Question 1

What is a key functionality of the background service built on Unity Catalog for managing data?

Options :

A : It allows for manual creation of dashboards and requires custom setup for each metric.

B : It provides a static report of metrics without real-time updates or customization options.

C : It performs incremental processing of data, calculates profile and drift metrics, auto-generates dashboards, and includes automatic PII detection.

D : It focuses exclusively on managing user access to data without processing or visualizing metrics.

Answer: C

Question 2

As a Gen AI engineer working on improving a search engine’s ranking algorithm, you want to evaluate the effectiveness of your new ranking model. Which metric would you use to assess how well the model ranks the most relevant documents higher, taking into account both the position of the documents and their relevance?

Options :

A : Normalized Discounted Cumulative Gain (NDCG)

B : Mean Reciprocal Rank (MRR)

C : Precision at k

D : F1 Score

Answer: A

Question 3

As a Generative AI engineer, you are responsible for developing and deploying a machine learning model that provides real-time predictions for a financial services application. The model must be monitored for performance, and you need to track its training metrics, serve it in production, and evaluate its accuracy over time. Which combination of Databricks products would best address these requirements?

Options :

A : Use MLflow Tracking to record and manage the model-s training metrics, Model Serving for deploying the model in production, and Lakehouse Monitoring to monitor the model-s performance and operational metrics.

B : Use Lakehouse Monitoring to evaluate the model-s accuracy, MLflow Tracking to deploy the model, and Model Serving to monitor the model-s performance in production.

C : Use MLflow Evaluation to track the model-s training process, MLflow Tracking for evaluating the model-s accuracy, and Model Serving to deploy the model.

D : Use MLflow Tracking to monitor the model-s real-time performance, MLflow Evaluation for tracking training metrics, and Lakehouse Monitoring to serve the model in production.

Answer: A

Question 4

As a Gen AI engineer tasked with selecting a deployment strategy for an email marketing system that generates customer segmentation reports daily based on accumulated data, which deployment strategy would be most appropriate given the need to process data on a regular schedule and write results to persistent storage?

Options :

A : Batch Deployment

B : Real-Time Deployment

C : Edge Deployment

D : Streaming Deployment

Answer: A

Question 5

In which of the following scenarios model learns using RAG?

Options :

A : Model Fine Tuning

B : None of these options

C : Model Pre-Training

D : Passing Contextual Information

Answer: D