Free GEN-AI-ENG sample questions

Correct answer: Use a Python library like `PyMuPDF` to extract tables from the PDFs, convert them to Markdown format, and then embed the Markdown text as separate documents.

Simple text-based chunking strategies are ineffective for structured data like tables. The best approach is to use a specialized library to extract the tables, preserve their structure by converting them to a text-friendly format like Markdown, and then embed them. This ensures the semantic relationship between rows and columns is maintained, leading to much higher retrieval accuracy for queries about specific data points within those tables.

Correct answer: Use Unity Catalog to apply row-level security and column-level masking on the source Delta tables, and ensure the RAG application's service principal has appropriately restricted permissions.

The most robust and scalable governance strategy is to enforce security at the data source. By using Unity Catalog's built-in features like row-level security and column masking on the Delta tables, access control is managed centrally and consistently. The RAG application, operating under a service principal with limited permissions, will inherit these restrictions, ensuring that it can only retrieve and process data the user is authorized to see, thus enforcing compliance.

Correct answer: Pairwise comparison, where the judge LLM is presented with two different responses to the same query and asked to choose the better one.

When ground truth (golden answers) is not available, pairwise comparison is a highly effective evaluation method. It relies on relative judgment rather than absolute correctness. The judge LLM's task is simplified to determining which of two responses is better, which is a more reliable and consistent task for an LLM than assigning an absolute score without a reference. This approach allows for effective ranking and selection of better-performing models or prompts without needing a costly human-labeled dataset.

Correct answer: beautifulsoup4

Beautiful Soup is a powerful Python library designed for parsing HTML and XML documents. It excels at navigating, searching, and modifying the parse tree, making it ideal for extracting specific content (like the main article text within ` ` or ` ` tags) while ignoring irrelevant boilerplate content (like menus in ` ` tags or ads). While `requests` is used to fetch the HTML, `beautifulsoup4` is the tool for cleaning and extracting the desired content.

Correct answers: The number of chunks retrieved per request.; The chunk retrieval scores (e.g., cosine similarity).

Correct answer: `chunks_df.write.format("delta").mode("overwrite").saveAsTable("workspace.default.document_chunks")`

This command correctly uses the Spark DataFrameWriter API. `format("delta")` specifies the storage format. `mode("overwrite")` ensures that if the table already exists, its contents are replaced, and if it doesn't, it will be created. `saveAsTable()` is the action that writes the data to the specified table name in Unity Catalog.

Correct answer: A one-shot prompt that provides an example of the exact JSON structure required, including keys like 'slogans', 'text', and 'style', followed by the request for the new coffee brand slogans.

To get consistent, structured output like JSON, the most reliable method is few-shot (or in this case, one-shot) prompting. By providing a concrete example of the desired output format, you are giving the model a clear template to follow. This significantly reduces the ambiguity and variability in its response, making it much more likely to generate a correctly formatted JSON object with the specified keys and structure every time.

Correct answer: `SELECT transcript_id, ai_query('sentiment_analyzer', transcript_text) AS sentiment FROM transcripts;`

The `ai_query()` function is the standard way to invoke a model serving endpoint from within Databricks SQL for batch inference. The correct syntax requires the endpoint name as the first argument and the input data (in this case, the `transcript_text` column) as the second argument. This allows for seamless, scalable inference on large datasets directly within a SQL query.

Correct answer: True

This statement is true. Inference Tables are a managed feature of Databricks Model Serving. When enabled on an endpoint, Databricks automatically captures the payload of incoming requests and outgoing responses and logs them to a managed Delta table. This process is transparent to the client application and requires no modification to the code making the API calls.

Correct answer: Include `utils.py` in the `code_path` parameter of the `mlflow.pyfunc.log_model` function.

The `code_path` parameter in `mlflow.pyfunc.log_model` is specifically designed for this purpose. It takes a list of file paths or directories. MLflow will automatically package these files with the model and add them to the Python path when the model is loaded for inference. This ensures that any custom modules or helper functions are available to the model's code.