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 that violate the constraints; all existing data must pass CHECK constraints in order to add them to an existing table.
• B. The activity details table already exists; CHECK constraints can only be added during initial table creation.
• C. Because another team uses this table to support a frequently running application, two-phase locking is preventing the operation from committing.
• D. The activity details table already contains records; CHECK constraints can only be added prior to inserting values into a table.
• E. 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.

When evaluating the Ganglia Metrics for a given cluster with 3 executor nodes, which indicator would signal proper utilization of the VM's resources?

• A. Network I/O never spikes
• B. CPU Utilization is around 75%
• C. Bytes Received never exceeds 80 million bytes per second
• D. Total Disk Space remains constant
• E. The five Minute Load Average remains consistent/flat

Which REST API call can be used to review the notebooks configured to run as tasks in a multi-task job?

• A. /jobs/get
• B. /jobs/runs/list
• C. /jobs/runs/get-output
• D. /jobs/runs/get
• E. /jobs/list

A data engineer is investigating unexpectedly high all-purpose compute costs over the past 30 days.
A query against system.compute.clusters returns cluster configuration details, but the engineer notices that several clusters known to be expensive are missing from the results.
The engineer also needs to determine how long each all-purpose cluster actually ran to identify clusters that stayed up longer than intended.
Which two actions should the data engineer take to diagnose the cost drivers?

• A. Derive each cluster's actual runtime from system.compute.node_timeline by summing the active node (driver = true) intervals per cluster_id, rather than from the difference between usage_start_time and usage_end_time in system.billing.usage.
• B. Calculate cumulative runtime per cluster as sum(usage_end_time - usage_start_time) from system.billing.usage, since each usage record spans exactly the interval during which the cluster was executing.
• C. Query system.compute.clusters and filter for rows where auto_termination_minutes is null or zero, treating these as the clusters responsible for the highest spend, since a cluster without auto-termination is necessarily a top cost driver.
• D. Replace the system.compute.clusters query entirely with system.compute.warehouse_events, filtering for SCALED_UP events, because all-purpose cluster cost data is only available through warehouse event records.
• E. Use system.billing.usage as the primary source, filtering on billing_origin_product = 'ALL_PURPOSE', aggregate usage_quantity grouped by usage_metadata.cluster_id, then LEFT JOIN to the most recent system.compute.clusters rows per cluster_id so that clusters without a matching configuration record still appear in the results.

Which statement describes the correct use of pyspark.sql.functions.broadcast?

• A. It caches a copy of the indicated table on attached storage volumes for all active clusters within a Databricks workspace.
• B. It marks a column as small enough to store in memory on all executors, allowing a broadcast join.
• C. It marks a column as having low enough cardinality to properly map distinct values to available partitions, allowing a broadcast join.
• D. It marks a DataFrame as small enough to store in memory on all executors, allowing a broadcast join.
• E. It caches a copy of the indicated table on all nodes in the cluster for use in all future queries during the cluster lifetime.