The data governance team is reviewing code used for deleting records for compliance with GDPR. They note the following logic is used to delete records from the Delta Lake table named users.

Assuming that user_id is a unique identifying key and that delete_requests contains all users that have requested deletion, which statement describes whether successfully executing the above logic guarantees that the records to be deleted are no longer accessible and why?

• A. Yes; Delta Lake ACID guarantees provide assurance that the delete command succeeded fully and permanently purged these records.
• B. No; the Delta Lake delete command only provides ACID guarantees when combined with the merge into command.
• C. No; the Delta cache may return records from previous versions of the table until the cluster is restarted.
• D. Yes; the Delta cache immediately updates to reflect the latest data files recorded to disk.
• E. No; files containing deleted records may still be accessible with time travel until a vacuum command is used to remove invalidated data files.

A data engineer needs to productionize a new Spark application written by teammate. This application has numerous external dependencies, including libraries, and requires custom environment variables and Spark configuration parameters to be set. Which two methods will help the data engineer accomplish the task? (Choose two.)

• A. Use secrets in init scripts to store configuration data
• B. Install libraries on DBFS
• C. Add libraries to compute policies
• D. Use compute policies to set system properties, environment variables, and Spark configuration parameters.
• E. Create init scripts on DBFS.

A data engineer is analyzing transactional data in a PySpark DataFrame df containing customer_id, transaction_timestamp (precise to milliseconds), and amount_spent. The objective is to compute a cumulative sum of amount_spent per customer, strictly ordered by transaction_timestamp. The cumulative sum must include all transactions from the earliest timestamp up to and including the current row, respecting temporal ordering within each customer partition. Which PySpark code snippet most accurately constructs the appropriate window specification and applies the aggregation to yield the correct cumulative expenditure per customer?

• A.
  
• B.
  
• C.
  
• D.
  

A junior data engineer is working to implement logic for a Lakehouse table named silver_device_recordings. The source data contains 100 unique fields in a highly nested JSON structure.
The silver_device_recordings table will be used downstream for highly selective joins on a number of fields, and will also be leveraged by the machine learning team to filter on a handful of relevant fields, in total, 15 fields have been identified that will often be used for filter and join logic.
The data engineer is trying to determine the best approach for dealing with these nested fields before declaring the table schema.
Which of the following accurately presents information about Delta Lake and Databricks that may Impact their decision-making process?

• A. Schema inference and evolution on Databricks ensure that inferred types will always accurately match the data types used by downstream systems.
• B. Tungsten encoding used by Databricks is optimized for storing string data: newly-added native support for querying JSON strings means that string types are always most efficient.
• C. Because Delta Lake uses Parquet for data storage, Dremel encoding information for nesting can be directly referenced by the Delta transaction log.
• D. By default Delta Lake collects statistics on the first 32 columns in a table; these statistics are leveraged for data skipping when executing selective queries.

A data engineering workspace was automatically enabled for Unity Catalog, creating a workspace catalog. New team members report they can create tables in the default schema but cannot access table in other schemas within the same workspace catalog. Why are the new team members unable to access tables in other schemas?

• A. New users only receive CREATE TABLE privileges on the default schema.
• B. Workspace catalog permissions are not subject to inheritance rules.
• C. Workspace users receive USE CATALOG and specific privileges on default schema only.
• D. Tables in other schemas require additional BROWSEprivileges that new users don't receive automatically