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Databricks Databricks-Generative-AI-Engineer-Associate Exam Syllabus Topics:

Topic	Details
Topic 1	Governance: Generative AI Engineers who take the exam get knowledge about masking techniques, guardrail techniques, and legal licensing requirements in this topic.
Topic 2	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.
Topic 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.

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Databricks Certified Generative AI Engineer Associate Sample Questions (Q50-Q55):

NEW QUESTION # 50
A Generative Al Engineer is developing a RAG system for their company to perform internal document Q&A for structured HR policies, but the answers returned are frequently incomplete and unstructured It seems that the retriever is not returning all relevant context The Generative Al Engineer has experimented with different embedding and response generating LLMs but that did not improve results.
Which TWO options could be used to improve the response quality?
Choose 2 answers

A. Add the section header as a prefix to chunks
B. Increase the document chunk size
C. Fine tune the response generation model
D. Use a larger embedding model
E. Split the document by sentence

Answer: A,B

Explanation:
The problem describes a Retrieval-Augmented Generation (RAG) system for HR policy Q&A where responses are incomplete and unstructured due to the retriever failing to return sufficient context. The engineer has already tried different embedding and response-generating LLMs without success, suggesting the issue lies in the retrieval process-specifically, how documents are chunked and indexed. Let's evaluate the options.
* Option A: Add the section header as a prefix to chunks
* Adding section headers provides additional context to each chunk, helping the retriever understand the chunk's relevance within the document structure (e.g., "Leave Policy: Annual Leave" vs. just "Annual Leave"). This can improve retrieval precision for structured HR policies.
* Databricks Reference:"Metadata, such as section headers, can be appended to chunks to enhance retrieval accuracy in RAG systems"("Databricks Generative AI Cookbook," 2023).
* Option B: Increase the document chunk size
* Larger chunks include more context per retrieval, reducing the chance of missing relevant information split across smaller chunks. For structured HR policies, this can ensure entire sections or rules are retrieved together.
* Databricks Reference:"Increasing chunk size can improve context completeness, though it may trade off with retrieval specificity"("Building LLM Applications with Databricks").
* Option C: Split the document by sentence
* Splitting by sentence creates very small chunks, which could exacerbate the problem by fragmenting context further. This is likely why the current system fails-it retrieves incomplete snippets rather than cohesive policy sections.
* Databricks Reference: No specific extract opposes this, but the emphasis on context completeness in RAG suggests smaller chunks worsen incomplete responses.
* Option D: Use a larger embedding model
* A larger embedding model might improve vector quality, but the question states that experimenting with different embedding models didn't help. This suggests the issue isn't embedding quality but rather chunking/retrieval strategy.
* Databricks Reference: Embedding models are critical, but not the focus when retrieval context is the bottleneck.
* Option E: Fine tune the response generation model
* Fine-tuning the LLM could improve response coherence, but if the retriever doesn't provide complete context, the LLM can't generate full answers. The root issue is retrieval, not generation.
* Databricks Reference: Fine-tuning is recommended for domain-specific generation, not retrieval fixes ("Generative AI Engineer Guide").
Conclusion: Options A and B address the retrieval issue directly by enhancing chunk context-either through metadata (A) or size (B)-aligning with Databricks' RAG optimization strategies. C would worsen the problem, while D and E don't target the root cause given prior experimentation.

NEW QUESTION # 51
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: D

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 # 52
A Generative Al Engineer is working with a retail company that wants to enhance its customer experience by automatically handling common customer inquiries. They are working on an LLM-powered Al solution that should improve response times while maintaining a personalized interaction. They want to define the appropriate input and LLM task to do this.
Which input/output pair will do this?

A. Input: Customer service chat logs; Output Group the chat logs by users, followed by summarizing each user's interactions, then respond
B. Input: Customer reviews; Output Group the reviews by users and aggregate per-user average rating, then respond
C. Input: Customer reviews: Output Classify review sentiment
D. Input: Customer service chat logs; Output: Find the answers to similar questions and respond with a summary

Answer: D

Explanation:
The task described in the question involves enhancing customer experience by automatically handling common customer inquiries using an LLM-powered AI solution. This requires the system to process input data (customer inquiries) and generate personalized, relevant responses efficiently. Let's evaluate the options step-by-step in the context of Databricks Generative AI Engineer principles, which emphasize leveraging LLMs for tasks like question answering, summarization, and retrieval-augmented generation (RAG).
* Option A: Input: Customer reviews; Output: Group the reviews by users and aggregate per-user average rating, then respond
* This option focuses on analyzing customer reviews to compute average ratings per user. While this might be useful for sentiment analysis or user profiling, it does not directly address the goal of handling common customer inquiries or improving response times for personalized interactions. Customer reviews are typically feedback data, not real-time inquiries requiring immediate responses.
* Databricks Reference: Databricks documentation on LLMs (e.g., "Building LLM Applications with Databricks") emphasizes that LLMs excel at tasks like question answering and conversational responses, not just aggregation or statistical analysis of reviews.
* Option B: Input: Customer service chat logs; Output: Group the chat logs by users, followed by summarizing each user's interactions, then respond
* This option uses chat logs as input, which aligns with customer service scenarios. However, the output-grouping by users and summarizing interactions-focuses on user-specific summaries rather than directly addressing inquiries. While summarization is an LLM capability, this approach lacks the specificity of finding answers to common questions, which is central to the problem.
* Databricks Reference: Per Databricks' "Generative AI Cookbook," LLMs can summarize text, but for customer service, the emphasis is on retrieval and response generation (e.g., RAG workflows) rather than user interaction summaries alone.
* Option C: Input: Customer service chat logs; Output: Find the answers to similar questions and respond with a summary
* This option uses chat logs (real customer inquiries) as input and tasks the LLM with identifying answers to similar questions, then providing a summarized response. This directly aligns with the goal of handling common inquiries efficiently while maintaining personalization (by referencing past interactions or similar cases). It leverages LLM capabilities like semantic search, retrieval, and response generation, which are core to Databricks' LLM workflows.
* Databricks Reference: From Databricks documentation ("Building LLM-Powered Applications," 2023), an exact extract states:"For customer support use cases, LLMs can be used to retrieve relevant answers from historical data like chat logs and generate concise, contextually appropriate responses."This matches Option C's approach of finding answers and summarizing them.
* Option D: Input: Customer reviews; Output: Classify review sentiment
* This option focuses on sentiment classification of reviews, which is a valid LLM task but unrelated to handling customer inquiries or improving response times in a conversational context.
It's more suited for feedback analysis than real-time customer service.
* Databricks Reference: Databricks' "Generative AI Engineer Guide" notes that sentiment analysis is a common LLM task, but it's not highlighted for real-time conversational applications like customer support.
Conclusion: Option C is the best fit because it uses relevant input (chat logs) and defines an LLM task (finding answers and summarizing) that meets the requirements of improving response times and maintaining personalized interaction. This aligns with Databricks' recommended practices for LLM-powered customer service solutions, such as retrieval-augmented generation (RAG) workflows.

NEW QUESTION # 53
A Generative AI Engineer is developing a chatbot designed to assist users with insurance-related queries. The chatbot is built on a large language model (LLM) and is conversational. However, to maintain the chatbot's focus and to comply with company policy, it must not provide responses to questions about politics. Instead, when presented with political inquiries, the chatbot should respond with a standard message:
"Sorry, I cannot answer that. I am a chatbot that can only answer questions around insurance." Which framework type should be implemented to solve this?

A. Contextual Guardrail
B. Compliance Guardrail
C. Security Guardrail
D. Safety Guardrail

Answer: D

Explanation:
In this scenario, the chatbot must avoid answering political questions and instead provide a standard message for such inquiries. Implementing aSafety Guardrailis the appropriate solution for this:
* What is a Safety Guardrail?Safety guardrails are mechanisms implemented in Generative AI systems to ensure the model behaves within specific bounds. In this case, it ensures the chatbot does not answer politically sensitive or irrelevant questions, which aligns with the business rules.
* Preventing Responses to Political Questions:The Safety Guardrail is programmed to detect specific types of inquiries (like political questions) and prevent the model from generating responses outside its intended domain. When such queries are detected, the guardrail intervenes and provides a pre-defined response: "Sorry, I cannot answer that. I am a chatbot that can only answer questions around insurance."
* How It Works in Practice:The LLM system can include aclassification layeror trigger rules based on specific keywords related to politics. When such terms are detected, the Safety Guardrail blocks the normal generation flow and responds with the fixed message.
* Why Other Options Are Less Suitable:
* B (Security Guardrail): This is more focused on protecting the system from security vulnerabilities or data breaches, not controlling the conversational focus.
* C (Contextual Guardrail): While context guardrails can limit responses based on context, safety guardrails are specifically about ensuring the chatbot stays within a safe conversational scope.
* D (Compliance Guardrail): Compliance guardrails are often related to legal and regulatory adherence, which is not directly relevant here.
Therefore, aSafety Guardrailis the right framework to ensure the chatbot only answers insurance-related queries and avoids political discussions.

NEW QUESTION # 54
A Generative AI Engineer is designing an LLM-powered live sports commentary platform. The platform provides real-time updates and LLM-generated analyses for any users who would like to have live summaries, rather than reading a series of potentially outdated news articles.
Which tool below will give the platform access to real-time data for generating game analyses based on the latest game scores?

A. AutoML
B. DatabrickslQ
C. Foundation Model APIs
D. Feature Serving

Answer: D

Explanation:
* Problem Context: The engineer is developing an LLM-powered live sports commentary platform that needs to provide real-time updates and analyses based on the latest game scores. The critical requirement here is the capability to access and integrate real-time data efficiently with the platform for immediate analysis and reporting.
* Explanation of Options:
* Option A: DatabricksIQ: While DatabricksIQ offers integration and data processing capabilities, it is more aligned with data analytics rather than real-time feature serving, which is crucial for immediate updates necessary in a live sports commentary context.
* Option B: Foundation Model APIs: These APIs facilitate interactions with pre-trained models and could be part of the solution, but on their own, they do not provide mechanisms to access real- time game scores.
* Option C: Feature Serving: This is the correct answer as feature serving specifically refers to the real-time provision of data (features) to models for prediction. This would be essential for an LLM that generates analyses based on live game data, ensuring that the commentary is current and based on the latest events in the sport.
* Option D: AutoML: This tool automates the process of applying machine learning models to real-world problems, but it does not directly provide real-time data access, which is a critical requirement for the platform.
Thus,Option C(Feature Serving) is the most suitable tool for the platform as it directly supports the real-time data needs of an LLM-powered sports commentary system, ensuring that the analyses and updates are based on the latest available information.

NEW QUESTION # 55
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DATABRICKS-GENERATIVE-AI-ENGINEER-ASSOCIATE TRAINING MATERIALS - REAL DATABRICKS-GENERATIVE-AI-ENGINEER-ASSOCIATE TORRENT

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