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.
  

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The data governance team has instituted a requirement that all tables containing Personal Identifiable Information (PH) must be clearly annotated. This includes adding column comments, table comments, and setting the custom table property "contains_pii" = true.
The following SQL DDL statement is executed to create a new table:

Which command allows manual confirmation that these three requirements have been met?

• A. SHOW TABLES dev
• B. DESCRIBE HISTORY dev.pii test
• C. DESCRIBE EXTENDED dev.pii test
• D. SHOW TBLPROPERTIES dev.pii test
• E. DESCRIBE DETAIL dev.pii test

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A data architect has heard about lake's built-in versioning and time travel capabilities. For auditing purposes they have a requirement to maintain a full of all valid street addresses as they appear in the customers table.
The architect is interested in implementing a Type 1 table, overwriting existing records with new values and relying on Delta Lake time travel to support long-term auditing. A data engineer on the project feels that a Type 2 table will provide better performance and scalability. Which piece of information is critical to this decision?

• A. Delta Lake only supports Type 0 tables; once records are inserted to a Delta Lake table, they cannot be modified.
• B. Delta Lake time travel cannot be used to query previous versions of these tables because Type 1 changes modify data files in place.
• C. Shallow clones can be combined with Type 1 tables to accelerate historic queries for long-term versioning.
• D. Delta Lake time travel does not scale well in cost or latency to provide a long-term versioning solution.
• E. Data corruption can occur if a query fails in a partially completed state because Type 2 tables requires setting multiple fields in a single update.

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A data team is automating a daily multi-task ETL pipeline in Databricks. The pipeline includes a notebook for ingesting raw data, a Python wheel task for data transformation, and a SQL query to update aggregates. They want to trigger the pipeline programmatically and see previous runs in the GUI. They need to ensure tasks are retried on failure and stakeholders are notified by email if any task fails. Which two approaches will meet these requirements? (Choose two.)

• A. Trigger the job programmatically using the Databricks Jobs REST API (/jobs/run-now), the CLI (databricks jobs run-now), or one of the Databricks SDKs.
• B. Create a single orchestrator notebook that calls each step with dbutils.notebook.run(), defining a job for that notebook and configuring retries and notifications at the notebook level.
• C. Use the REST API endpoint /jobs/runs/submit to trigger each task individually as separate job runs and implement retries using custom logic in the orchestrator.
• D. Create a multi-task job using the UI, Databricks Asset Bundles (DABs), or the Jobs REST API (/jobs/create) with notebook, Python wheel, and SQL tasks. Configure task-level retries and email notifications in the job definition.
• E. Use Databricks Asset Bundles (DABs) to deploy the workflow, then trigger individual tasks directly by referencing each task's notebook or script path in the workspace.

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The downstream consumers of a Delta Lake table have been complaining about data quality issues impacting performance in their applications. Specifically, they have complained that invalid latitude and longitude values in the activity_details table have been breaking their ability to use other geolocation processes.
A junior engineer has written the following code to add CHECK constraints to the Delta Lake table:

A senior engineer has confirmed the above logic is correct and the valid ranges for latitude and longitude are provided, but the code fails when executed.
Which statement explains the cause of this failure?

• A. The activity details table already contains records; CHECK constraints can only be added prior to inserting values into a table.
• B. The activity details table already exists; CHECK constraints can only be added during initial table creation.
• C. The current table schema does not contain the field valid coordinates; schema evolution will need to be enabled before altering the table to add a constraint.
• D. The activity details table already contains records that violate the constraints; all existing data must pass CHECK constraints in order to add them to an existing table.
• E. Because another team uses this table to support a frequently running application, two-phase locking is preventing the operation from committing.

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