This is the free Google Generative AI Leader practice question bank —
40 of 62 total questions, each with a full explanation, free to
read with no signup required. Updated 2026-04-24.
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Question 1
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. Applying the latest software patches to the AI model on a regular basis.
B. Monitoring the AI model’s performance for unexpected outputs and potential errors.
C. Establishing ethical guidelines for AI model responses to ensure fairness and avoid harm.
D. Implementing access controls and protecting sensitive information within the training data.
Show Answer
Correct Answer: D
Explanation: At the data collection and training stage, the primary security risk is exposure or misuse of the training data. Implementing strong access controls and protecting sensitive information (such as PII) ensures confidentiality, compliance, and reduces the risk of data leakage or poisoning. The other options relate to later lifecycle stages (model maintenance, monitoring, or ethics) rather than data security during collection.
Question 1
A company collects customer feedback through open-ended survey questions where customers can write detailed responses in their own words, such as “The product was easy to use, and the customer support was excellent, but the delivery took longer than expected.” What type of data is this?
A. Quantitative data
B. Structured data
C. Labeled data
D. Unstructured data
Show Answer
Correct Answer: D
Explanation: Open-ended survey responses are free-form natural language text without a predefined schema or fixed fields. Because the content, length, and structure vary from one response to another, this type of information is classified as unstructured data rather than quantitative, structured, or labeled data.
Question 2
An order fulfillment team has an agent that automatically processes orders, updates inventory, sends shipping notifications, and handles returns. What type of agent is this?
A. A conversational agent
B. A customer service agent
C. An employee productivity agent
D. A workflow agent
Show Answer
Correct Answer: D
Explanation: The described agent automates an end-to-end operational process: processing orders, updating inventory, triggering shipping notifications, and handling returns across systems. This is characteristic of a workflow (or business process) agent, which coordinates multi-step, multi-system tasks rather than focusing on conversation, general customer support, or individual productivity.
Question 2
A company is developing a generative AI application to analyze customer feedback collected through online surveys. Stakeholders are concerned about potential privacy risks associated with this data, as the feedback contains personally identifiable information. They need to mitigate these risks before using the data to train the AI model. What action should the company prioritize?
A. Focusing on collecting only quantitative feedback data in future surveys.
B. Implementing strong access controls to limit which teams can view the raw survey data.
C. Ensuring that the AI model is trained on a large and diverse dataset.
D. Applying data anonymization techniques to remove or obscure sensitive data
Show Answer
Correct Answer: D
Explanation: The primary privacy risk comes from personally identifiable information in the survey data. Applying data anonymization (e.g., removing, masking, or aggregating identifiers) directly mitigates this risk before model training. Other options do not sufficiently address PII exposure at the data level.
Question 3
A company trains a generative AI model designed to classify customer feedback as positive, negative, or neutral. However, the training dataset disproportionately includes feedback from a specific demographic and uses outdated language norms that don’t reflect current customer communication styles. When the model is deployed, it shows a strong bias in its sentiment analysis for new customer feedback, misclassifying reviews from underrepresented demographics and struggling to understand current slang or phrasing. What type of model limitation is this?
A. Edge case
B. Data dependency
C. Hallucination
D. Overfitting
Show Answer
Correct Answer: B
Explanation: The limitation arises from the model’s reliance on its training data. A skewed dataset causes demographic bias, and outdated language in the data leads to poor performance on modern slang and phrasing. These issues reflect data dependency, not edge cases, hallucination, or overfitting.
Question 3
A company is using a language model to solve complex customer service inquiries. For a particular issue, the prompt includes the following instructions:
“To address this customer’s problem, we should first identify the core issue they are experiencing. Then we need to check if there are any known solutions or workarounds in our knowledge base. If a solution exists, we should clearly explain it to the customer. If not, we might need to escalate the issue to a specialist. Following these steps will help us provide a comprehensive and helpful response. Now, given the customer’s message: ‘My order hasn’t arrived, and the tracking number shows no updates for a week,’ what should be the next step in resolving this?”
What type of prompting is this?
A. Few-shot
B. Chain-of-thought
C. Role-based
D. Zero-shot
Show Answer
Correct Answer: B
Explanation: The prompt explicitly guides the model through a sequence of intermediate reasoning steps—identifying the issue, checking known solutions, deciding whether to explain or escalate, and then applying that process to the customer’s message. This step-by-step structured reasoning is characteristic of chain-of-thought prompting, not few-shot, role-based, or zero-shot prompting.
Question 4
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 difficulty in analyzing large datasets of financial transactions and market data to identify anomalies and predict future financial performance.
D. The challenge of efficiently producing high-quality written summaries and initial drafts of financial communications.
Show Answer
Correct Answer: D
Explanation: Gemma is a lightweight generative language model whose main strength is natural language generation and summarization. It can efficiently draft written summaries, reports, emails, and other financial communications, freeing analysts from routine writing tasks. The other options involve heavier data extraction pipelines, complex knowledge-base systems, or advanced quantitative analysis, which are less well-suited to Gemma’s core capabilities.
Question 4
A company is developing a generative AI-powered customer support chatbot. They want to ensure the chatbot can answer a wide range of customer questions accurately, even those related to recently updated product information not present in the model’s original training data. What is a key benefit of implementing retrieval-augmented generation (RAG) in this chatbot?
A. RAG will significantly reduce the computational resources required to run the generative AI model.
B. RAG will enable the chatbot to fine-tune its underlying language model on the fly based on customer interactions.
C. RAG will enable the chatbot to access and utilize external, up-to-date knowledge sources to provide more accurate and relevant answers.
D. RAG will primarily help the chatbot generate more creative and engaging conversational responses.
Show Answer
Correct Answer: C
Explanation: Retrieval-augmented generation allows the chatbot to retrieve relevant information from external, up-to-date knowledge sources (such as product databases or documentation) and use that information to ground its generated responses. This enables accurate answers to questions about recent product updates that were not part of the model’s original training data.
Question 5
What does a diffusion model do?
A. Generates high-quality content by refining noise into structured data.
B. Facilitates the storage and management of structured data.
C. Analyzes data and predicts future trends and patterns.
D. Optimizes business processes and resource allocation.
Show Answer
Correct Answer: A
Explanation: Diffusion models learn to generate data by starting from random noise and iteratively denoising it to produce coherent, high-quality samples such as images, audio, or text. The other options describe unrelated tasks like data storage, forecasting, or business optimization.
Question 5
A customer service team wants to use generative AI to improve the quality and consistency of their email responses to customer inquiries. They need a solution that can guide the AI to adopt a helpful, empathetic tone while adhering to company policies. Which prompting technique should they use?
A. Role prompting that instructs the AI to act as an experienced customer service representative with corporate knowledge.
B. Few-shot prompting that provides examples of good and bad customer service emails.
C. Prompt chaining that engages the AI in a conversation to gather the necessary information before generating the email response.
D. One-shot prompting that provides a single example of a good customer service email.
Show Answer
Correct Answer: A
Explanation: The goal is to guide the AI to consistently adopt a helpful, empathetic tone while following company policies. Role prompting directly addresses this by defining a clear persona (an experienced customer service representative with corporate knowledge), which naturally shapes tone, behavior, and policy adherence. The other techniques focus more on examples or interaction flow, but they do not as directly enforce consistent tone and policy alignment as role prompting does.
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