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| Topic | Details |
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| 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.
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| 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.
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| Topic 3 | - 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.
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| Topic 4 | - 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.
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Google Cloud Certified - Generative AI Leader Exam Sample Questions (Q21-Q26):
NEW QUESTION # 21
A large online retailer with a vast product catalog wants to improve customer satisfaction by making it easier for shoppers to find the specific products they ' re looking for. The retailer also wants to provide personalized recommendations to increase sales. What should the company do?
- A. Use AI Commerce Search on Gemini Enterprise for Customer Experience to enable natural language searches, provide custom recommendations, and improve product discovery.
- B. Use Google Cloud ' s Vision API to analyze product images, automatically tag the images with relevant keywords, and improve search accuracy.
- C. Use Agent Search on Gemini Enterprise Agent Platform to allow employees to search across the company ' s intranet and receive personalized search results based on their role, past searches, and current collaborators.
- D. Use Recommendations to provide custom recommendations to users.
Answer: A
Explanation:
AI Commerce Search on Gemini Enterprise for Customer Experience addresses both requirements: helping shoppers discover products through natural-language searches and delivering personalized recommendations that can increase conversions. It is purpose-built for commerce experiences and can interpret user intent, improve result relevance, and support individualized product discovery across large catalogs.
Recommendations alone addresses personalization but does not fully solve the natural-language product- search requirement. Vision API can identify and label image content, but image tagging by itself does not provide a complete commerce-search and recommendation experience. Agent Search on Gemini Enterprise Agent Platform is intended primarily for enterprise employees searching internal organizational information, not customers navigating a retail catalog. Because option C combines intelligent product search, personalized recommendations, and improved discovery within a commerce-focused offering, it is the most comprehensive solution.
NEW QUESTION # 22
A global news agency is developing a generative AI tool to quickly summarize breaking newsarticles 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?
- A. Grounding with Google Search
- B. BigQuery
- C. Document AI
- D. Vertex AI Natural Language API
Answer: A
Explanation:
For summarizing breaking news articles as they emerge online from various global sources, the generative AI model needs access to current, broad, and rapidly updating information. Grounding with Google Search allows the LLM to pull in the latest information from the web, ensuring the summaries are current and comprehensive. While Vertex AI Natural Language API can summarize text, it wouldn't inherently have access to the latest breaking news unless explicitly fed.
________________________________________
NEW QUESTION # 23
A language learning startup called VerbaQuest wants to improve outcomes for its learners.
Rather than a fixed syllabus, its app will use generative AI to observe each learner's quiz results in real time. When a learner has trouble with a grammar rule, the app immediately produces a simpler explanation and proposes a 5-question targeted drill. When the learner shows mastery, the app advances them to more challenging lessons and exercises. Which generative AI use case does this most closely reflect?
- A. Adaptive personalized experience
- B. Text generation
- C. Recommendation systems
- D. Automation
Answer: A
Explanation:
This scenario describes an app that continuously tailors explanations and practice to each learner based on real time quiz performance. It simplifies instruction when a learner struggles and advances them when they demonstrate mastery. That is the essence of adaptivity and personalization because the system shapes the pace, difficulty, and content for each individual rather than following a fixed syllabus.
Generative AI is the mechanism that produces the customized explanations and targeted drills, yet the defining pattern is the closed loop of observing performance, deciding on the next best action for this learner, and delivering bespoke content. That full loop is what characterizes an adaptive and personalized learning experience.
NEW QUESTION # 24
A software development team wants to use generative AI (gen AI) to code faster so they can launch their software prototype quicker. What should the team do?
- A. Use gen AI to refactor and optimize existing code.
- B. Use gen AI to identify potential bugs and security vulnerabilities in their code.
- C. Use gen AI to suggest code snippets and complete functions.
- D. Use gen AI to automatically generate comprehensive documentation for their code.
Answer: C
Explanation:
While generative AI can assist with all the options listed (refactoring, documentation, bug identification), its most direct and significant impact on coding faster for a prototype is through code generation. Suggesting code snippets and completing functions directly accelerates the writing of new code, enabling quicker prototyping.
NEW QUESTION # 25
SummitCart, a global e commerce fulfillment company, is deploying a generative AI driven system in its regional distribution centers to observe conveyor operations and forecast sorter and motor failures in real time. Any outage would pause order packing and could cost several million dollars per hour. When choosing the model and the managed platform, which characteristic should be prioritized for this mission critical rollout?
- A. High availability with a firm uptime Service Level Agreement
- B. The shortest end to end response latency
- C. Access to cutting edge features before general availability
- D. The lowest per request pricing across regions
Answer: A
Explanation:
This rollout is mission critical and any downtime would incur enormous costs, so the platform and model selection must prioritize guaranteed uptime.
For an always-on operational system in distribution centers you need high availability commitments that are explicit and enforceable. A documented uptime SLA signals that the provider designs and operates the service for reliability and that it will be supported with measurable objectives and remediation if targets are missed. Choosing services that publish clear availability targets and provide regional resilience, failover capabilities, and enterprise support reduces the risk of production outages and protects revenue.
NEW QUESTION # 26
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