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| Certification Vendor: | Google Cloud |
|---|---|
| Exam Name: | Google Cloud Certified - Generative AI Leader Exam |
| Exam Number: | GCP-GAIL |
| Exam Price: | USD 99.00 |
| Available Languages: | English |
| Certificate Validity Period: | 3 years |
| Passing Score: | Pass / Fail (Approx 70%) |
| Related Certifications: | Google Cloud Certified - Generative AI Leader |
| Exam Duration: | 90 minutes |
| Real Exam Qty: | 50-60 |
| Exam Format: | Multiple choice questions with single or multiple correct answers |
| Sample Questions: | Google Generative-AI-Leader Sample Questions |
| Exam Way: | Remote as well as onsite |
| Pre Condition: | This certification is for anyone in any job role, with or without hands-on technical experience. |
| Official Syllabus URL: | https://cloud.google.com/learn/certification/generative-ai-leader |
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NEW QUESTION # 53
A sales manager wants to responsibly use generative AI (gen AI) to increase efficiency with their existing tasks. They want to allow the sales team to focus on building customer relationships and closing deals. How should the sales team use gen AI?
Answer: B
Explanation:
The strategic goal is to boost sales efficiency by shifting the team's focus to high-value activities (relationships and closing deals) by automating repetitive administrative tasks.
Option C directly addresses this goal by leveraging Gen AI's core capabilities for text generation and summarization/analysis:
Drafting emails automates a major time sink for sales reps (a common, repetitive task).
Providing real-time insights automates the labor-intensive research and manual data analysis required to understand customer needs, giving the rep instant, actionable context.
Options A and D are less direct solutions for improving sales efficiency: Option A is an expensive, high-risk platform replacement, not an efficiency use case. Option D describes marketing tasks, which, while related, are not the primary, day-to-day tasks that sales reps perform to clear their schedules for relationship building. Therefore, Gen AI's most effective role in sales is as a productivity assistant for drafting and quick research.
(Reference: Google Cloud documentation on sales enablement use cases emphasizes that Gen AI's role is to automate administrative and time-consuming tasks like drafting outreach messages and synthesizing customer information to enhance seller productivity, allowing them to focus on revenue-generating activities.)
NEW QUESTION # 54
A company is developing an AI character for a video game. The AI character needs to learn how to navigate a complex environment and make decisions to achieve certain objectives within the game. When the AI takes actions that lead to positive outcomes, like finding a reward or overcoming an obstacle, it receives a positive score. When it takes actions that lead to negative outcomes, like hitting a wall or losing progress, it receives a negative score. Through this process of trial and error, the AI gradually improves the character's ability to play the game effectively. What machine learning should the company use?
Answer: B
Explanation:
This scenario perfectly describes reinforcement learning. In reinforcement learning, an agent learns to make decisions by interacting with an environment, receiving1 rewards for desirable actions and penalties for undesirable ones,2 and iteratively improving its behavior through trial and error to maximize cumulative reward.
NEW QUESTION # 55
A global news company is using a large language model to automatically generate summaries of news articles for their website. The model's summary of an international summit was accurate until it hallucinated by stating a detail that did not occur. How should the company overcome this hallucination?
Answer: C
Explanation:
The core problem is the model's hallucination--it invented a factual detail--in a context (news reporting) where factual accuracy is non-negotiable. To correct a factual error in a generative summary, the model must be constrained to speak only based on verifiable facts from a reliable source.
The most effective technique to combat hallucinations and ensure factual adherence is Grounding (D). Grounding connects the Large Language Model's (LLM's) output to a specific, trusted, and verifiable source of information. This is often implemented using Retrieval- Augmented Generation (RAG). In this scenario, grounding the summary model on the original source articles ensures that every generated statement is directly entailed by the provided facts (the source article content).
NEW QUESTION # 56
What will Google Cloud's Agent Assist help a company achieve?
Answer: A
Explanation:
Google Cloud's Agent Assist is specifically designed to augment human customer service agents. It provides real-time suggestions, retrieves relevant information, and offers recommended responses to agents during live interactions, improving their efficiency and consistency.
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NEW QUESTION # 57
A home loan company is deploying a generative AI system to automate initial loan application reviews. Several applicants have been unexpectedly rejected, leading to customer complaints and potential bias concerns. They need to ensure responsible and fair lending practices. What aspect of the AI system should they prioritize?
Answer: D
Explanation:
The problem centers on unexpected rejections and potential bias in a high-stakes, regulated domain (lending). In such a context, the central tenet of Responsible AI is transparency and fairness.
While all options are valid goals, the priority when facing bias concerns and customer complaints due to rejection is to provide accountability and verify the fairness of the automated decision. This is achieved through Explainable AI (XAI).
Ensuring AI decision-making is explainable (B) means building mechanisms that allow developers, regulators, and affected customers to understand why a specific decision (rejection) was made. Explainability is crucial for:
Auditing for bias: If the reasons for rejection can be traced (e.g., system rejects based on loan-to-value ratio, not race), bias can be identified and corrected.
Compliance: Financial services are heavily regulated, and the ability to explain a lending decision is often a legal or regulatory requirement.
Customer Trust: Providing a clear reason for rejection (even if the news is bad) reduces complaints and fosters confidence, directly addressing the core issue of unexpected rejections.
Options A, C, and D address security, speed, and accuracy, respectively, but Explainability is the direct mechanism for proving fairness and ensuring accountability, making it the most critical priority in this scenario.
(Reference: Google's Responsible AI principles and training materials highlight that in high-stakes domains like finance, explainability is essential for establishing trust, identifying and mitigating bias, and meeting regulatory compliance.)
NEW QUESTION # 58
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