Databricks Generative AI Engineer Associate Question 3
Select 3You are tasked with designing a generative AI application on Databricks that will generate personalized product recommendations based on user preferences and purchase history. Which critical considerations should you prioritize during the design phase to ensure scalability, accuracy, and responsible AI deployment?
- A
Ensure the training dataset includes diverse user demographics to minimize bias in recommendations.
- B
Design the application to leverage only pre-trained models without fine-tuning to save on computational resources.
- C
Implement monitoring mechanisms to detect and address data drift in user preferences over time.
- D
Incorporate explainability techniques to provide insights into why specific recommendations are generated.
- E
Focus solely on optimizing latency and throughput while ignoring ethical AI principles.
Show answer and explanation
Correct answers: A, C, D
Explanation
Designing a generative AI application requires balancing performance, accuracy, and ethical considerations. Prioritizing diverse data, monitoring for data drift, and incorporating explainability ensures the application remains fair, scalable, and trustworthy. Ignoring these principles or relying solely on pre-trained models without fine-tuning can lead to suboptimal outcomes.
- A. Correct.
Including diverse user demographics in the training dataset is critical to minimizing bias and ensuring fairness in personalized recommendations.
- B. Incorrect.
Using only pre-trained models without fine-tuning may reduce computational costs, but it could result in suboptimal accuracy for the specific use case, especially when user preferences are domain-specific.
- C. Correct.
Monitoring for data drift is essential for maintaining model accuracy over time, as user preferences and purchase patterns can evolve.
- D. Correct.
Explainability is important for responsible AI deployment, as it helps build trust with users by providing transparency into the recommendation process.
- E. Incorrect.
While optimizing latency and throughput is important for performance, ignoring ethical AI principles can lead to biased or harmful recommendations, undermining the application's reliability and user trust.