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Google - Generative-AI-Leader–High-quality New Soft Simulations

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Google Generative-AI-Leader Exam Syllabus Topics:

Topic
Details

Topic 1

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 2

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 3

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 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.

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Google Cloud Certified - Generative AI Leader Exam Sample Questions (Q40-Q45):

NEW QUESTION # 40
A logistics company wants to use a generative AI (gen AI) agent to automatically check real-time inventory levels across its warehouses and adjust delivery schedules. The gen AI agent needs access to internal inventory data. They want the most cost-effective solution. What should the organization do?

A. Use pre-built gen AI chatbots for inventory questions.
B. Use Vertex AI Studio to fine-tune a model with sample inventory data.
C. Build a custom API instead of using the gen AI agent.
D. Use Google Cloud databases and Vertex AI for the agent to get live data.

Answer: D

Explanation:
To achieve real-time inventory checks and adjust delivery schedules, the generative AI agent needs live access to the company's internal inventory data. Google Cloud databases provide the structured storage for this data, and Vertex AI offers the platform to build, deploy, and manage the AI agent, including connecting it to these live data sources. This approach allows the agent to make informed decisions based on current information. Building a custom API for every interaction might be less cost-effective in the long run for dynamic inventory data. Pre-built chatbots might not have the direct integration needed for real-time adjustments, and fine-tuning with sample data wouldn't provide the live data access required.

NEW QUESTION # 41
What is an example of unsupervised machine learning?

A. Predicting subscription renewal based on past renewal status data.
B. Forecasting sales figures using historical sales and marketing spend.
C. Analyzing customer purchase patterns to identify natural groupings.
D. Training a system to recognize product images using labeled categories.

Answer: C

Explanation:
Unsupervised learning deals with unlabeled data. Identifying "natural groupings" or clusters in customer purchase patterns (e.g., segmenting customers into different buying behaviors without pre-defined labels) is a classic example of unsupervised learning (clustering). Options B, C, and D are examples of supervised learning, as they involve labeled data for training (product categories, renewal status, sales figures).
________________________________________

NEW QUESTION # 42
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. Monitoring the AI model's performance for unexpected outputs and potential errors.
B. Applying the latest software patches to the AI model on a regular basis.
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.

Answer: D

Explanation:
The stage mentioned is Data Collection/Training Data Preparation. In the machine learning lifecycle, this initial stage is where raw data is ingested and processed. If the model is being trained for customer service, the data (e.g., customer transcripts) is highly likely to contain sensitive information (like Personally Identifiable Information or PII).
Therefore, the most critical security and privacy consideration at this stage is protecting the integrity and confidentiality of the data itself.
Implementing strong access controls and protecting sensitive information (A) is the essential first step in a secure AI pipeline, aligning with Google's Secure AI Framework (SAIF). If data access is not controlled and sensitive data is not de-identified or redacted before it is used for training, the resulting model could leak that sensitive information to users.
Options B, C, and D are all important controls, but they occur at later stages of the ML lifecycle:
B (Software patches/latest versions) is part of deployment and management.
C (Ethical guidelines/fairness) is a Responsible AI goal implemented via guardrails and testing (later stages).
D (Monitoring) is an MLOps step that happens after deployment.
The critical consideration at the data collection stage is ensuring the data's security and privacy before it influences the model.
(Reference: Google Cloud guidance on securing generative AI emphasizes that one of the most significant risks is data leakage, making safeguarding training data and implementing identity and access control the foundational steps in the data ingestion and preparation phases.)

NEW QUESTION # 43
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 spend 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 Natural Language API
B. Pre-built RAG with Vertex AI Search
C. Vertex AI Conversation
D. Vertex AI Model Garden

Answer: B

Explanation:
This scenario strongly points to the need for accurate and up-to-date information retrieval from a product catalog. Pre-built RAG (Retrieval-Augmented Generation) combined with Vertex AI Search is the ideal solution. Vertex AI Search can index the product catalog, and RAG can then use thisindexed data to ground the responses of a generative AI model, ensuring that both customer searches and support agent queries retrieve precise and relevant product information.
________________________________________

NEW QUESTION # 44
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

Answer: D

Explanation:
Vertex AI is Google Cloud's unified machine learning platform that provides end-to-end support for the ML lifecycle, including access to pre-trained models (foundation models), tools for fine-tuning, deployment, and management of generative AI solutions. BigQuery is a data warehouse, GKE is for container orchestration, and Cloud Storage is for object storage; while they might be components used with Vertex AI, they are not the comprehensive generative AI platform themselves.
________________________________________

NEW QUESTION # 45
......

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