A data engineer wants to store intermediate data in a temporary view for use within the same Spark session. The data should not persist after the session ends. Which Spark SQL command should be used?

• A. CREATE VIEW
• B. CREATE TABLE
• C. CREATE TEMP VIEW
• D. CREATE DATABASE

A data engineer runs CREATE TABLE gold.revenue AS SELECT * FROM silver.orders in Unity Catalog without specifying a LOCATION.
What is the result?

• A. An external table is created and files are written to the workspace root DBFS location.
• B. A managed Delta table is created in the metastore/schema's default storage location.
• C. A temporary view is created that is dropped at the end of the session.
• D. The command fails because a LOCATION clause is mandatory in Unity Catalog.

In a healthcare provider organization using Delta Lake to store electronic health records (EHRs), a data analyst needs to analyze a snapshot of the patient_records table from two weeks ago before some recent data corrections were applied. What approach should the Data Engineer take to allow the analyst to query that specific prior version?

• A. Use the VACUUMcommand to remove all versions of the table older than two weeks, then the analyst can query the remaining version.
• B. Restore the table to the version from two weeks ago using the RESTOREcommand, and have the analyst query the restored table.
• C. Identify the version number corresponding to two weeks ago from the Delta transaction log, share that version number with the analyst to query using VERSION AS OF syntax, or export that version to a new Delta table for the analyst to query.
• D. Truncate the table to remove all data, then reload the data from two weeks ago into the truncated table for the analyst to query.

A governance team is evaluating whether to use Unity Catalog attribute-based access control (ABAC) policies or manually applied row filters and column masks to protect sensitive data across their catalog. Why should the team use ABAC policies instead of manually applied row filters and column masks?

• A. ABAC policies allow row filters and column masks to be written in Python and Scala directly, whereas manually applied filters and masks can only use SQL user-defined functions.
• B. ABAC policies use governed tags to dynamically match tables and columns, so a single policy defined on a catalog automatically applies to all current and future tables within that catalog without per-table configuration.
• C. ABAC policies support applying multiple distinct row filters to the same table for a single user at query time, whereas manually applied row filters are limited to one filter per table.
• D. ABAC policies can be applied to views and materialized views in addition to tables, whereas manually applied row filters and column masks can only be applied to tables.

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. Create separate Lakeflow Jobs for each parallel task and trigger them sequentially using external orchestration.
• C. Configure the parallel tasks with sequential dependencies so each task waits for the previous one to finish.
• D. Set the dashboard task to depend only on the SQL query task; other tasks run independently.