Pass Generative-AI-Leader Exam – Real Test Engine PDF with 103 Questions [Q44-Q61]

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Pass Generative-AI-Leader Exam – Real Test Engine PDF with 103 Questions

Get New Generative-AI-Leader Certification Practice Test Questions Exam Dumps

NEW QUESTION 44
What is a key advantage of using Google’s custom-designed TPUs?

TPUs are lightweight processors intended for deployment on edge devices.

TPUs increase the storage capacity and data retrieval speeds within Google Cloud data centers.

TPUs are specialized AI processors that excel at parallel processing for machine learning workloads.

TPUs are primarily designed to improve the general processing speed of virtual machines in the cloud.

NEW QUESTION 45
A social media platform uses a generative AI model to automatically generate summaries of user-submitted posts to provide quick overviews for other users. While the summaries are generally accurate for factual posts, the model occasionally misinterprets sarcasm, satire, or nuanced opinions, leading to summaries that misrepresent the original intent and potentially cause misunderstandings or offense among users. What should the platform do to overcome this limitation of the AI-generated summaries?

Implement stricter safety settings to filter out potentially misinterpreted content altogether.

Increase the temperature parameter of the model to encourage more varied and less literal interpretations.

Decrease the output length of the summaries to make them more concise.

Incorporate a human-in-the-loop (HITL) review process to refine the summaries.

NEW QUESTION 46
A large e-commerce 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?

Vertex AI Search

Vertex AI Platform

Google Shopping

Google Search

NEW QUESTION 47
A financial institution uses generative AI (gen AI) to approve and reject loan applications, but gives no reasons for rejection. Customers are starting to file complaints. The company needs to implement a solution to reduce the complaints. What should the company do?

Collect a larger and more diverse dataset for the gen AI model.

Implement explainable gen AI policies.

Fine-tune the gen AI model.

Develop fairness assessments for the gen AI model.

NEW QUESTION 48
A home loan company is deploying a generative AI system to automate initial loan application reviews.
Several applicants have been unexpectedly rejected, leading to customer complaints and potential bias concerns. They need to ensure responsible and fair lending practices. What aspect of the AI system should they prioritize?

Increasing the speed at which the AI system processes loan applications to handle the high volume.

Ensuring AI decision-making is explainable to understand decision reasons and establish accountability.

Regularly updating the AI model with more financial data to improve its accuracy over time.

Implementing stricter data security measures to protect applicants ‘ financial information from unauthorized access.

NEW QUESTION 49
What does Model Garden enable a company to do?

Discover, customize, and deploy existing models from Google and its partners.

Evaluate the performance of different models using various metrics.

Manage different versions of a model, including the code, data, and parameters used to train it.

Train new models from scratch using large datasets.

NEW QUESTION 50
A research team has collected a large dataset of sensor readings from various industrial machines. This dataset includes measurements like temperature, pressure, vibration levels, and electrical current, recorded at regular intervals. The team has not yet assigned any labels or categories to these readings and wants to identify potential anomalies, malfunctions, or natural groupings of machine behavior based on the sensor data alone. What type of machine learning should they use?

Reinforcement learning

Unsupervised learning

Deep learning

Supervised learning

NEW QUESTION 51
A creative agency named Northshore Images plans to fine tune an image generation model using Vertex AI, and it needs a single repository to hold about 32 TB of source pictures. The data science group requires extremely durable and elastically scalable storage for unstructured objects that will feed their Vertex AI training runs. Which Google Cloud service best fits storing large collections of object data such as image files?

Vertex AI Model Registry

BigQuery

Cloud Storage

Cloud SQL

NEW QUESTION 52
A sales manager wants to responsibly use generative AI (gen AI) to increase efficiency with their existing tasks. They want to allow the sales team to focus on building customer relationships and closing deals. How should the sales team use gen AI?

To replace the sales team’s CRM system with a more intuitive and user-friendly interface.

To analyze customer interactions on social media and automatically generate sales pitches tailored to their public profiles.

To draft emails and provide real-time insights about customer needs.

To automate creative content like blog posts and social media updates to attract new leads.

NEW QUESTION 53
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?

Data dependency

Edge case

Hallucination

Overfitting

NEW QUESTION 54
What is a key advantage of using Google ‘ s custom-designed TPUs?

TPUs are lightweight processors intended for deployment on edge devices.

TPUs increase the storage capacity and data retrieval speeds within Google Cloud data centers.

TPUs are specialized AI processors that excel at parallel processing for machine learning workloads.

TPUs are primarily designed to improve the general processing speed of virtual machines in the cloud.

NEW QUESTION 55
A global news agency is developing a generative AI tool to quickly summarize breaking news articles as they emerge online. The goal is to provide their audience with rapid updates on fast- developing stories from various global sources. What Google Cloud solution should they use?

Document AI

BigQuery

Vertex AI Natural Language API

Grounding with Google Search

NEW QUESTION 56
A company is evaluating different generative AI (gen AI) platforms and wants to understand the role of the infrastructure layer in supporting the development and deployment of gen AI models. What is the function of the infrastructure layer in the gen AI landscape?

To provide access to pre-trained gen AI models.

To provide a user-friendly interface for interacting with gen AI models.

To provide the compute resources needed to run and train AI models and store training data.

To provide tools and resources for building and deploying gen AI applications.

NEW QUESTION 57
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 (PII). They need to mitigate these risks before using the data to train the AI model. What action should the company prioritize?

Focusing on collecting only quantitative feedback data in future surveys.

Ensuring that the AI model is trained on a large and diverse dataset.

Implementing strong access controls to limit which teams can view the raw survey data.

Applying data anonymization techniques to remove or obscure sensitive data.

NEW QUESTION 58
A sales manager wants to responsibly use generative AI (gen AI) to increase efficiency with their existing tasks. They want to allow the sales team to focus on building customer relationships and closing deals. How should the sales team use gen AI?

To replace the sales team ‘ s CRM system with a more intuitive and user-friendly interface.

To analyze customer interactions on social media and automatically generate sales pitches tailored to their public profiles.

To draft emails and provide real-time insights about customer needs.

To automate creative content like blog posts and social media updates to attract new leads.

NEW QUESTION 59
What does Model Garden enable a company to do?

Discover, customize, and deploy existing models from Google and its partners.

Evaluate the performance of different models using various metrics.

Manage different versions of a model, including the code, data, and parameters used to train it.

Train new models from scratch using large datasets.

NEW QUESTION 60
A research team has collected a large dataset of sensor readings from various industrial machines. This dataset includes measurements like temperature, pressure, vibration levels, and electrical current, recorded at regular intervals. The team has not yet assigned any labels or categories to these readings and wants to identify potential anomalies, malfunctions, or natural groupings of machine behavior based on the sensor data alone. What type of machine learning should they use?

Reinforcement learning

Unsupervised learning

Deep learning

Supervised learning

NEW QUESTION 61
A company is exploring Google Agentspace to improve how its employees search for information on their enterprise systems and automate certain tasks. What is the key business advantage of using Agentspace?

Enhanced real-time communication and collaboration among team members.

Greater interoperability with legacy software systems and databases.

Improved productivity and data interaction using AI assistants and advanced document analysis.

More granular control over support team access and permissions for sensitive data.

Google Generative-AI-Leader Exam Syllabus Topics:

Topic	Details
Topic 1	Techniques to Improve Generative AI Model Output: This section of the exam measures the skills of AI Engineers and focuses on improving model reliability and performance. It introduces best practices to address common foundation model limitations such as bias, hallucinations, and data dependency, using methods like retrieval-augmented generation, prompt engineering, and human-in-the-loop systems. Candidates are also tested on different prompting techniques, grounding approaches, and the ability to configure model settings such as temperature and token count to optimize results.
Topic 2	Business Strategies for a Successful Generative AI Solution: This section of the exam measures the skills of Cloud Architects and evaluates the ability to design, implement, and manage enterprise-level generative AI solutions. It covers the decision-making process for selecting the right solution, integrating AI into an organization, and measuring business impact. A strong emphasis is placed on secure AI practices, highlighting Google’s Secure AI Framework and cloud security tools, as well as the importance of responsible AI, including fairness, transparency, privacy, and accountability.
Topic 3	Fundamentals of Generative AI: This section of the exam measures the skills of AI Engineers and focuses on the foundational concepts of generative AI. It covers the basics of artificial intelligence, natural language processing, machine learning approaches, and the role of foundation models. Candidates are expected to understand the machine learning lifecycle, data quality, and the use of structured and unstructured data. The section also evaluates knowledge of business use cases such as text, image, code, and video generation, along with the ability to identify when and how to select the right model for specific organizational needs.
Topic 4	Google Cloud’s Generative AI Offerings: This section of the exam measures the skills of Cloud Architects and highlights Google Cloud’s strengths in generative AI. It emphasizes Google’s AI-first approach, enterprise-ready platform, and open ecosystem. Candidates will learn about Google’s AI infrastructure, including TPUs, GPUs, and data centers, and how the platform provides secure, scalable, and privacy-conscious solutions. The section also explores prebuilt AI tools such as Gemini, Workspace integrations, and Agentspace, while demonstrating how these offerings enhance customer experience and empower developers to build with Vertex AI, RAG capabilities, and agent tooling.