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Databricks Certified Data Engineer Professional Sample Questions:

1. A security analytics pipeline must enrich billions of raw connection logs with geolocation data.
The join hinges on finding which IPv4 range each event's address falls into.
Table 1: network_events ( 5 billion rows)
event_id ip_int
42 3232235777
Table 2: ip_ranges ( 2 million rows)
start_ip_int end_ip_int country
3232235520 3232236031 US
The query is currently very slow:
SELECT n.event_id, n.ip_int, r.country
FROM network_events n
JOIN ip_ranges r
ON n.ip_int BETWEEN r.start_ip_int AND r.end_ip_int;
Which change will most dramatically accelerate the query while preserving its logic?

A) Add a broadcast hint: /*+ BROADCAST(r) */ for ip_ranges.
B) Force a sort-merge join with /*+ MERGE(r) */.
C) Add a range-join hint /*+ RANGE_JOIN(r, 65536) */.
D) Increase spark.sql.shuffle.partitions from 200 to 10000.

2. A data engineer created a daily batch ingestion pipeline using a cluster with the latest DBR version to store banking transaction data, and persisted it in a MANAGED DELTA table called prod.gold.all_banking_transactions_daily. The data engineer is constantly receiving complaints from business users who query this table ad hoc through a SQL Serverless Warehouse about poor query performance. Upon analysis, the data engineer identified that these users frequently use high- cardinality columns as filters. The engineer now seeks to implement a data layout optimization technique that is incremental, easy to maintain, and can evolve over time. Which command should the data engineer implement?

A) Alter the table to use Liquid Clustering and implement a periodic OPTIMIZE command.
B) Alter the table to use Z-ORDER and implement a periodic OPTIMIZE command.
C) Alter the table to use Hive-Style Partitions and implement a periodic OPTIMIZE command.
D) Alter the table to use Hive-Style Partitions + Z-ORDER and implement a periodic OPTIMIZE command.

3. A Databricks job has been configured with 3 tasks, each of which is a Databricks notebook. Task A does not depend on other tasks. Tasks B and C run in parallel, with each having a serial dependency on Task A.
If task A fails during a scheduled run, which statement describes the results of this run?

A) Tasks B and C will attempt to run as configured; any changes made in task A will be rolled back due to task failure.
B) Tasks B and C will be skipped; task A will not commit any changes because of stage failure.
C) Unless all tasks complete successfully, no changes will be committed to the Lakehouse; because task A failed, all commits will be rolled back automatically.
D) Tasks B and C will be skipped; some logic expressed in task A may have been committed before task failure.
E) Because all tasks are managed as a dependency graph, no changes will be committed to the Lakehouse until all tasks have successfully been completed.

4. A data organization has adopted Delta Sharing to securely distribute curated datasets from a Unity Catalog-enabled workspace. The data engineering team shares large Delta tables internally via Databricks-to-Databricks and externally via Open Sharing for aggregated reports. While testing, they encounter challenges related to access control, data update visibility, and shareable object types. What is a limitation of the Delta Sharing protocol or implementation when used with Databricks-to-Databricks or Open Sharing?

A) With Databricks-to-Databricks sharing, Unity Catalog recipients must re-ingest data manually using COPY INTO or REST APIs.
B) Delta Sharing does not support Unity Catalog-enabled tables; only legacy Hive Metastore tables are shareable.
C) With Open Sharing, recipients cannot access Volumes, Models, or notebooks -- only static Delta tables are supported.
D) Delta Sharing (both Databricks-to-Databricks and Open Sharing) allows recipients to modify the source data if they have select privileges.

5. A data engineer needs to implement column masking for a sensitive column in a Unity Catalog- managed table. The masking logic must dynamically check if users belong to specific groups defined in a separate table (group_access) that maps groups to allowed departments. Which approach should the engineer use to efficiently enforce this requirement?

A) Use a row filter to restrict access based on the user's group.
B) Create a view without selecting the sensitive column.
C) Apply a column mask that references the group_access mapping table in its UDF.
D) Create a UDF that hardcodes allowed groups and apply it as a column mask.

Solutions: