Google Generative-AI-Leader : Google Cloud Certified - Generative AI Leader Exam

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Google Generative-AI-Leader Exam Syllabus Topics:

Reference: https://cloud.google.com/developers/generative-ai-leader

Google Cloud Certified - Generative AI Leader Sample Questions:

Question #1
According to Google-recommended practices, when should generative AI be used to augment your strategic thought processes?

A. When tasks involve sensitive information or require human oversight, such as medical diagnosis or legal advice
B. When tasks require a high level of accuracy and precision, such as financial reporting or accounting.
C. When tasks involve critical thinking, creativity, and strategic decision-making, such as data analysis or content creation.
D. When tasks are repetitive and rule-based, such as generating text responses to customer service inquiries.

Question #2
An organization is increasingly concerned about the security of its sensitive business data as it begins to use generative AI applications that are hosted on Google Cloud. They want to ensure that the underlying infrastructure itself has robust security measures built in from the ground up to protect against potential threats and vulnerabilities. What Google Cloud security features directly address this need?

A. Google Cloud Observability
B. Security Command Center
C. Secure-by-design infrastructure
D. Identity and Access Management

Question #3
A company wants to use an AI agent to automate some tasks. They want everyone to understand the different functions of an AI agent. What is the function of an AI agent in the context of gen AI?

A. To store and manage large datasets used for training and running gen AI models.
B. To provide a user-friendly interface for interacting with gen AI models.
C. To provide the computational resources needed to train and run gen AI models.
D. To analyze situations, use multiple tools, and make informed decisions without requiring constant human input.

Question #4
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 However, the training dataset disproportionately includes feedback from a specific demographic and uses outdated language norms that don't reflect current customer communication styles.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?

A. Data dependency
B. Overfitting
C. Hallucination
D. Edge case

Question #5
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?

A. Decrease the output length of the summaries to make them more concise.
B. Implement stricter safety settings to filter out potentially misinterpreted content altogether.
C. Incorporate a human-in-the-loop (HITL) review process to refine the summaries.
D. Increase the temperature parameter of the model to encourage more varied and less literal interpretations.

Solutions: