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

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.

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.

Problem Context: The engineer needs a cost-effective deployment strategy for an LLM application with relatively low request volume.

Explanation of Options:

Option A: Switching to external models may not provide the required control or integration necessary for specific application needs.

Option B: Using a pay-per-token model is cost-effective, especially for applications with variable or low request volumes, as it aligns costs directly with usage.

Option C: Changing to a model with fewer parameters could reduce costs, but might also impact the performance and capabilities of the application.

Option D: Manually throttling requests is a less efficient and potentially error-prone strategy for managing costs.

OptionBis ideal, offering flexibility and cost control, aligning expenses directly with the application's usage patterns.