A data governance team at a large enterprise is improving data discoverability across its organization. The team has hundreds of tables in their Databricks Lakehouse with thousands of columns that lack proper documentation. Many of these tables were created by different teams over several years, with missing context about column meanings and business logic. The data governance team needs to quickly generate comprehensive column descriptions for all existing tables to meet compliance requirements and improve data literacy across the organization. They want to leverage modern capabilities to automatically generate meaningful descriptions rather than manually documenting each column, which would take months to complete. Which approach should the team use in Databricks to automatically generate column comments and descriptions for existing tables?

• A. Use the DESCRIBE TABLE command to extract existing schema information and manually write descriptions based on column names and data types.
• B. Write custom PySpark code using df.describe() and df.schema to programmatically generate basic statistical descriptions for each column.
• C. Use Delta Lake's DESCRIBE HISTORY command to analyze table evolution and infer column purposes from historical changes.
• D. Navigate to the table in Databricks Catalog Explorer, select the table schema view, and use the AI Generate option which leverages artificial intelligence to automatically create meaningful column descriptions based on column names, data types, sample values, and data patterns.

The DevOps team has configured a production workload as a collection of notebooks scheduled to run daily using the Jobs UI. A new data engineering hire is onboarding to the team and has requested access to one of these notebooks to review the production logic.
What are the maximum notebook permissions that can be granted to the user without allowing accidental changes to production code or data?

• A. Can Run
• B. No permissions
• C. Can Read
• D. Can Edit
• E. Can Manage

The data science team has requested assistance in accelerating queries on free form text from user reviews. The data is currently stored in Parquet with the below schema:
item_id INT, user_id INT, review_id INT, rating FLOAT, review STRING
The review column contains the full text of the review left by the user. Specifically, the data science team is looking to identify if any of 30 key words exist in this field.
A junior data engineer suggests converting this data to Delta Lake will improve query performance.
Which response to the junior data engineer's suggestion is correct?

• A. Text data cannot be stored with Delta Lake.
• B. Delta Lake statistics are not optimized for free text fields with high cardinality.
• C. ZORDER ON review will need to be run to see performance gains.
• D. The Delta log creates a term matrix for free text fields to support selective filtering.
• E. Delta Lake statistics are only collected on the first 4 columns in a table.

The following code has been migrated to a Databricks notebook from a legacy workload:

The code executes successfully and provides the logically correct results, however, it takes over
20 minutes to extract and load around 1 GB of data.
Which statement is a possible explanation for this behavior?

• A. %sh executes shell code on the driver node. The code does not take advantage of the worker nodes or Databricks optimized Spark.
• B. Instead of cloning, the code should use %sh pip install so that the Python code can get executed in parallel across all nodes in a cluster.
• C. %sh does not distribute file moving operations; the final line of code should be updated to use %fs instead.
• D. Python will always execute slower than Scala on Databricks. The run.py script should be refactored to Scala.
• E. %sh triggers a cluster restart to collect and install Git. Most of the latency is related to cluster startup time.

A team of data engineer are adding tables to a DLT pipeline that contain repetitive expectations for many of the same data quality checks.
One member of the team suggests reusing these data quality rules across all tables defined for this pipeline.
What approach would allow them to do this?

• A. Add data quality constraints to tables in this pipeline using an external job with access to pipeline configuration files.
• B. Use global Python variables to make expectations visible across DLT notebooks included in the same pipeline.
• C. Maintain data quality rules in a Delta table outside of this pipeline's target schema, providing the schema name as a pipeline parameter.
• D. Maintain data quality rules in a separate Databricks notebook that each DLT notebook of file.