Question 1

A Generative Al Engineer has already trained an LLM on Databricks and it is now ready to be deployed. Which of the following steps correctly outlines the easiest process for deploying a model on Databricks?

Options :

A :

Log the model as a pickle object, upload the object to Unity Catalog Volume, register it to Unity Catalog using MLflow, and start a serving endpoint

B :

Log the model using MLflow during training, directly register the model to Unity Catalog using the MLflow API, and start a serving endpoint

C :

Save the model along with its dependencies in a local directory, build the Docker image, and run the Docker container

D : Wrap the LLMs prediction function into a Flask application and serve using Gunicorn

Answer: B

Question 2

As a Gen AI Engineer, you have a task to decide if you need a vector database for your Q&A chat bot. In which of the following scenarios you should use a vector database?

Options :

A : When you need to handle large-scale data, perform fast semantic searches, and require persistent storage.

B : When you need to quickly prototype a solution with a smaller dataset that can be handled in-memory.

C : When you need to store and retrieve large amounts of structured data based on exact matches.

D : When you need to perform keyword-based searches on a relational database.

Answer: A

Question 3

As a Gen AI engineer managing the lifecycle of models in MLflow, which of the following URI patterns are used for referencing models in the Unity Catalog (MLflow) Model Registry? Choose two correct answers.

Options :

A : models:/{model_name}/latest

B : models:/{model_name}@{alias}

C : models:/{model_name}@{version}

D : models:/{model_name}/{model_version}

Answer: B,D

Question 4

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 5

Why summarizing retrieved documents before feeding them into an AI system is recommended?

Options :

A : To help the AI system to generate responses in multiple languages.

B : To eliminate irrelevant information, making the document shorter.

C : To allow the AI system to process multiple documents simultaneously.

D : To ensure the AI system stays within its context window and focuses on the most essential information.

Answer: D

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