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Google Generative-AI-Leader Exam Overview:

Certification Vendor:	Google Cloud
Exam Name:	Generative AI Leader Certification Exam
Exam Number:	Generative-AI-Leader
Exam Price:	USD 99 (plus tax where applicable)
Certificate Validity Period:	3 years
Passing Score:	Not publicly disclosed
Exam Duration:	90 minutes
Available Languages:	English, Spanish, Portuguese, Japanese
Exam Format:	Multiple choice
Real Exam Qty:	50-60
Recommended Training:	Generative AI Leader Study Guide Generative AI Leader Training Course
Exam Registration:	Google Cloud Certification Registration
Sample Questions:	Google Generative-AI-Leader Sample Questions
Exam Way:	Online-proctored or onsite-proctored
Pre Condition:	No prerequisites required; open to all roles and backgrounds
Official Syllabus URL:	https://cloud.google.com/learn/certification/generative-ai-leader

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Generative-AI-Leader技術内容 & Generative-AI-Leader PDF問題サンプル

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Google Generative-AI-Leader 認定試験の出題範囲：

トピック	出題範囲
トピック 1	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.
トピック 2	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.
トピック 3	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.
トピック 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.

Google Cloud Certified - Generative AI Leader Exam 認定 Generative-AI-Leader 試験問題 (Q85-Q90):

質問 # 85
A team is using a generative AI model to automatically generate short summaries of customer feedback. They need to ensure that these summaries are concise and easy to digest. What model setting should they adjust?

A. Temperature
B. Top-p (nucleus sampling)
C. Output length
D. Safety settings

正解：C

解説：
The objective is to make the generated summaries concise-that is, to control their length.
In the configuration of a generative AI model, particularly a large language model (LLM), the parameter used to directly control the maximum size of the response is the Output Length parameter (often referred to as max_output_tokens or max_tokens). By setting a low limit on this parameter, the team can ensure that the model is forced to terminate its response once that limit is reached, resulting in a shorter, more concise summary that is " easy to digest, " as requested.
The other parameters control different aspects of the output quality:
Temperature (C) controls the creativity or randomness of the output. Lowering it makes the output more predictable; raising it makes it more diverse. It does not control length.
Top-p (A) is a decoding method related to temperature that also controls the model ' s creativity by limiting the vocabulary from which it can choose the next token. It does not control length.
Safety settings (B) are used to filter and block the generation of harmful, illegal, or inappropriate content.
They do not affect the length or conciseness of the output.
(Reference: Google Cloud ' s Generative AI documentation on model parameters explicitly lists max_output_tokens or Output Length as the setting used to determine the maximum size of a model ' s generated response.)

質問 # 86
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. Implementing access controls and protecting sensitive information within the training data.
B. Applying the latest software patches to the AI model on a regular basis.
C. Monitoring the AI model's performance for unexpected outputs and potential errors.
D. Establishing ethical guidelines for AI model responses to ensure fairness and avoid harm.

正解：A

解説：
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.

質問 # 87
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?

A. BigQuery
B. Grounding with Google Search
C. Vertex AI Natural Language API
D. Document AI

正解：B

解説：
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.
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質問 # 88
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?

A. To provide tools and resources for building and deploying gen AI applications.
B. To provide access to pre-trained gen AI models.
C. To provide a user-friendly interface for interacting with gen AI models.
D. To provide the compute resources needed to run and train AI models and store training data.

正解：D

解説：
The infrastructure layer supplies the foundational computing, storage, networking, and acceleration resources required to train and run generative AI models. This includes CPUs, GPUs, TPUs, high-performance networks, scalable storage, and systems optimized for demanding AI workloads. Training foundation models and serving model responses require substantial processing capacity, while training datasets and model artifacts require reliable storage. Access to pre-trained models belongs primarily to the model layer. A user-friendly model interface is part of the application or experience layer. Development, deployment, tuning, and management tools belong to the platform layer. These layers work together, but their functions are distinct. Because the question asks specifically about the infrastructure layer, the correct function is supplying the computational resources and data storage needed to train and operate AI models.

質問 # 89
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 AI solutions are pre-packaged for easy deployment, eliminating the need for customization and integration efforts.
B. Google Cloud's strict adherence to proprietary technologies ensures the highest level of security and performance.
C. Google Cloud's focus on automation aims to replace human jobs with AI systems, potentially leading to significant workforce reductions.
D. Google Cloud's AI solutions have an open approach that supports customer choice across offerings.

正解：D

解説：
Google Cloud promotes an open and flexible approach to its AI offerings, supporting open standards, open- source initiatives (like TensorFlow, Kubernetes, and Gemma), and providing various integration options. This helps alleviate vendor lock-in concerns by giving customers choice and control over their technology stack.
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質問 # 90
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