Databricks-Generative-AI-Engineer-Associate - Databricks Certified Generative AI Engineer Associate

Problem Context: When using data to train a model, it’s essential to ensure compliance with licensing to avoid legal risks. Legal issues can arise from using data without permission, especially when it comes from third-party sources.

Explanation of Options:

Option A: Reaching out to data curatorsbeforeusing the data is an appropriate action. This allows you to ensure you have permission or understand the licensing terms before starting to use the data in your model.

Option B: Usingoriginal datathat you personally created is always a safe option. Since you have full ownership over the data, there are no legal risks, as you control the licensing.

Option C: Using data that is explicitly labeled with an open license and adhering to the license terms is a correct and recommended approach. This ensures compliance with legal requirements.

Option D: Reaching out to the data curatorsafteryou have already started using the trained model isnot appropriate. If you’ve already used the data without understanding its licensing terms, you may have already violated the terms of use, which could lead to legal complications. It’s essential to clarify the licensing termsbeforeusing the data, not after.

Thus,Option Dis not appropriate because it could expose you to legal risks by using the data without first obtaining the proper licensing permissions.

In this scenario, the Generative AI Engineer needs to design a system that can handle different types of queries about the monster truck team. The queries may involve text-based information, API lookups for event dates, or table queries for standings. The best solution is to implement atool-based agent system.

Here’s how option B works, and why it’s the most appropriate answer:

System Design Using Agent-Based Model:In modern agent-based LLM systems, you can design a system where the LLM (Large Language Model) acts as a central orchestrator. The model can "decide" which tools to use based on the query. These tools can include API calls, table lookups, or natural language searches. The system should contain asystem promptthat informs the LLM about the available tools.

System Prompt Listing Tools:By creating a well-craftedsystem prompt, the LLM knows which tools are at its disposal. For instance, one tool may query an external API for event dates, another might look up standings in a database, and a third may involve searching a vector database for general text-based information. Theagentwill be responsible for calling the appropriate tool depending on the query.

Agent Orchestration of Calls:The agent system is designed to execute a series of steps based on the incoming query. If a user asks for the next event date, the system will recognize this as a task that requires an API call. If the user asks about standings, the agent might query the appropriate table in the database. For text-based questions, it may call a search function over ingested data. The agent orchestrates this entire process, ensuring the LLM makes calls to the right resources dynamically.

Generative AI Tools and Context:This is a standard architecture for integrating multiple functionalities into a system where each query requires different actions. The core design in option B is efficient because it keeps the system modular and dynamic by leveraging tools rather than overloading the LLM with static information in a system prompt (like option D).

Why Other Options Are Less Suitable:

A (RAG Architecture): While relevant, simply ingesting PDFs into a vector store only helps with text-based retrieval. It wouldn’t help with API lookups or table queries.

C (Conditional Logic with RAG/API/TABLE): Although this approach works, it relies heavily on manual text parsing and might introduce complexity when scaling the system.

D (System Prompt with Event Dates and Standings): Hardcoding dates and table information into a system prompt isn’t scalable. As the standings or events change, the system would need constant updating, making it inefficient.

By bundling multiple tools into a single agent-based system (as in option B), the Generative AI Engineer can best handle the diverse requirements of this system.

Problem Context: The Generative AI Engineer needs a tool to build amulti-step LLM-based workflow. This type of workflow often involves chaining multiple steps together, such as query generation, retrieval of information, response generation, and post-processing, with LLMs integrated at several points.

Explanation of Options:

Option A: Pandas: Pandas is a powerful data manipulation library for structured data analysis, but it is not designed for managing or orchestrating multi-step workflows, especially those involving LLMs.

Option B: TensorFlow: TensorFlow is primarily used for training and deploying machine learning models, especially deep learning models. It is not designed for orchestrating multi-step tasks in LLM-based workflows.

Option C: PySpark: PySpark is a distributed computing framework used for large-scale data processing. While useful for handling big data, it is not specialized for chaining LLM-based operations.

Option D: LangChain: LangChain is a purpose-built framework designed specifically fororchestrating multi-step workflowswith large language models (LLMs). It enables developers to easily chain different tasks, such as retrieving documents, summarizing information, and generating responses, all in a structured flow. This makes it the best tool for building complex LLM-based workflows.

Thus,LangChainis the most suitable library for creating multi-step LLM-based workflows.