Question 1

Your team is integrating a generative LLM into a financial analytics platform. The system must meet strict regulatory compliance standards, including data privacy and accuracy in financial forecasting. During system analysis, you find that the LLM occasionally uses customer data in ways that may not fully comply with data privacy regulations. Which action should you prioritize to ensure the system meets the required data privacy specifications?

Options :

A : Implement data anonymization techniques before feeding data into the LLM.

B : Rely on the LLM-s internal mechanisms to handle data privacy.

C : Reduce the amount of customer data used by the LLM to minimize risks.

D : Consult with the legal team after deployment to address any compliance issues that arise.

Answer: A

Question 2

You are developing a generative AI model to create personalized content recommendations for users. The user base is highly diverse, with varying preferences and behaviors. The model must balance relevance with the risk of over-personalization, which could lead to filter bubbles. Which strategy would most effectively address this challenge?

Options :

A : Increase the weight of recent user interactions to prioritize current preferences.

B : Use a collaborative filtering approach exclusively to generate recommendations.

C : Train separate models for each user segment based on demographic data.

D : Incorporate a mix of personalized and non-personalized content in recommendations.

Answer: D

Question 3

You are building a recommendation system for a large-scale e-commerce platform that needs to analyze customer behavior, purchase history, and browsing patterns in real-time to provide personalized recommendations. The data is coming from multiple sources, some of which are too large to fit into memory. Which approach would be most effective for handling this complex scenario?

Options :

A : Storing all incoming data in a relational database and querying it in real-time

B : Using a rule-based engine to analyze and recommend products based on predefined criteria

C : Using a streaming data processing framework like Apache Kafka combined with in-memory processing

D : Loading all data into memory and running a batch process to generate recommendations

Answer: C

Question 4

A production system running a generative AI model is experiencing memory leaks, leading to gradual system slowdowns and crashes. Which of the following strategies would most effectively help in diagnosing and resolving this issue?

Options :

A : Increase the system-s RAM to handle more data

B : Use profiling tools to identify and optimize memory usage in the model-s code

C : Retrain the model with a focus on memory efficiency

D : Reduce the batch size used during inference

Answer: B

Question 5

You are deploying a generative AI model on an edge device with limited computational resources. The model must generate responses in near real-time while ensuring data privacy. Which strategy would be the most effective for balancing performance, accuracy, and privacy?

Options :

A : Perform all computations on the cloud

B : Use federated learning without model optimization

C : Use model quantization and secure enclave processing

D : Deploy the full model with no optimization

Answer: C

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