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| Certification Vendor: | Google Cloud |
|---|---|
| Exam Name: | Generative AI Leader |
| Exam Number: | Generative-AI-Leader |
| Exam Duration: | 90 minutes |
| Certificate Validity Period: | 2 years |
| Related Certifications: | Google Cloud Digital Leader Google Cloud Professional Machine Learning Engineer |
| Exam Format: | Multiple choice, Multiple select |
| Real Exam Qty: | 50-60 |
| Available Languages: | English |
| Exam Price: | $99 USD |
| 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 |
>> Generative-AI-Leader Original Questions <<
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NEW QUESTION # 55
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?
Answer: C
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 this indexed 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 # 56
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?
Answer: B
Explanation:
When AI struggles with nuances like sarcasm or satire, human oversight is often the most effective solution. A human-in-the-loop (HITL) process allows human reviewers to check, correct, and refine AI-generated content before it is published, ensuring accuracy and appropriateness, especially for sensitive or complex language.
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NEW QUESTION # 57
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?
Answer: B
Explanation:
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.)
NEW QUESTION # 58
A customer support team uses a generative AI agent to answer product questions. The AI cannot answer questions about a new software version with features and troubleshooting documented in internal articles that weren ' t part of its training data. What cost-effective technique should they use to improve their output?
Answer: C
Explanation:
Retrieval-augmented generation connects the agent to an external knowledge source containing the current internal articles. When a customer submits a question, the system retrieves relevant passages and supplies them to the model as grounding context before the response is generated. This allows the agent to answer questions about the new software release without retraining the foundation model. It is cost-effective because the knowledge index can be refreshed whenever documentation changes, keeping responses current while reducing hallucinations. Role-based and basic prompting can influence behavior or response style but cannot supply facts that the model has never received. Fine-tuning may modify model behavior, but it is more expensive, slower to update, and generally less suitable for frequently changing factual documentation. RAG therefore provides the most practical and maintainable solution.
NEW QUESTION # 59
A customer service team wants to use generative AI to improve the quality and consistency of their email responses to customer inquiries. They need a solution that can guide the AI to adopt a helpful, empathetic tone while adhering to company policies. Which prompting technique should they use?
Answer: C
Explanation:
The most direct and effective way to influence the style, personality, and knowledge context of an AI's response is through Role Prompting.
Role Prompting involves instructing the model to assume a specific persona (a "role") before responding. By assigning the AI the role of an "experienced customer service representative" (B), the model is implicitly directed to adopt a professional, helpful, and empathetic tone. Furthermore, specifying "with corporate knowledge" directs the model to prioritize responses consistent with internal company policies. This technique is a foundational element of prompt engineering, often used in conjunction with other methods (like grounding, if specific policy documents were needed) to dramatically shift the output style and relevance.
While Few-shot prompting (D) could provide examples to influence style, it's less efficient than a clear role instruction and still requires the model to infer the persona. Prompt Chaining (A) is used to manage multi-turn conversation memory, not to set the tone or persona. Therefore, defining the Role is the core technique for establishing both the desired tone and the necessary professional context in a single instruction.
(Reference: Google's documentation on prompt engineering for customer service shows examples where users begin the prompt with "I am a customer service representative" to set the tone and persona for the generated response, confirming Role Prompting as the technique for ensuring style and consistency.)
NEW QUESTION # 60
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