Databricks Databricks-Generative-AI-Engineer-Associate Exam With Confidence Using Practice Dumps

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.

The goal is to make fine-tuned LLMs from a production (prod) Databricks workspace available for testing in a development (dev) workspace, leveraging Unity Catalog and MLflow, while ensuring cost-effectiveness and security. Let’s analyze the options.

Option A: Use an external model registry which can be accessed from all workspaces

An external registry adds cost (e.g., hosting fees) and complexity (e.g., integration, security configurations) outside Databricks’ native ecosystem, reducing security compared to Unity Catalog’s governance.

Databricks Reference:"Unity Catalog provides a centralized, secure model registry within Databricks"("Unity Catalog Documentation," 2023).

Option B: Setup a script to export the model from prod and import it to dev

Export/import scripts require manual effort, storage for model artifacts, and repeated execution, increasing operational cost and risk (e.g., version mismatches, unsecured transfers). It’s less efficient than a native solution.

Databricks Reference: Manual processes are discouraged when Unity Catalog offers built-in sharing:"Avoid redundant workflows with Unity Catalog’s cross-workspace access"("MLflow with Unity Catalog").

Option C: Setup a duplicate training pipeline in dev, so that an identical model is available in dev

Duplicating the training pipeline doubles compute and storage costs, as it retrains the model from scratch. It’s neither cost-effective nor necessary when the prod model can be reused securely.

Databricks Reference:"Re-running training is resource-intensive; leverage existing models where possible"("Generative AI Engineer Guide").

Option D: Use MLflow to log the model directly into Unity Catalog, and enable READ access in the dev workspace to the model

Unity Catalog, integrated with MLflow, allows models logged in prod to be centrally managed and accessed across workspaces with fine-grained permissions (e.g., READ for dev). This is cost-effective (no extra infrastructure or retraining) and secure (governed by Databricks’ access controls).

Databricks Reference:"Log models to Unity Catalog via MLflow, then grant access to other workspaces securely"("MLflow Model Registry with Unity Catalog," 2023).

Conclusion: Option D leverages Databricks’ native tools (MLflow and Unity Catalog) for a seamless, cost-effective, and secure solution, avoiding external systems, manual scripts, or redundant training.

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.