Databricks-Certified-Professional-Data-Engineer Databricks Exam Lab Questions

In Delta Lake, a shallow clone creates a new table by copying the metadata of the source table without duplicating the data files. When the vacuum command is run on the source table, it removes old data files that are no longer needed to maintain the transactional log ' s integrity, potentially including files referenced by the shallow clone ' s metadata. If these files are purged, the shallow cloned tables will reference non-existent data files, causing them to stop working properly. This highlights the dependency of shallow clones on the source table ' s data files and the impact of data management operations like vacuum on these clones.

This is the correct answer because it describes how data will be filtered when a query is run with the following filter: longitude < 20 and longitude > -20. The query is run on a Delta Lake table that has the following schema: user_id LONG, post_text STRING, post_id STRING, longitude FLOAT, latitude FLOAT, post_time TIMESTAMP, date DATE. This table is partitioned by the date column. When a query is run on a partitioned Delta Lake table, Delta Lake uses statistics in the Delta Log to identify data files that might include records in the filtered range. The statistics include information such as min and max values for each column in each data file. By using these statistics, Delta Lake can skip reading data files that do not match the filter condition, which can improve query performance and reduce I/O costs. Verified References: [Databricks Certified Data Engineer Professional], under “Delta Lake” section; Databricks Documentation, under “Data skipping” section.

Scheduling a job to execute the data processing pipeline once an hour on a new job cluster is the most cost-effective solution given the scenario. Job clusters are ephemeral in nature; they are spun up just before the job execution and terminated upon completion, which means you only incur costs for the time the cluster is active. Since the total processing time is only 10 minutes, a new job cluster created for each hourly execution minimizes the running time and thus the cost, while also fulfilling the requirement for hourly data updates for the business reporting team ' s dashboards.

Lakeflow Declarative Pipelines (DLT) supports data quality enforcement using @dlt.expect, @dlt.expect_or_drop, and @dlt.expect_all.

@dlt.expect applies a rule and records whether rows pass or fail the condition but does not drop failing rows . Instead, failing rows can be written to a quarantine table.

@dlt.expect_or_drop enforces that only rows passing the condition flow downstream, dropping bad records automatically.

In this case, the requirement is:

Good rows (reading < 120) go to the silver table .

Bad rows (reading > = 120) go to a quarantine table .

Bad rows should not be included in silver.

The correct implementation is Option A , where:

The silver table uses @dlt.expect to validate reading < 120. These rows flow normally.

The quarantine table applies an expectation for reading > = 120, ensuring bad records are captured separately.

Other options are incorrect:

Option B/D: These either use expect_or_drop incorrectly or apply wrong conditions, leading to dropped rows without quarantining properly.

Option C: Uses expect_or_drop for both tables, which would discard bad rows instead of persisting them into a quarantine table.

Thus, Option A meets the business requirement to split good and bad data streams while ensuring both are captured for auditing and processing.