Question 1

As a Gen AI engineer utilizing MLflow for managing your model lifecycle, which feature of MLflow is most beneficial for handling both traditional machine learning and Gen AI workflows effectively?

Options :

A : MLflow provides a detailed user manual for each model flavor, which includes extensive theoretical explanations and guidelines for using different types of models.

B : MLflow’s unified platform allows you to manage end-to-end ML and GenAI workflows, including deployment, monitoring, and evaluation, from development to production.

C : MLflow exclusively supports GenAI-specific model flavors and evaluation metrics, without compatibility for traditional machine learning models.

D : MLflow focuses solely on managing model training and does not offer tools for model deployment or evaluation, requiring additional external tools for these tasks.

Answer: B

Question 2

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

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 Gen AI engineer, you need to design a prompt for a model to extract and format customer data from a support ticket into JSON. Which of the following prompts will best ensure that the model outputs the data in the correct JSON format?

Options :

A : Extract the Customer Name, Issue Type, and Ticket ID from the support ticket and provide the results in JSON format.

B : Extract the following fields from the support ticket and format them as JSON: Customer Name, Issue Type, Ticket ID. Example output: {"Customer Name": "Alice Smith", "Issue Type": "Technical Support", "Ticket ID": "12345"}.

C : Provide a summary of the customer’s support ticket in JSON format.

Answer: B

Question 5

How can a Generative AI Engineer handle complex documents (with mixed text and images) for data extraction and chunking for a RAG based LLM application?

Options :

A : Using python libraries like PyPDF

B : Using Multi-modal models like OpenAI-s GPT-4o and beyond

C : All of these options

D : Using layout model from Hugging Face

Answer: C