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| Certification Vendor: | Google Cloud |
|---|---|
| Exam Name: | Generative AI Leader |
| Exam Number: | Generative-AI-Leader |
| Available Languages: | English |
| Certificate Validity Period: | 2 years |
| Exam Duration: | 90 minutes |
| Exam Price: | $99 USD |
| Real Exam Qty: | 50-60 |
| Exam Format: | Multiple select, Multiple choice |
| Related Certifications: | Google Cloud Digital Leader Google Cloud Professional Machine Learning Engineer |
| Recommended Training: | Google Cloud Skills Boost - Generative AI learning paths |
| Exam Registration: | Google Cloud Certification Portal |
| Sample Questions: | Google Generative-AI-Leader Sample Questions |
| Exam Way: | Online proctored exam |
| Pre Condition: | No strict prerequisites; basic understanding of cloud computing and AI concepts recommended |
| Official Syllabus URL: | https://cloud.google.com/learn/certification/generative-ai-leader |
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질문 # 33
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 However, the training dataset disproportionately includes feedback from a specific demographic and uses outdated language norms that don't reflect current customer communication styles.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?
정답：B
설명：
The core reason for the model's failure is that the training data itself was flawed (disproportionate demographic representation and outdated language). This flaw directly leads to the observed bias and poor performance on underrepresented groups and modern communication styles.
This is a classic example of Data Dependency, a fundamental limitation of all machine learning models, including generative AI. Data dependency refers to the absolute reliance of an AI model on the quality, completeness, and fairness of the data on which it was trained. Since the model essentially only mimics the patterns it learned from its dataset, if the dataset contains societal, demographic, or linguistic biases, the model will faithfully reproduce and amplify those biases in its output, leading to unfair classification for certain groups.
Hallucination (C) is the invention of facts or data.
Overfitting (D) is poor generalization because the model memorized the training data too well, typically resulting in very poor performance across all unseen data, not just specific demographics.
Bias is the result of the data dependency, not the fundamental limitation itself.
(Reference: Google's training on Generative AI Limitations identifies Data Dependency as the fundamental limitation where the model is limited by the scope and quality of its training data, directly leading to issues of bias when the data is not diverse or representative.)
질문 # 34
A company's development team is eager to start building generative AI solutions with Google Cloud, but has limited experience in AI development. They need to launch their gen AI solution quickly. What Google Cloud benefit would help the company achieve their goal?
정답：C
설명：
For a team with limited AI experience needing to launch quickly, leveraging pre-trained models (foundation models) and low-code/no-code tools significantly reduces the development burden and accelerates time to market. This allows them to build and deploy generative AI solutions without requiring deep expertise from scratch. While other options are helpful, this directly addresses the need for quick launch with limited experience.
질문 # 35
Pike and Rowan Law uses a generative AI system to produce first draft contract clauses for its clients. To ensure accuracy and compliance, a licensed attorney must review, edit, and approve each AI draft before any client sees it. This addition of expert oversight within the AI workflow represents which recommended practice?
정답：A
설명：
The correct option is Human in the Loop (HITL) because a licensed attorney must review, edit, and approve each AI draft before any client sees it.
Adding human oversight ensures accuracy, legal compliance, and accountability for outputs. HITL is a recommended practice for high-stakes domains where expert judgment must validate model results before release, which matches a legal drafting workflow that requires professional approval.
질문 # 36
A market research firm wants to use a Google Cloud prebuilt generative AI offering to streamline the process of extracting and synthesizing information from lengthy market reports and research papers. Their goal is to improve efficiency and provide faster insights to their clients. What should the organization do?
정답：A
설명：
NotebookLM is a prebuilt, source-grounded research and synthesis tool designed for working with uploaded documents and other selected sources. The firm can provide its market reports and research papers, ask questions about their contents, generate summaries, compare information, and identify important themes.
Because responses are grounded in the supplied sources, researchers can obtain focused insights more efficiently while retaining the ability to review supporting material. The standard Gemini app can provide general assistance but is less specifically optimized for a defined document collection. Building custom conversational agents would introduce unnecessary development effort when a prebuilt offering satisfies the requirement. Gemini in Google Workspace can assist with drafting and collaboration, but the central need is extracting and synthesizing information from lengthy source documents. NotebookLM is therefore the most appropriate choice.
질문 # 37
A company wants to use generative AI to create a chatbot that can answer customer questions about their products and services. They need to ensure that the chatbot only uses information from the company's official documentation. What should the company do?
정답：D
설명：
The core requirement is to guarantee that the chatbot only uses information from the company's official documentation and does not rely on its general knowledge base. This is crucial for ensuring factual accuracy, relevance to the company's specific products, and preventing the generation of fabricated or incorrect information (hallucinations).
The specific technique designed to address this challenge is Grounding. Grounding is the process of connecting the Large Language Model's (LLM's) responses to a trusted, verifiable source of information, such as an organization's internal documents, databases, or live data feeds. When an LLM is grounded, it is forced to base its answers only on the provided context, effectively preventing it from drawing on its broad, generalized training data. Grounding is often implemented using a method called Retrieval-Augmented Generation (RAG), particularly with tools like Google Cloud's Vertex AI Search, which indexes the official documentation and feeds the relevant snippets to the model.
Options A, B, and C address different aspects of model output: Role prompting sets the model's persona, adjusting temperature controls creativity, and prompt chaining manages conversation history, but none of these techniques restrict the model's source of truth to the official documentation. Therefore, Grounding is the correct and most effective technique for this requirement.
질문 # 38
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