Google Generative AI Leader Exam Questions

Question 6 of 75

A highly regulated financial institution wants to use Gemini as the core decision engine for a loan approval system that will deterministically approve or reject loan applications based on a strict set of predefined criteria. Why is this an inappropriate use case for Gemini?

A.

Gemini deployment for this scenario would be too expensive and complex.

B.

Gemini’s designed for flexible content generation and inference, not rigid rule-based decisions.

C.

Gemini is not equipped to handle structured numerical data for financial assessments.

D.

Gemini cannot integrate with required financial databases.

Question 7 of 75

What is an example of unsupervised machine learning?

A.

Predicting subscription renewal based on past renewal status data.

B.

Training a system to recognize product images using labeled categories.

C.

Analyzing customer purchase patterns to identify natural groupings.

D.

Forecasting sales figures using historical sales and marketing spend.

Question 8 of 75

A user asks a generative AI model about the scientific accuracy of a popular science fiction movie. The model confidently states that humans can indeed travel faster than light, referencing specific but entirely fictional theories and providing made-up explanations of how this is achieved according to the movie's "established science." The model presents this information as factual, without indicating that it originates from a fictional work. What type of model limitation is this?

A.

Bias

B.

Hallucination

C.

Knowledge cutoff

D.

Data dependency

Question 9 of 75

What does a diffusion model do?

A.

Generates high-quality content by refining noise into structured data.

B.

Facilitates the storage and management of structured data.

C.

Analyzes data and predicts future trends and patterns.

D.

Optimizes business processes and resource allocation.

Question 10 of 75

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.

Applying the latest software patches to the AI model on a regular basis.

B.

Monitoring the AI model’s performance for unexpected outputs and potential errors.

C.

Establishing ethical guidelines for AI model responses to ensure fairness and avoid harm.

D.

Implementing access controls and protecting sensitive information within the training data.