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NEW QUESTION # 11
A Generative AI Engineer at a legal firm is designing a RAG system to analyze historical legal cases. The system needs to process millions of court opinions and legal documents, already organized by time and topic, to track how interpretations of specific laws have evolved over time. All of these documents are in plain-text.
The engineer needs to choose a chunking method that would most effectively preserve continuity and the temporal nature of the cases. Which method do they choose?
Answer: B
Explanation:
In the context of legal document analysis where the " evolution of interpretation " is the primary goal, preserving narrative continuity is paramount. Windowed summarization with overlapping chunks is the most effective method for this use case. Overlapping (e.g., 10-15% of the chunk size) ensures that sentences or concepts split at the boundary of one chunk are preserved in the next, preventing the loss of critical context that often occurs in legal jargon. Furthermore, windowed summarization allows the system to condense long- form court opinions into manageable parts while maintaining the chronological " thread " of the argument.
While sentence-level embeddings with metadata (D) are useful for filtering, they often lack the sufficient context required to understand the nuances of a legal ruling. A windowed approach provides the LLM with enough surrounding text to understand the " why " behind a legal evolution, rather than just the " when. "
NEW QUESTION # 12
A Generative Al Engineer is helping a cinema extend its website's chat bot to be able to respond to questions about specific showtimes for movies currently playing at their local theater. They already have the location of the user provided by location services to their agent, and a Delta table which is continually updated with the latest showtime information by location. They want to implement this new capability In their RAG application.
Which option will do this with the least effort and in the most performant way?
Answer: B
Explanation:
The task is to extend a cinema chatbot to provide movie showtime information using a RAG application, leveraging user location and a continuously updated Delta table, with minimal effort and high performance.
Let's evaluate the options.
* Option A: Create a Feature Serving Endpoint from a FeatureSpec that references an online store synced from the Delta table. Query the Feature Serving Endpoint as part of the agent logic / tool implementation
* Databricks Feature Serving provides low-latency access to real-time data from Delta tables via an online store. Syncing the Delta table to a Feature Serving Endpoint allows the chatbot to query showtimes efficiently, integrating seamlessly into the RAG agent'stool logic. This leverages Databricks' native infrastructure, minimizing effort and ensuring performance.
* Databricks Reference:"Feature Serving Endpoints provide real-time access to Delta table data with low latency, ideal for production systems"("Databricks Feature Engineering Guide," 2023).
* Option B: Query the Delta table directly via a SQL query constructed from the user's input using a text-to-SQL LLM in the agent logic / tool
* Using a text-to-SQL LLM to generate queries adds complexity (e.g., ensuring accurate SQL generation) and latency (LLM inference + SQL execution). While feasible, it's less performant and requires more effort than a pre-built serving solution.
* Databricks Reference:"Direct SQL queries are flexible but may introduce overhead in real-time applications"("Building LLM Applications with Databricks").
* Option C: Write the Delta table contents to a text column, then embed those texts using an embedding model and store these in the vector index. Look up the information based on the embedding as part of the agent logic / tool implementation
* Converting structured Delta table data (e.g., showtimes) into text, embedding it, and using vector search is inefficient for structured lookups. It's effort-intensive (preprocessing, embedding) and less precise than direct queries, undermining performance.
* Databricks Reference:"Vector search excels for unstructured data, not structured tabular lookups"("Databricks Vector Search Documentation").
* Option D: Set up a task in Databricks Workflows to write the information in the Delta table periodically to an external database such as MySQL and query the information from there as part of the agent logic / tool implementation
* Exporting to an external database (e.g., MySQL) adds setup effort (workflow, external DB management) and latency (periodic updates vs. real-time). It's less performant and more complex than using Databricks' native tools.
* Databricks Reference:"Avoid external systems when Delta tables provide real-time data natively"("Databricks Workflows Guide").
Conclusion: Option A minimizes effort by using Databricks Feature Serving for real-time, low-latency access to the Delta table, ensuring high performance in a production-ready RAG chatbot.
NEW QUESTION # 13
After changing the response generating LLM in a RAG pipeline from GPT-4 to a model with a shorter context length that the company self-hosts, the Generative AI Engineer is getting the following error:
What TWO solutions should the Generative AI Engineer implement without changing the response generating model? (Choose two.)
Answer: A,E
Explanation:
* Problem Context: After switching to a model with a shorter context length, the error message indicating that the prompt token count has exceeded the limit suggests that the input to the model is too large.
* Explanation of Options:
* Option A: Use a smaller embedding model to generate- This wouldn't necessarily address the issue of prompt size exceeding the model's token limit.
* Option B: Reduce the maximum output tokens of the new model- This option affects the output length, not the size of the input being too large.
* Option C: Decrease the chunk size of embedded documents- This would help reduce the size of each document chunk fed into the model, ensuring that the input remains within the model's context length limitations.
* Option D: Reduce the number of records retrieved from the vector database- By retrieving fewer records, the total input size to the model can be managed more effectively, keeping it within the allowable token limits.
* Option E: Retrain the response generating model using ALiBi- Retraining the model is contrary to the stipulation not to change the response generating model.
OptionsCandDare the most effective solutions to manage the model's shorter context length without changing the model itself, by adjusting the input size both in terms of individual document size and total documents retrieved.
NEW QUESTION # 14
Which TWO chain components are required for building a basic LLM-enabled chat application that includes conversational capabilities, knowledge retrieval, and contextual memory?
Answer: D,F
Explanation:
Building a basic LLM-enabled chat application with conversational capabilities, knowledge retrieval, and contextual memory requires specific components that work together to process queries, maintain context, and retrieve relevant information. Databricks' Generative AI Engineer documentation outlines key components for such systems, particularly in the context of frameworks like LangChain or Databricks' MosaicML integrations. Let's evaluate the required components:
* Understanding the Requirements :
* Conversational capabilities : The app must generate natural, coherent responses.
* Knowledge retrieval : It must access external or domain-specific knowledge.
* Contextual memory : It must remember prior interactions in the conversation.
* Databricks Reference: " A typical LLM chat application includes a memory component to track conversation history and a retrieval mechanism to incorporate external knowledge " ( " Databricks Generative AI Cookbook, " 2023).
* Evaluating the Options :
* A. (Q) : This appears incomplete or unclear (possibly a typo). Without further context, it's not a valid component.
* B. Vector Stores : These store embeddings of documents or knowledge bases, enabling semantic search and retrieval of relevant information for the LLM. This is critical for knowledge retrieval in a chat application.
* Databricks Reference : " Vector stores, such as those integrated with Databricks' Lakehouse, enable efficient retrieval of contextual data for LLMs " ( " Building LLM Applications with Databricks " ).
* C. Conversation Buffer Memory : This component stores the conversation history, allowing the LLM to maintain context across multiple turns. It's essential for contextual memory.
* Databricks Reference : " Conversation Buffer Memory tracks prior user inputs and LLM outputs, ensuring context-aware responses " ( " Generative AI Engineer Guide " ).
* D. External tools : These (e.g., APIs or calculators) enhance functionality but aren't required for a basic chat app with the specified capabilities.
* E. Chat loaders : These might refer to data loaders for chat logs, but they're not a core chain component for conversational functionality or memory.
* F. React Components : These relate to front-end UI development, not the LLM chain's backend functionality.
* Selecting the Two Required Components :
* For knowledge retrieval , Vector Stores (B) are necessary to fetch relevant external data, a cornerstone of Databricks' RAG-based chat systems.
* For contextual memory , Conversation Buffer Memory (C) is required to maintain conversation history, ensuring coherent and context-aware responses.
* While an LLM itself is implied as the core generator, the question asks for chain components beyond the model, making B and C the minimal yet sufficient pair for a basic application.
Conclusion : The two required chain components are B. Vector Stores and C. Conversation Buffer Memory
, as they directly address knowledge retrieval and contextual memory, respectively, aligning with Databricks' documented best practices for LLM-enabled chat applications.
NEW QUESTION # 15
A Generative AI Engineer is building a RAG application that will rely on context retrieved from source documents that are currently in PDF format. These PDFs can contain both text and images. They want to develop a solution using the least amount of lines of code.
Which Python package should be used to extract the text from the source documents?
Answer: B
Explanation:
* Problem Context: The engineer needs to extract text from PDF documents, which may contain both text and images. The goal is to find a Python package that simplifies this task using the least amount of code.
* Explanation of Options:
* Option A: flask: Flask is a web framework for Python, not suitable for processing or extracting content from PDFs.
* Option B: beautifulsoup: Beautiful Soup is designed for parsing HTML and XML documents, not PDFs.
* Option C: unstructured: This Python package is specifically designed to work with unstructured data, including extracting text from PDFs. It provides functionalities to handle various types of content in documents with minimal coding, making it ideal for the task.
* Option D: numpy: Numpy is a powerful library for numerical computing in Python and does not provide any tools for text extraction from PDFs.
Given the requirement,Option C(unstructured) is the most appropriate as it directly addresses the need to efficiently extract text from PDF documents with minimal code.
NEW QUESTION # 16
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