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| Section | Objectives |
|---|---|
| Databricks Mosaic AI Platform | - MLflow for GenAI
|
| Retrieval-Augmented Generation (RAG) | - RAG architecture
|
| Foundations of Generative AI | - Large Language Models (LLMs)
|
| Prompt Engineering | - Prompt evaluation
|
| Responsible AI and Governance | - Model governance
|
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NEW QUESTION # 50
A Generative AI Engineer is using LangGraph to define multiple tools in a single agentic application. They want to enable the main orchestrator LLM to decide on its own which tools are most appropriate to call for a given prompt. To do this, they must determine the general flow of the code. Which sequence will do this?
Answer: B
Explanation:
In modern agentic frameworks like LangGraph or LangChain, the standard workflow for creating an autonomous tool-calling agent follows a specific sequence. First, tools must be defined (often as Python functions with clear docstrings, which the LLM uses to understand the tool ' s purpose). Second, the agent logic is defined, which specifies how the LLM should think. Third, the agent is initialized using a logic pattern like ReAct (Reason + Act). The ReAct framework is essential here because it enables the " orchestrator " loop: the LLM receives a prompt, generates a " Thought " about which tool to use, generates an
" Action " to call that tool, receives an " Observation " (the tool ' s output), and repeats until it can provide a final answer. Loading tools into " separate agents " (C) or defining tools " inside " agents (D) are non-standard patterns that add unnecessary complexity and do not align with the centralized orchestration model required for LangGraph.
NEW QUESTION # 51
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: A
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 # 52
A Generative AI Engineer is testing a simple prompt template in LangChain using the code below, but is getting an error.
Assuming the API key was properly defined, what change does the Generative AI Engineer need to make to fix their chain?
Answer: C
Explanation:
To fix the error in the LangChain code provided for using a simple prompt template, the correct approach is Option C. Here's a detailed breakdown of why Option C is the right choice and how it addresses the issue:
Proper Initialization: In Option C, the LLMChain is correctly initialized with the LLM instance specified as OpenAI(), which likely represents a language model (like GPT) from OpenAI. This is crucial as it specifies which model to use for generating responses.
Correct Use of Classes and Methods:
The PromptTemplate is defined with the correct format, specifying that adjective is a variable within the template. This allows dynamic insertion of values into the template when generating text.
The prompt variable is properly linked with the PromptTemplate, and the final template string is passed correctly.
The LLMChain correctly references the prompt and the initialized OpenAI() instance, ensuring that the template and the model are properly linked for generating output.
Why Other Options Are Incorrect:
Option A: Misuses the parameter passing in generate method by incorrectly structuring the dictionary.
Option B: Incorrectly uses prompt.format method which does not exist in the context of LLMChain and PromptTemplate configuration, resulting in potential errors.
Option D: Incorrect order and setup in the initialization parameters for LLMChain, which would likely lead to a failure in recognizing the correct configuration for prompt and LLM usage.
Thus, Option C is correct because it ensures that the LangChain components are correctly set up and integrated, adhering to proper syntax and logical flow required by LangChain's architecture. This setup avoids common pitfalls such as type errors or method misuses, which are evident in other options.
NEW QUESTION # 53
A Generative AI Engineer wants to build an LLM-based solution to help a restaurant improve its online customer experience with bookings by automatically handling common customer inquiries. The goal of the solution is to minimize escalations to human intervention and phone calls while maintaining a personalized interaction. To design the solution, the Generative AI Engineer needs to define the input data to the LLM and the task it should perform.
Which input/output pair will support their goal?
Answer: A
Explanation:
Context: The goal is to improve the online customer experience in a restaurant by handling common inquiries about bookings, minimizing escalations, and maintaining personalized interactions.
Explanation of Options:
* Option A: Grouping and summarizing chat logs by user could provide insights into customer interactions but does not directly address the task of handling booking inquiries or minimizing escalations.
* Option B: Using chat logs to generate interactive buttons for booking details directly supports the goal of facilitating online bookings, minimizing the need for human intervention by providing clear, interactive options for customers to self-serve.
* Option C: Classifying sentiment of customer reviews does not directly help with booking inquiries, although it might provide valuable feedback insights.
* Option D: Providing cancellation options is helpful but narrowly focuses on one aspect of the booking process and doesn't support the broader goal of handling common inquiries about bookings.
Option Bbest supports the goal of improving online interactions by using chat logs to generate actionable items for customers, helping them complete booking tasks efficiently and reducing the need for human intervention.
NEW QUESTION # 54
A team uses Mosaic AI Vector Search to retrieve documents for their Retrieval-Augmented Generation (RAG) pipeline. The search query returns five relevant documents, and the first three are added to the prompt as context. Performance evaluation with Agent Evaluation shows that some lower-ranked retrieved documents have higher context relevancy scores than higher-ranked documents. Which option should the team consider to optimize this workflow?
Answer: D
Explanation:
The scenario describes a common "retrieval gap" where the initial bi-encoder (embedding model) used for vector search identifies relevant documents but does not rank them perfectly. This happens because embedding models represent entire documents as a single vector, which can lose nuance. The standard engineering solution is to implement a Reranker (Cross-Encoder). Unlike embedding models, a reranker processes the query and a candidate document simultaneously, allowing it to capture deep semantic interactions between the two. In a Mosaic AI workflow, after the vector search retrieves the top $k$ documents, the reranker evaluates those specific $k$ documents to produce a more accurate relevance score. This ensures that the most contextually relevant documents are placed at the top of the list (and thus the top of the LLM prompt), which is crucial because LLMs are sensitive to document order and often prioritize information found at the beginning of the context.
NEW QUESTION # 55
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