A financial services firm is developing a RAG application to answer analyst questions about quarterly earnings reports. The reports are dense PDFs. During evaluation, the team notices that retrieval often fails to find specific numerical data mentioned deep within tables. The current chunking strategy is a simple recursive character split with a size of 1000 and an overlap of 200. What is the most effective approach to improve retrieval accuracy for tabular data?
A legal tech company is building a RAG system using a large corpus of internal legal documents. To comply with data privacy regulations, any document containing Personally Identifiable Information (PII) must be handled with strict access controls. The documents are stored in Delta Lake and managed by Unity Catalog. How should an engineer implement a governance strategy to prevent unauthorized access to sensitive documents during the retrieval process?
An e-commerce company has deployed a customer support chatbot. The team wants to use an LLM-as-a-judge approach to evaluate the helpfulness of the chatbot's responses. They have a dataset of customer queries but lack a corresponding set of human-written, 'golden' answers. Which evaluation method is most suitable in this scenario?
A developer is building a RAG application that sources information from public websites. To keep the information current, the data ingestion pipeline runs daily, scraping new articles. The developer notices that many articles contain large, irrelevant sections like advertisements, navigation menus, and user comments, which are degrading the quality of the retrieved context. Which Python library is best suited for extracting only the main article content from these HTML pages?
A development team is using Inference Tables to monitor a deployed RAG application. They notice a sudden spike in requests that result in responses containing fallback messages like 'I cannot answer this question based on the provided information.' This indicates a problem with the retrieval step. Which TWO metrics, available through Inference Tables and the associated monitoring dashboards, would be most direct in diagnosing this retrieval failure? (Choose two.)
A data engineer needs to load processed text chunks into a Delta table for a RAG application. The data is currently in a Spark DataFrame named `chunks_df` with columns `doc_id`, `chunk_text`, and `chunk_sequence`. The target table, `workspace.default.document_chunks`, must be created if it does not exist and overwritten if it does. Which command correctly performs this operation?
A developer is creating a prompt for a marketing campaign slogan generator. The business requires the output to be a JSON object containing three distinct slogan options, each with a specific 'style' (e.g., 'playful', 'professional', 'bold'). Which prompt design is most likely to elicit the desired structured response consistently?
A financial firm is deploying a sentiment analysis model on earnings call transcripts. To manage costs, they want to use the `ai_query()` function for batch processing directly within their Databricks SQL workflow. The model is served at an endpoint named `sentiment_analyzer`. The transcripts are in a table `transcripts` with a column `transcript_text`. What is the correct SQL syntax to invoke the model?
True or False: When enabling Inference Tables on a Databricks Model Serving endpoint, both the requests and the responses are automatically captured and stored in a Delta table in the user's Unity Catalog schema without requiring any code changes to the client application.
A developer is building a custom `pyfunc` model for a RAG chain. The model needs to perform a pre-processing step to extract keywords from the user's query before sending it to the retriever. The keyword extraction logic is contained in a helper function within a separate Python file (`utils.py`). How should the developer package the model with MLflow to ensure the `utils.py` file is available at inference time?