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Databricks Exam Databricks-Generative-AI-Engineer-Associate Topic 3 Question 2 Discussion

Actual exam question for Databricks's Databricks-Generative-AI-Engineer-Associate exam
Question #: 2
Topic #: 3
[All Databricks-Generative-AI-Engineer-Associate Questions]

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?

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Suggested Answer: D

To build an LLM-powered system that accesses up-to-date news articles and stock prices, the best approach is to create an agent that has access to specific tools (option D).

Agent with SQL and Web Search Capabilities: By using an agent-based architecture, the LLM can interact with external tools. The agent can query Delta tables (for up-to-date stock prices) via SQL and perform web searches to retrieve the latest news articles. This modular approach ensures the system can access both structured (stock prices) and unstructured (news) data sources dynamically.

Why This Approach Works:

SQL Queries for Stock Prices: Delta tables store stock prices, which the agent can query directly for the latest data.

Web Search for News: For news articles, the agent can generate search queries and retrieve the most relevant and recent articles, then pass them to the LLM for processing.

Why Other Options Are Less Suitable:

A (Summarizing News for Stock Prices): This convoluted approach would not ensure accuracy when retrieving stock prices, which are already structured and stored in Delta tables.

B (Stock Price Volatility Queries): While this could retrieve relevant information, it doesn't address how to obtain the most up-to-date news articles.

C (Vector Store): Storing news articles and stock prices in a vector store might not capture the real-time nature of stock data and news updates, as it relies on pre-existing data rather than dynamic querying.

Thus, using an agent with access to both SQL for querying stock prices and web search for retrieving news articles is the best approach for ensuring up-to-date and accurate responses.


by Carin at Oct 22, 2024, 08:22 PM

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Della
1 months ago
I'm torn between options C and D. Both sound like they could work well, but I'm curious to see how the performance would compare.
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Vivan
5 days ago
Option C could be more efficient in terms of retrieval and generation.
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Christiane
1 months ago
Haha, I bet the Generative AI Engineer is hoping the LLM doesn't start buying and selling stocks on its own! Option D seems the way to go.
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Alton
1 months ago
Option B is interesting, using the LLM to investigate the causes of stock volatility. That could provide some valuable insights.
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Patria
17 days ago
C: It's definitely a unique way to leverage LLM technology for financial analysis.
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Veta
18 days ago
B: I agree, it could help identify the reasons behind the fluctuations.
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Rosio
1 months ago
A: Option B sounds like a smart approach to analyze stock volatility.
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Ciara
2 months ago
I like the idea of a RAG architecture in option C. Storing the data in a vector store and retrieving it on the fly could be really efficient.
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Selma
9 days ago
Definitely, having a well-structured architecture is key for smooth operation.
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Elliott
12 days ago
It's important to have a system that can quickly access the necessary data.
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Graciela
12 days ago
I agree, using a RAG architecture could streamline the process.
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Mona
13 days ago
Option C sounds like a solid choice for efficient data retrieval.
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Lavelle
2 months ago
I agree with Krissy. Option A seems like the most practical approach for the Generative AI Engineer to architect their LLM system.
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Laurel
2 months ago
Option D seems like the most comprehensive approach. Using an agent with specific tools to handle the data retrieval and then feeding that to the LLM for generation sounds like a solid architecture.
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Mozell
15 days ago
C) Download and store news articles and stock price information in a vector store. Use a RAG architecture to retrieve and generate at runtime.
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Reuben
17 days ago
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.
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Lizbeth
25 days ago
A) Use an LLM to summarize the latest news articles and lookup stock tickers from the summaries to find stock prices.
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Latanya
1 months ago
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.
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Krissy
2 months ago
I think option A is the best choice because it allows the LLM to summarize news articles and find stock prices efficiently.
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