Question 1

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 2

A Generative AI Engineer is creating an LLM-powered application that will need access to up-to-date news articles and stock prices. The design requires the use of stock prices which are stored in Delta tables and finding the latest relevant news articles by searching the internet. How should the Generative AI Engineer architect their LLM system?

Options :

A :

Use an LLM to summarize the latest news articles and lookup stock tickers from the summaries to find stock prices.

B :

Query the Delta table for volatile stock prices and use an LLM to generate a search query to investigate potential causes of the stock volatility.

C :

Download and store news articles and stock price information in a vector store. Use a RAG architecture to retrieve and generate at runtime

D :

Create an agent with tools for SQL querying of Delta tables and web searching, provide retrieved values to an LLM for generation of response.

Answer: D

Question 3

As a Gen AI engineer developing an intelligent system that integrates various external tools and APIs, which statement best describes the role of agents?

Options :

A : Agents only handle coding tasks and do not interact with other types of tools.

B : Agents rely solely on search tools, like web browsing and search engines, for all tasks.

C : Agents use pre-configured tools without considering the context of the task.

D : "Agents' reasoning skills decide which tools to use and when/how to use them.

Answer: D

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

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

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