Question 1

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 2

What is a key challenge when implementing reranking in a Retrieval-Augmented Generation (RAG) pipeline for a Gen AI application?

Options :

A : Reranking simplifies the RAG pipeline by reducing the overall complexity and streamlining the process.

B : Reranking requires minimal computational resources, making it an efficient process for large datasets.

C : Reranking eliminates the need for any further processing or adjustments to the retrieved documents before generating a response.

D : Reranking involves multiple calls to the LLM, which can significantly increase both the cost and latency of the RAG chain.

Answer: D

Question 3

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

Question 4

Which of the following features is offered by Databricks Model Serving for AI models?

Options :

A : It does not offer any monitoring or payload logging features for external models like OpenAI or Anthropic.

B : It requires separate tools and interfaces for managing foundation models and external models.

C : It only supports models built with MLflow, limiting the integration of external models.

D : It provides a unified UI, API, and SDK for managing custom models, foundation models, and external models, ensuring seamless governance, monitoring, and logging.

Answer: D

Question 5

Which benefits of Databricks Model Serving contribute to improving efficiency in managing machine learning models in production?

Options :

A : Reduce operational costs by leveraging serverless computing.

B : Streamline the ML lifecycle, enabling data science teams to focus on core tasks.

C : Support for a wide range of models, including custom, foundational, and external models.

D : Built-in A/B testing for model performance optimization.

E : All of these options.

Answer: E

Free Databricks-Certified-Generative-AI-Engineer-Associate Mock Exam – Practice Online Confidently

Buying Options: