Which of the following locations hosts the driver and worker nodes of a Databricks-managed clus-ter?

• A.Data plane
• B.Control plane
• C.Databricks Filesystem
• D.Databricks web application
• E.JDBC data source

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When investigating a data issue you realized that a process accidentally updated the table, you want to query the same table with yesterday's version of the data so you can review what the prior version looks like, what is the best way to query historical data so you can do your analysis?

• A.SELECT * FROM TIME_TRAVEL(table_name) WHERE time_stamp = 'timestamp'
• B.TIME_TRAVEL FROM table_name WHERE time_stamp = date_sub(current_date(), 1)
• C.SELECT * FROM table_name TIMESTAMP AS OF date_sub(current_date(), 1)
• D.DISCRIBE HISTORY table_name AS OF date_sub(current_date(), 1)
• E.SHOW HISTORY table_name AS OF date_sub(current_date(), 1)

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Spill occurs as a result of executing various wide transformations. However, diagnosing spill requires one to proactively look for key indicators.
Where in the Spark UI are two of the primary indicators that a partition is spilling to disk?

• A.Stage's detail screen and Executor's files
• B.Stage's detail screen and Query's detail screen
• C.Driver's and Executor's log files
• D.Executor's detail screen and Executor's log files

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A junior data engineer has ingested a JSON file into a table raw_table with the following schema:
1. cart_id STRING,
2. items ARRAY<item_id:STRING>
The junior data engineer would like to unnest the items column in raw_table to result in a new table with the
following schema:
1.cart_id STRING,
2.item_id STRING
Which of the following commands should the junior data engineer run to complete this task?

• A.1. SELECT cart_id, filter(items) AS item_id
  2. FROM raw_table;
• B.1. SELECT cart_id, slice(items) AS item_id
  2. FROM raw_table;
• C.1. SELECT cart_id, reduce(items) AS item_id
  2. FROM raw_table;
• D.1. SELECT cart_id, flatten(items) AS item_id
  2. FROM raw_table;
• E.1. SELECT cart_id, explode(items) AS item_id
  2. FROM raw_table;

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The data engineering team is migrating an enterprise system with thousands of tables and views into the Lakehouse. They plan to implement the target architecture using a series of bronze, silver, and gold tables. Bronze tables will almost exclusively be used by production data engineering workloads, while silver tables will be used to support both data engineering and machine learning workloads. Gold tables will largely serve business intelligence and reporting purposes. While personal identifying information (PII) exists in all tiers of data, pseudonymization and anonymization rules are in place for all data at the silver and gold levels.
The organization is interested in reducing security concerns while maximizing the ability to collaborate across diverse teams.
Which statement exemplifies best practices for implementing this system?

• A.Isolating tables in separate databases based on data quality tiers allows for easy permissions management through database ACLs and allows physical separation of default storage locations for managed tables.
• B.Because databases on Databricks are merely a logical construct, choices around database organization do not impact security or discoverability in the Lakehouse.
• C.Storinq all production tables in a single database provides a unified view of all data assets available throughout the Lakehouse, simplifying discoverability by granting all users view privileges on this database.
• D.Working in the default Databricks database provides the greatest security when working with managed tables, as these will be created in the DBFS root.
• E.Because all tables must live in the same storage containers used for the database they're created in, organizations should be prepared to create between dozens and thousands of databases depending on their data isolation requirements.

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