Databricks-Generative-AI-Engineer-Associate Dumps with Practice Exam Questions Answers

The system answers questions on unfolding news topics using articles and social media, with a concern about toxic outputs from toxic inputs. A guardrail must limit toxicity in the LLM’s responses. Let’s evaluate the options.

Option A: Use only approved social media and news accounts to prevent unexpected toxic data from getting to the LLM

Curating input sources (e.g., verified accounts) reduces exposure to toxic content at the data ingestion stage, directly limiting toxic outputs. This is a proactive guardrail aligned with data quality control.

Databricks Reference:"Control input data quality to mitigate unwanted LLM behavior, such as toxicity"("Building LLM Applications with Databricks," 2023).

Option B: Implement rate limiting

Rate limiting controls request frequency, not content quality. It prevents overload but doesn’t address toxicity in social media inputs or outputs.

Databricks Reference: Rate limiting is for performance, not safety:"Use rate limits to manage compute load"("Generative AI Cookbook").

Option C: Reduce the amount of context items the system will include in consideration for its response

Reducing context might limit exposure to some toxic items but risks losing relevant information, and it doesn’t specifically target toxicity. It’s an indirect, imprecise fix.

Databricks Reference: Context reduction is for efficiency, not safety:"Adjust context size based on performance needs"("Databricks Generative AI Engineer Guide").

Option D: Log all LLM system responses and perform a batch toxicity analysis monthly

Logging and analyzing responses is reactive, identifying toxicity after it occurs rather than preventing it. Monthly analysis doesn’t limit real-time toxic outputs.

Databricks Reference: Monitoring is for auditing, not prevention:"Log outputs for post-hoc analysis, but use input filters for safety"("Building LLM-Powered Applications").

Conclusion: Option A is the most effective guardrail, proactively filtering toxic inputs from unverified sources, which aligns with Databricks’ emphasis on data quality as a primary safety mechanism for LLM systems.

For a code generation model that supports multiple programming languages and where quality is the primary objective,CodeLlama-34Bis the most suitable choice. Here’s the reasoning:

Specialization in Code Generation:CodeLlama-34B is specifically designed for code generation tasks. This model has been trained with a focus on understanding and generating code, which makes it particularly adept at handling various programming languages and coding contexts.

Capacity and Performance:The "34B" indicates a model size of 34 billion parameters, suggesting a high capacity for handling complex tasks and generating high-quality outputs. The large model size typically correlates with better understanding and generation capabilities in diverse scenarios.

Suitability for Development Teams:Given that the model is optimized for code, it will be able to assist software developers more effectively than general-purpose models. It understands coding syntax, semantics, and the nuances of different programming languages.

Why Other Options Are Less Suitable:

A (Llama2-70b): While also a large model, it's more general-purpose and may not be as fine-tuned for code generation as CodeLlama.

B (BGE-large): This model may not specifically focus on code generation.

C (MPT-7b): Smaller than CodeLlama-34B and likely less capable in handling complex code generation tasks at high quality.

Therefore, for a high-quality, multi-language code generation application,CodeLlama-34B(option D) is the best fit.

To understand how a typical RAG-enabled customer-facing chatbot processes a user's question, let’s go through the correct sequence as depicted in the diagram and explained in option A:

Embedding Model (1):The first step involves the user's question being processed through an embedding model. This model converts the text into a vector format that numerically represents the text. This step is essential for allowing the subsequent vector search to operate effectively.

Vector Search (2):The vectors generated by the embedding model are then used in a vector search mechanism. This search identifies the most relevant documents or previously answered questions that are stored in a vector format in a database.

Context-Augmented Prompt (3):The information retrieved from the vector search is used to create a context-augmented prompt. This step involves enhancing the basic user query with additional relevant information gathered to ensure the generated response is as accurate and informative as possible.

Response-Generating LLM (4):Finally, the context-augmented prompt is fed into a response-generating large language model (LLM). This LLM uses the prompt to generate a coherent and contextually appropriate answer, which is then delivered as the final output to the user.

Why Other Options Are Less Suitable:

B, C, D: These options suggest incorrect sequences that do not align with how a RAG system typically processes queries. They misplace the role of embedding models, vector search, and response generation in an order that would not facilitate effective information retrieval and response generation.

Thus, the correct sequence isembedding model, vector search, context-augmented prompt, response-generating LLM, which is option A.

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.

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.