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

Topic	Details
Topic 1	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.
Topic 2	Assembling and Deploying Applications: In this topic, Generative AI Engineers get knowledge about coding a chain using a pyfunc mode, coding a simple chain using langchain, and coding a simple chain according to requirements. Additionally, the topic focuses on basic elements needed to create a RAG application. Lastly, the topic addresses sub-topics about registering the model to Unity Catalog using MLflow.
Topic 3	Governance: Generative AI Engineers who take the exam get knowledge about masking techniques, guardrail techniques, and legal licensing requirements in this topic.
Topic 4	Data Preparation: Generative AI Engineers covers a chunking strategy for a given document structure and model constraints. The topic also focuses on filter extraneous content in source documents. Lastly, Generative AI Engineers also learn about extracting document content from provided source data and format.
Topic 5	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.

Databricks Certified Generative AI Engineer Associate Sample Questions (Q32-Q37):

NEW QUESTION # 32
A Generative AI Engineer is building a Generative AI system that suggests the best matched employee team member to newly scoped projects. The team member is selected from a very large team. Thematch should be based upon project date availability and how well their employee profile matches the project scope. Both the employee profile and project scope are unstructured text.
How should the Generative Al Engineer architect their system?

A. Create a tool to find available team members given project dates. Create a second tool that can calculate a similarity score for a combination of team member profile and the project scope. Iterate through the team members and rank by best score to select a team member.
B. Create a tool for finding available team members given project dates. Embed all project scopes into a vector store, perform a retrieval using team member profiles to find the best team member.
C. Create a tool for finding available team members given project dates. Embed team profiles into a vector store and use the project scope and filtering to perform retrieval to find the available best matched team members.
D. Create a tool for finding team member availability given project dates, and another tool that uses an LLM to extract keywords from project scopes. Iterate through available team members' profiles and perform keyword matching to find the best available team member.

Answer: C

NEW QUESTION # 33
A Generative Al Engineer needs to design an LLM pipeline to conduct multi-stage reasoning that leverages external tools. To be effective at this, the LLM will need to plan and adapt actions while performing complex reasoning tasks.
Which approach will do this?

A. Encourage the LLM to make multiple API calls in sequence without planning or structuring the calls, allowing the LLM to decide when and how to use external tools spontaneously.
B. Tram the LLM to generate a single, comprehensive response without interacting with any external tools, relying solely on its pre-trained knowledge.
C. Use a Chain-of-Thought (CoT) prompting technique to guide the LLM through a series of reasoning steps, then manually input the results from external tools for the final answer.
D. Implement a framework like ReAct which allows the LLM to generate reasoning traces and perform task-specific actions that leverage external tools if necessary.

Answer: D

Explanation:
The task requires an LLM pipeline for multi-stage reasoning with external tools, necessitating planning, adaptability, and complex reasoning. Let's evaluate the options based on Databricks' recommendations for advanced LLM workflows.
* Option A: Train the LLM to generate a single, comprehensive response without interacting with any external tools, relying solely on its pre-trained knowledge
* This approach limits the LLM to its static knowledge base, excluding external tools and multi- stage reasoning. It can't adapt or plan actions dynamically, failing the requirements.
* Databricks Reference:"External tools enhance LLM capabilities beyond pre-trained knowledge" ("Building LLM Applications with Databricks," 2023).
* Option B: Implement a framework like ReAct which allows the LLM to generate reasoning traces and perform task-specific actions that leverage external tools if necessary
* ReAct (Reasoning + Acting) combines reasoning traces (step-by-step logic) with actions (e.g., tool calls), enabling the LLM to plan, adapt, and execute complex tasks iteratively. This meets all requirements: multi-stage reasoning, tool use, and adaptability.
* Databricks Reference:"Frameworks like ReAct enable LLMs to interleave reasoning and external tool interactions for complex problem-solving"("Generative AI Cookbook," 2023).
* Option C: Encourage the LLM to make multiple API calls in sequence without planning or structuring the calls, allowing the LLM to decide when and how to use external tools spontaneously
* Unstructured, spontaneous API calls lack planning and may lead to inefficient or incorrect tool usage. This doesn't ensure effective multi-stage reasoning or adaptability.
* Databricks Reference: Structured frameworks are preferred:"Ad-hoc tool calls can reduce reliability in complex tasks"("Building LLM-Powered Applications").
* Option D: Use a Chain-of-Thought (CoT) prompting technique to guide the LLM through a series of reasoning steps, then manually input the results from external tools for the final answer
* CoT improves reasoning but relies on manual tool interaction, breaking automation and adaptability. It's not a scalable pipeline solution.
* Databricks Reference:"Manual intervention is impractical for production LLM pipelines" ("Databricks Generative AI Engineer Guide").
Conclusion: Option B (ReAct) is the best approach, as it integrates reasoning and tool use in a structured, adaptive framework, aligning with Databricks' guidance for complex LLM workflows.

NEW QUESTION # 34
A Generative Al Engineer is building a system which will answer questions on latest stock news articles.
Which will NOT help with ensuring the outputs are relevant to financial news?

A. Implement a comprehensive guardrail framework that includes policies for content filters tailored to the finance sector.
B. Increase the compute to improve processing speed of questions to allow greater relevancy analysis C Implement a profanity filter to screen out offensive language
C. Incorporate manual reviews to correct any problematic outputs prior to sending to the users

Answer: B

Explanation:
In the context of ensuring that outputs are relevant to financial news, increasing compute power (option B) does not directly improve therelevanceof the LLM-generated outputs. Here's why:
* Compute Power and Relevancy:Increasing compute power can help the model process inputs faster, but it does not inherentlyimprove therelevanceof the answers. Relevancy depends on the data sources, the retrieval method, and the filtering mechanisms in place, not on how quickly the model processes the query.
* What Actually Helps with Relevance:Other methods, like content filtering, guardrails, or manual review, can directly impact the relevance of the model's responses by ensuring the model focuses on pertinent financial content. These methods help tailor the LLM's responses to the financial domain and avoid irrelevant or harmful outputs.
* Why Other Options Are More Relevant:
* A (Comprehensive Guardrail Framework): This will ensure that the model avoids generating content that is irrelevant or inappropriate in the finance sector.
* C (Profanity Filter): While not directly related to financial relevancy, ensuring the output is clean and professional is still important in maintaining the quality of responses.
* D (Manual Review): Incorporating human oversight to catch and correct issues with the LLM's output ensures the final answers are aligned with financial content expectations.
Thus, increasing compute power does not help with ensuring the outputs are more relevant to financial news, making option B the correct answer.

NEW QUESTION # 35
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: Find the answers to similar questions and respond with a summary
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 Group the chat logs by users, followed by summarizing each user's interactions, then respond

Answer: A

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 # 36
A Generative Al Engineer is building a RAG application that answers questions about internal documents for the company SnoPen AI.
The source documents may contain a significant amount of irrelevant content, such as advertisements, sports news, or entertainment news, or content about other companies.
Which approach is advisable when building a RAG application to achieve this goal of filtering irrelevant information?

A. Include in the system prompt that any information it sees will be about SnoPenAI, even if no data filtering is performed.
B. Consolidate all SnoPen AI related documents into a single chunk in the vector database.
C. Keep all articles because the RAG application needs to understand non-company content to avoid answering questions about them.
D. Include in the system prompt that the application is not supposed to answer any questions unrelated to SnoPen Al.

Answer: D

Explanation:
In a Retrieval-Augmented Generation (RAG) application built to answer questions about internal documents, especially when the dataset contains irrelevant content, it's crucial to guide the system to focus on the right information. The best way to achieve this is byincluding a clear instruction in the system prompt(option C).
* System Prompt as Guidance:The system prompt is an effective way to instruct the LLM to limit its focus to SnoPen AI-related content. By clearly specifying that the model should avoid answering questions unrelated to SnoPen AI, you add an additional layer of control that helps the model stay on- topic, even if irrelevant content is present in the dataset.
* Why This Approach Works:The prompt acts as a guiding principle for the model, narrowing its focus to specific domains. This prevents the model from generating answers based on irrelevant content, such as advertisements or news unrelated to SnoPen AI.
* Why Other Options Are Less Suitable:
* A (Keep All Articles): Retaining all content, including irrelevant materials, without any filtering makes the system prone to generating answers based on unwanted data.
* B (Include in the System Prompt about SnoPen AI): This option doesn't address irrelevant content directly, and without filtering, the model might still retrieve and use irrelevant data.
* D (Consolidating Documents into a Single Chunk): Grouping documents into a single chunk makes the retrieval process less efficient and won't help filter out irrelevant content effectively.
Therefore, instructing the system in the prompt not to answer questions unrelated to SnoPen AI (option C) is the best approach to ensure the system filters out irrelevant information.

NEW QUESTION # 37
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Databricks Certified Generative AI Engineer Associate Sample Questions (Q32-Q37):

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