A data engineering team is designing the Gold layer in their Unity Catalog-governed lakehouse for downstream BI and analytics users. The team wants to expose business-ready metrics with fast query performance and consistent definitions, while keeping the transformation logic in Spark notebooks. Which type of Gold layer object meets this requirement?

• A. A materialized view that precomputes aggregations from Silver tables on a schedule and is queried by BI tools for faster, consistent analytics.
• B. A view that directly references raw Bronze tables so BI tools can define their own joins and aggregations.
• C. A streaming table that continuously ingests raw events from source systems and exposes them directly to BI users.
• D. A collection of CSV files stored in a cloud storage object. BI users will parse the files to query the gold layer data.

A data engineer has a Python variable table_name that they would like to use in a SQL query.
They want to construct a Python code block that will run the query using table_name.
They have the following incomplete code block:
____(f"SELECT customer_id, spend FROM {table_name}")
Which of the following can be used to fill in the blank to successfully complete the task?

• A. spark.sql
• B. spark.delta.sql
• C. dbutils.sql
• D. spark.delta.table
• E. spark.table

A data engineer is implementing a data pipeline using Databricks notebooks. The engineer wants to share parameters such as file paths and processing dates across multiple tasks within a job.
Which Databricks utility enables parameter passing?

• A. dbutils.fs
• B. display
• C. dbutils.widgets
• D. spark.conf

A data engineer configures a Databricks Lakeflow Job for daily customer data processing:
- Entry task: Single notebook loads raw data
- Parallel tasks:
- SQL query task for cleansing
- Notebook task for feature engineering
- Pipeline task for model updates
- Exit task: Dashboard refresh (must run after all parallel tasks
complete)
- Requirement: Implement this dependency pattern using a DAG-based task graph Which task configuration ensures all parallel tasks complete before the dashboard refresh task runs?

• A. Add the dashboard task as dependent on all three parallel tasks using fan-in control flow.
• B. Set the dashboard task to depend only on the SQL query task; other tasks run independently.
• C. Create separate Lakeflow Jobs for each parallel task and trigger them sequentially using external orchestration.
• D. Configure the parallel tasks with sequential dependencies so each task waits for the previous one to finish.