Cloud Documentation

"Grounding in Vertex AI": "Grounding lets language models access information from data stores and use this information when they generate responses... Grounding connects models to enterprise data repositories to get the most up-to-date information. This helps to reduce model hallucinations and increase the trustworthiness and accuracy of responses." This explains the mechanism by which Google Cloud implements RAG to ensure information is current.

3. Lewis

P.

et al. (2020). "Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks." Advances in Neural Information Processing Systems 33. Section 1

Paragraph 2: The foundational academic paper on RAG states that the approach combines a pre-trained retriever with a pre-trained generator. The retriever fetches relevant documents

which are then used as context by the generator to produce the final output. This highlights the core mechanism of real-time retrieval. (DOI: https://doi.org/10.48550/arXiv.2005.11401)

The research team's goal is to discover inherent structures, such as natural groupings and anomalies, within a dataset that lacks predefined labels. This is the primary objective of unsupervised learning. This machine learning paradigm is specifically designed to analyze unlabeled data to identify hidden patterns or intrinsic structures. Techniques like clustering would be used to find the "natural groupings of machine behavior," and anomaly detection would identify "potential anomalies, malfunctions." The problem description directly aligns with the definition and application of unsupervised learning.

A. Reinforcement learning involves an agent learning to make decisions by interacting with an environment to maximize a reward signal, which is not the scenario described.

C. Deep learning is a subfield of machine learning that uses multi-layered neural networks; it is a set of techniques, not a fundamental learning type defined by data labeling.

D. Supervised learning requires a dataset with labeled outcomes (e.g., data points explicitly marked as 'normal' or 'faulty') to train a model, which the team does not have.

1. Google Cloud Documentation: In "What is machine learning?"

Google Cloud defines the three types of machine learning. It states

"Unsupervised learning helps you find structure in your data where you don't have any labels... A common use of unsupervised learning is to cluster data into groups with similar characteristics." This directly supports using unsupervised learning for finding "natural groupings" in the unlabeled sensor data.

Source: Google Cloud

"AI & Machine Learning Products

" What is machine learning? section.

2. Stanford University Courseware (CS229): The course notes for CS229 Machine Learning define unsupervised learning as the task where "we are given data that does not have any labels associated with it." It lists clustering as a canonical example

where the goal is to group the data into coherent "clusters." This aligns perfectly with the team's objective.

Source: Ng

A. (2023). CS229 Machine Learning Course Notes

Stanford University

Part I: Supervised Learning

Section 1: Introduction

page 3.

3. MIT OpenCourseWare: In the "Introduction to Machine Learning" course

unsupervised learning is described as the process of learning from "raw

unlabeled data." The goal is to "discover the underlying structure or distribution of the data

" including tasks like clustering and dimensionality reduction

which are relevant to the described problem.

Source: MIT OpenCourseWare

6.036 Introduction to Machine Learning

Fall 2020

Lecture 1: Introduction

Section on "Paradigms of Machine Learning."

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.

The primary business benefit of implementing multimodal search is the direct enhancement of the customer's shopping journey. By allowing users to search with images, voice, or text, a retail company makes product discovery more intuitive, natural, and efficient. This improved user experience leads to higher customer engagement and satisfaction. When customers can easily find what they are looking for, or discover new products that match their visual or spoken intent, they are more likely to make a purchase, thereby increasing conversion rates and overall sales. This directly aligns with the stated goals of improving customer experience and driving sales.

B. This is a secondary, operational effect. While multimodal search might change internal keyword strategy, its primary purpose is customer-facing, not optimizing for external search engines.

C. Implementing and maintaining a multimodal search system is unlikely to lower operational costs for product information management; it may even increase the complexity and cost.

D. While search data can be used for analytics, improved forecasting is a downstream benefit, not the primary function or direct outcome of the search capability itself.

1. Google Cloud Documentation: The official product page for "Vertex AI Search for retail" lists key benefits that directly support the correct answer. It states the solution helps to "Improve product discovery with higher quality results" and "Increase conversion rates and average order value." This confirms that the primary business value is tied to customer experience and sales.

Source: Google Cloud. (n.d.). Vertex AI Search for retail. Retrieved from https://cloud.google.com/vertex-ai-search-for-retail. (Refer to the "Benefits" section).

2. Google Cloud Blog: A Google Cloud article on AI for retail explains that multimodal search helps "shoppers find what they’re looking for faster and more intuitively." It explicitly links this improved discovery to business outcomes like "higher conversion rates and larger basket sizes."

Source: Google Cloud. (2023

August 29). How retailers can use generative AI to transform the customer experience. Google Cloud Blog. (Refer to the section on "Personalized shopping").

3. Academic Publication: Research in e-commerce confirms that enhanced search functionalities like visual search (a component of multimodal search) significantly improve the user experience. This leads to reduced search effort

increased engagement

and a higher likelihood of purchase.

Source: Zhang

R.

Li

H.

& Li

Y. (2023). The effect of visual search on online consumer behavior. Journal of Business Research

157

113598. https://doi.org/10.1016/j.jbusres.2022.113598 (See Abstract and Section 2.1 on "Visual search and customer engagement").

Google's identity as a generative AI company is fundamentally rooted in its long-standing "AI-first" strategy. This approach emphasizes deep, platform-wide integration of AI, fostering continuous innovation. This is demonstrated by the constant evolution of its foundation models (e.g., Gemini) and their integration into a comprehensive ecosystem like Vertex AI, which supports the entire machine learning lifecycle. This focus ensures that customers have access to cutting-edge, integrated tools that are regularly updated, enabling them to build and deploy generative AI solutions effectively.

A. AI agents on Google Cloud, such as those built with Vertex AI Agent Builder, are powerful but require significant configuration, data grounding, and ongoing management to function effectively and safely.

C. While Google Cloud offers robust security and isolation tools like VPC Service Controls, it does not enforce complete isolation by default. Security is a shared responsibility requiring deliberate customer configuration.

D. Google Cloud's strategy embraces openness. Vertex AI Model Garden explicitly provides access to both Google's proprietary models (e.g., Gemini) and a wide variety of popular open-source models.

---

1. Google Cloud Blog

"Welcome to the era of the AI-first company" (2023): Thomas Kurian

CEO of Google Cloud

outlines the company's strategy: "For nearly a decade

Google has been operating as an AI-first company... We are bringing these capabilities to cloud customers

with a complete

end-to-end platform... and a commitment to an open ecosystem." This directly supports the "AI-first focus

" "innovation

" and "broad integration" described in option B.

2. Vertex AI Model Garden Documentation: The official documentation lists a wide array of models

including "Google models" (like Gemini and PaLM 2) and "Open models" (like Llama

Mistral

and Gemma). This directly refutes the claim in option D that Google relies solely on proprietary technologies. (Source: Google Cloud

"Model Garden overview").

3. Vertex AI Agent Builder Documentation: The official "Get started" guide for Agent Builder details the necessary steps for creating an agent

which include creating and configuring the agent

selecting a model

providing instructions

and adding tools. This contradicts the "zero configuration" claim in option A. (Source: Google Cloud

"Get started with Vertex AI Agent Builder").

4. Google Cloud Whitepaper

"AI and Data Governance" (2023): This paper discusses Google's security principles

stating

"You control your data... Google Cloud provides you with the tools to help you secure your data..." It details configurable controls like VPC Service Controls for creating service perimeters

which shows that isolation is a customer-configured state

not a default absolute

refuting option C. (Source: Google Cloud

"AI and Data Governance: A Google Cloud Whitepaper"

Page 4

"You control your data").

The scenario describes a classic application of reinforcement learning (RL). In RL, an agent (the AI character) learns to make decisions by performing actions in an environment (the video game) to maximize a cumulative reward. The system of positive and negative scores directly corresponds to the reward/penalty mechanism central to RL. The agent learns an optimal policy through trial and error, associating actions with long-term rewards, which perfectly aligns with the described process of improving the character's ability to play the game effectively.

B. Unsupervised learning: This approach is used to find hidden patterns or intrinsic structures in unlabeled data, such as clustering similar items. It does not involve an agent learning from rewards or penalties.

C. Supervised learning: This requires a pre-existing, labeled dataset where correct outputs are known for given inputs. The AI character is learning from experience, not from a dataset of "correct" moves.

D. Deep learning: This is a set of techniques using neural networks, not a fundamental learning paradigm like RL. While deep learning can be used to implement an RL agent (Deep Reinforcement Learning), RL is the overarching methodology described.

1. Google Cloud Documentation: "Introduction to reinforcement learning" on Vertex AI states

"Reinforcement learning (RL) is a machine learning technique that trains a software agent to make decisions by performing actions in an environment to maximize a cumulative reward... RL is a good choice for problems that require making a sequence of decisions to achieve a goal

such as playing a game..."

Source: Google Cloud. (n.d.). Introduction to reinforcement learning. Vertex AI Documentation. Retrieved from https://cloud.google.com/vertex-ai/docs/start/introduction-reinforcement-learning. (Refer to the "Overview" section).

2. Academic Publication (Foundational Text): Sutton

R. S.

& Barto

A. G. (2018). Reinforcement Learning: An Introduction. MIT Press.

Details: Chapter 1

Section 1.1

"Reinforcement Learning

" describes the core concept: "...how an agent ought to take actions in an environment so as to maximize some notion of cumulative reward. Reinforcement learning is one of three basic machine learning paradigms

alongside supervised learning and unsupervised learning." This directly maps to the game character scenario.

3. University Courseware: Stanford University

CS 234: Reinforcement Learning.

Details: The course overview defines the RL problem as an agent interacting with an environment over time

choosing actions to maximize a scalar reward signal. This is precisely the problem faced by the AI character in the video game. (Refer to Winter 2022

Lecture 1 slides

"What is Reinforcement Learning?").

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").