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Databricks Databricks-Generative-AI-Engineer-Associate 考試大綱：

主題	簡介
主題 1	Governance: Generative AI Engineers who take the exam get knowledge about masking techniques, guardrail techniques, and legal licensing requirements in this topic.
主題 2	Design Applications: The topic focuses on designing a prompt that elicits a specifically formatted response. It also focuses on selecting model tasks to accomplish a given business requirement. Lastly, the topic covers chain components for a desired model input and output.
主題 3	Application Development: In this topic, Generative AI Engineers learn about tools needed to extract data, Langchain similar tools, and assessing responses to identify common issues. Moreover, the topic includes questions about adjusting an LLM's response, LLM guardrails, and the best LLM based on the attributes of the application.
主題 4	Evaluation and Monitoring: This topic is all about selecting an LLM choice and key metrics. Moreover, Generative AI Engineers learn about evaluating model performance. Lastly, the topic includes sub-topics about inference logging and usage of Databricks features.

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最新的 Generative AI Engineer Databricks-Generative-AI-Engineer-Associate 免費考試真題 (Q81-Q86):

問題 #81
A Generative AI Engineer received the following business requirements for an external chatbot.
The chatbot needs to know what types of questions the user asks and routes to appropriate models to answer the questions. For example, the user might ask about upcoming event details. Another user might ask about purchasing tickets for a particular event.
What is an ideal workflow for such a chatbot?

A. The chatbot should be implemented as a multi-step LLM workflow. First, identify the type of question asked, then route the question to the appropriate model. If it's an upcoming event question, send the query to a text-to-SQL model. If it's about ticket purchasing, the customer should be redirected to a payment platform.
B. There should be two different chatbots handling different types of user queries.
C. The chatbot should only look at previous event information
D. The chatbot should only process payments

答案：A

解題說明：
* Problem Context: The chatbot must handle various types of queries and intelligently route them to the appropriate responses or systems.
* Explanation of Options:
* Option A: Limiting the chatbot to only previous event information restricts its utility and does not meet the broader business requirements.
* Option B: Having two separate chatbots could unnecessarily complicate user interaction and increase maintenance overhead.
* Option C: Implementing a multi-step workflow where the chatbot first identifies the type of question and then routes it accordingly is the most efficient and scalable solution. This approach allows the chatbot to handle a variety of queries dynamically, improving user experience and operational efficiency.
* Option D: Focusing solely on payments would not satisfy all the specified user interaction needs, such as inquiring about event details.
Option Coffers a comprehensive workflow that maximizes the chatbot's utility and responsiveness to different user needs, aligning perfectly with the business requirements.

問題 #82
Which TWO chain components are required for building a basic LLM-enabled chat application that includes conversational capabilities, knowledge retrieval, and contextual memory?

A. (Q)
B. External tools
C. React Components
D. Vector Stores
E. Conversation Buffer Memory
F. Chat loaders

答案：D,E

解題說明：
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.

問題 #83
When developing an LLM application, it's crucial to ensure that the data used for training the model complies with licensing requirements to avoid legal risks.
Which action is NOT appropriate to avoid legal risks?

A. Reach out to the data curators directly after you have started using the trained model to let them know.
B. Reach out to the data curators directly before you have started using the trained model to let them know.
C. Use any available data you personally created which is completely original and you can decide what license to use.
D. Only use data explicitly labeled with an open license and ensure the license terms are followed.

答案：A

解題說明：
* 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.

問題 #84
A Generative AI Engineer is deploying an agent using Mosaic AI Model Serving. The agent needs to access various Databricks resources, including Vector Search, Databricks SQL, and Functions. They need to find the easiest and best-practice way to authenticate the deployed agent to access these resources.
What approach should they choose?

A. Set appropriate permissions on the Model Serving endpoint for the agent, as these permissions will be used when connecting to other resources.
B. Log the authentication token while logging the agent; this token will be automatically used for authentication.
C. Define resource dependencies while logging the agent and deploy it with the Agent Framework.
D. Embed authentication credentials within the agent's code to access the required resources.

答案：C

問題 #85
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.)

A. Reduce the maximum output tokens of the new model
B. Decrease the chunk size of embedded documents
C. Retrain the response generating model using ALiBi
D. Use a smaller embedding model to generate
E. Reduce the number of records retrieved from the vector database

答案：B,E

解題說明：
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.
Options C and D are 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.

問題 #86
......

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