Free Sample — 15 Practice Questions
Preview 15 of 55 questions from the Generative AI Leader exam.
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Question 9
A human resources team is implementing a new generative AI application to assist the department in screening a large volume of job applications. They want to ensure fairness and build trust with potential candidates. What should the team prioritize?
A. Integrating the AI application with various job boards to maximize candidate reach.
B. Focusing on minimizing the processing time for each application to improve efficiency.
C. Ensuring AI operates transparently, especially regarding application evaluation and data usage.
D. Ensuring that the AI application can automatically rank all candidates without requiring human review.
Show Answer
Correct Answer: C
Explanation:
To ensure fairness and build trust in an AI-assisted hiring process, the priority should be transparency. Clearly explaining how the AI evaluates applications, what data it uses, and its limitations enables accountability, allows bias to be identified and addressed, and reassures candidates that decisions are made responsibly. The other options focus on efficiency or automation goals that do not directly address fairness or trust.
Question 30
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. Use few-shot prompting.
B. Use role prompting.
C. Adjust the temperature parameter.
D. Use prompt chaining.
Show Answer
Correct Answer: B
Explanation:
The goal is to ensure the generated marketing text is appropriate for a specific target audience, which primarily involves controlling tone, style, and perspective. Role prompting directly addresses this by instructing the model to adopt a specific persona (e.g., a marketing expert speaking to a defined audience), guiding language and messaging. Few-shot prompting provides examples, temperature adjusts randomness/creativity, and prompt chaining manages multi-step tasks, but none are as direct or effective as role prompting for audience alignment.
Question 51
A financial services company receives a high volume of loan applications daily submitted as scanned documents and PDFs with varying layouts. The manual process of extracting key information is time-consuming and prone to errors. This causes delays in loan processing and impacts customer satisfaction. The company wants to automate the extraction of this critical data to improve efficiency and accuracy. Which Google Cloud tool should they use?
A. Document AI API
B. Natural Language API
C. Vision AI
D. Dataflow
Show Answer
Correct Answer: A
Explanation:
The Document AI API is designed to extract structured data from unstructured documents such as scanned images and PDFs with varying layouts. It uses OCR and layout-aware machine learning models to accurately identify and extract key fields (names, dates, amounts, tables) from documents like loan applications. This directly addresses the need to automate data extraction, reduce manual effort, and improve accuracy and processing speed. The other options do not specialize in end-to-end document understanding and structured data extraction.
Question 42
A company wants a generative AI platform that provides the infrastructure, tools, and pre-trained models needed to build, deploy, and manage its generative AI solutions. Which Google Cloud offering should the company use?
A. BigQuery
B. Google Kubernetes Engine (GKE)
C. Google Cloud Storage
D. Vertex AI
Show Answer
Correct Answer: D
Explanation:
Vertex AI is Google Cloud’s managed generative AI and ML platform that provides pre-trained foundation models, development tools, and infrastructure to build, deploy, and manage generative AI solutions. The other options are general data storage or compute services and do not provide an end-to-end generative AI platform.
Question 31
A large e-commerce company with a vast and frequently updated product catalog finds that customers struggle to find products on their website, and support agents speed too much time finding detailed product information. The company wants to improve search accuracy and efficiency for both customers and support. What Google Cloud solution should they use?
A. Vertex AI Conversation
B. Vertex AI Natural Language API
C. Pre-built RAG with VERTEX AI Search
D. Vertex AI Model Garden
Show Answer
Correct Answer: C
Explanation:
The company needs an end-to-end solution to search a large, frequently changing product catalog with high accuracy and efficiency for both customers and support agents. Pre-built RAG with Vertex AI Search is designed for this use case: it indexes enterprise data, provides Google-quality search, and uses retrieval-augmented generation to answer natural-language queries grounded in the up-to-date catalog. The other options are components or APIs, not a complete search and retrieval solution.
Question 45
A highly regulated financial institution wants to use Gemini as the core decision engine for a loan approval system that will deterministically approve or reject loan applications based on a strict set of predefined criteria. Why is this an inappropriate use case for Gemini?
A. Gemini deployment for this scenario would be too expensive and complex.
B. Gemini’s designed for flexible content generation and inference, not rigid rule-based decisions.
C. Gemini is not equipped to handle structured numerical data for financial assessments.
D. Gemini cannot integrate with required financial databases.
Show Answer
Correct Answer: B
Explanation:
The scenario requires strictly deterministic, rule-based decisions for regulatory compliance. Gemini is a generative, probabilistic model optimized for flexible language understanding and inference, not for enforcing rigid, deterministic business rules. Using it as a core decision engine could introduce nondeterminism and unpredictability, making it unsuitable for this use case.
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 32
A company wants to adopt generative AI and is concerned about vendor lock-in. They want to maintain flexibility in their technology stack. What Google Cloud strength would ease their concerns?
A. Google Cloud’s strict adherence to proprietary technologies ensures the highest level of security and performance.
B. Google Cloud’s AI solutions are pre-packaged for easy deployment, eliminating the need for customization and integration efforts.
C. Google Cloud’s AI solutions have an open approach that supports customer choice across offerings.
D. Google Cloud’s focus on automation aims to replace human jobs with AI systems, potentially leading to significant workforce reductions.
Show Answer
Correct Answer: C
Explanation:
Vendor lock-in concerns are best addressed by an open approach that supports customer choice, interoperability, and use of open standards and open-source models. Google Cloud emphasizes openness across its AI offerings, enabling flexibility in tools, models, and integrations, unlike proprietary-only or rigid, pre-packaged solutions.
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 33
A large ecommerce company with a substantial product catalog and many support documents has customers struggling to find information on their website. This leads to high support costs and poor user experience. The company wants a Google Cloud solution to improve website search and reduce support costs while improving customer satisfaction. What Google Cloud product should the company use?
A. Google Shopping
B. Google Search
C. Vertex AI Search
D. Vertex AI Platform
Show Answer
Correct Answer: C
Explanation:
Vertex AI Search is purpose-built to deliver Google-quality, AI-powered search over an organization’s own data, such as large product catalogs and support documents. It enables natural-language and relevance-based search on websites, helping customers find information quickly, which reduces support costs and improves user experience. The other options are either consumer-facing services or general ML platforms, not specialized enterprise search solutions.
Question 24
What is an example of unsupervised machine learning?
A. Predicting subscription renewal based on past renewal status data.
B. Training a system to recognize product images using labeled categories.
C. Analyzing customer purchase patterns to identify natural groupings.
D. Forecasting sales figures using historical sales and marketing spend.
Show Answer
Correct Answer: C
Explanation:
Unsupervised learning works with unlabeled data to discover underlying structure. Analyzing customer purchase patterns to identify natural groupings is a classic clustering task, which does not rely on predefined labels. The other options involve prediction or classification using labeled outcomes, which are supervised learning.
Question 21
An organization needs an AI tool to analyze and summarize lengthy customer feedback text transcripts. You need to choose a Google foundation model with a large context window. What foundation model should the organization choose?
A. Gemini
B. CodeGemma
C. Imagen
D. Chirp
Show Answer
Correct Answer: A
Explanation:
The requirement is a foundation model that can analyze and summarize very long text transcripts, which primarily depends on having a large context window and strong natural language understanding. Gemini models (especially Gemini 1.5 variants) are designed for long-context text comprehension and summarization. CodeGemma is specialized for code, Imagen is for image generation, and Chirp focuses on speech-to-text rather than text analysis.
Question 39
What will Google Cloud’s Agent Assist help a company achieve?
A. The ability to analyze conversational data to identify customer sentiment, common topics of discussion, and insights into agent performance and customer experience.
B. The ability to provide real-time assistance and recommended responses to live customer service agents during their interactions.
C. The infrastructure to provide an enterprise-grade contact center solution with omnichannel support, routing, and integration with CRM systems.
D. The ability to build and deploy deterministic and generative chatbot agents for automated customer support.
Show Answer
Correct Answer: B
Explanation:
Google Cloud’s Agent Assist is designed to support human customer service agents during live interactions by providing real-time suggestions, recommended responses, and relevant knowledge articles. Its primary goal is to improve agent effectiveness and customer experience in the moment, rather than replacing agents, delivering full contact center infrastructure, or focusing mainly on post-call analytics.
Question 16
An organization wants to use generative AI to create a chatbot that can answer customer questions about their account balances. They need to ensure that the chatbot can access previous portions of the conversation with the customer. Which prompting technique should they use?
A. Use prompt chaining.
B. Use zero-shot prompting.
C. Use role prompting.
D. Use few-shot prompting.
Show Answer
Correct Answer: A
Explanation:
The requirement is that the chatbot can access and use previous parts of the conversation. Prompt chaining supports multi‑turn interactions by carrying forward outputs and context from earlier prompts into subsequent ones, effectively maintaining conversational history. Zero‑shot, role, and few‑shot prompting do not inherently provide conversation memory.
Question 36
The office of the CISO wants to use generative AI (gen AI) to help automate tasks like summarizing case information, researching threats, and taking actions like creating detection rules. What agent should they use?
A. Data agent
B. Security agent
C. Customer service agent
D. Code agent
Show Answer
Correct Answer: B
Explanation:
The tasks described—summarizing security cases, researching threats, and creating detection rules—are core cybersecurity functions. A Security agent is specifically designed to support threat detection, investigation, and automated security actions, aligning directly with the CISO’s needs.