Databricks Certified Data Engineer Professional Exam Questions

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Total 215 questions
Question 1

A data engineer needs to provide access to a group named manufacturing-team. The team needs privileges to create tables in the quality schema.

Which set of SQL commands will grant a group named manufacturing-team to create tables in a schema named production with the parent catalog named manufacturing with the least privileges?



Answer : C

To create a table within a schema, a principal must have CREATE TABLE on the schema, USE SCHEMA on that schema, and USE CATALOG on the parent catalog. This combination ensures the group has just enough privileges to create objects in that schema without excessive permissions like CREATE SCHEMA or CREATE CATALOG.

Reference Source: Databricks Unity Catalog Privilege Model -- ''Privileges Required to Create a Table.''


Question 2

The data engineering team has configured a job to process customer requests to be forgotten (have their data deleted). All user data that needs to be deleted is stored in Delta Lake tables using default table settings.

The team has decided to process all deletions from the previous week as a batch job at 1am each Sunday. The total duration of this job is less than one hour. Every Monday at 3am, a batch job executes a series of VACUUM commands on all Delta Lake tables throughout the organization.

The compliance officer has recently learned about Delta Lake's time travel functionality. They are concerned that this might allow continued access to deleted data.

Assuming all delete logic is correctly implemented, which statement correctly addresses this concern?



Answer : E

https://learn.microsoft.com/en-us/azure/databricks/delta/vacuum


Question 3

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?



Answer : B

The query joins billions of rows (network_events) with millions of rows (ip_ranges) using a range predicate (BETWEEN). Unlike equality joins (=), range joins are not efficiently handled by broadcast or sort-merge joins because:

Broadcast Join (D): Effective for small tables but only for equality joins. Since this query uses a range condition, broadcast will not reduce the complexity of scanning billions of records across non-equality conditions.

Sort-Merge Join (C): Works for ordered joins but is inefficient on range conditions. Sorting billions of records adds excessive overhead and will not resolve the bottleneck.

Increasing Shuffle Partitions (A): Only spreads out shuffle work but does not address the fundamental inefficiency of range-based lookups at scale.

Range Joins in Spark (RANGE_JOIN hint):

Databricks provides range join optimizations specifically for conditions such as BETWEEN. By applying a RANGE_JOIN hint, Spark can build optimized data structures (such as interval indexes or partition pruning strategies) that map billions of input rows to ranges much faster. This avoids brute-force scans and unnecessary shuffle costs.

Thus, Option B is the correct solution because:

It leverages range-join optimization, which is purpose-built for queries joining massive event logs to smaller lookup tables with IP ranges.

This ensures Spark can evaluate billions of rows against millions of ranges with optimized matching logic, drastically improving query performance while preserving correctness.


Question 4

A departing platform owner currently holds ownership of multiple catalogs and controls storage credentials and external locations. A data engineer has been asked to ensure continuity: transfer catalog ownership to the platform team group, delegate ongoing privilege management, and retain the ability to receive and share data via Delta Sharing.

Which role must be in place to perform these actions across the metastore?



Answer : A

Metastore Admins have the highest administrative privileges within a Unity Catalog metastore. They can transfer ownership of any Unity Catalog object, including catalogs, schemas, tables, storage credentials, and external locations. Metastore Admins are also required to manage Delta Sharing configurations such as creating or transferring shares and recipients.

Account Admins, by contrast, only create metastores and cannot change ownership or manage Delta Sharing objects. Workspace Admins have privileges limited to workspace-level management, not cross-metastore access.

Reference Source: Databricks Unity Catalog Administration Guide -- ''Metastore admin privileges and ownership transfer.''


Question 5

A query is taking too long to run. After investigating the Spark UI, the data engineer discovered a significant amount of disk spill. The compute instance being used has a core-to-memory ratio of 1:2.

What are the two steps the data engineer should take to minimize spillage? (Choose 2 answers)



Answer : A, D

Databricks recommends addressing disk spilling---which occurs when Spark tasks run out of memory---by increasing memory per core and controlling partition size. Selecting an instance type with a higher memory-to-core ratio (A) provides each task with more available RAM, directly reducing the chance of spilling to disk. Additionally, reducing spark.sql.files.maxPartitionBytes (D) creates smaller partitions, preventing any single task from holding too much data in memory. Increasing partition size (C) or disk capacity (B) does not solve memory bottlenecks, and bandwidth (E) affects network I/O, not spill behavior. Therefore, the correct actions are A and D.


Question 6

The Databricks workspace administrator has configured interactive clusters for each of the data engineering groups. To control costs, clusters are set to terminate after 30 minutes of inactivity. Each user should be able to execute workloads against their assigned clusters at any time of the day.

Assuming users have been added to a workspace but not granted any permissions, which of the following describes the minimal permissions a user would need to start and attach to an already configured cluster.



Answer : D

https://learn.microsoft.com/en-us/azure/databricks/security/auth-authz/access-control/cluster-acl

https://docs.databricks.com/en/security/auth-authz/access-control/cluster-acl.html


Question 7

A data engineer is configuring a Databricks Asset Bundle to deploy a job with granular permissions. The requirements are:

* Grant the data-engineers group CAN_MANAGE access to the job.

* Ensure the auditors' group can view the job but not modify/run it.

* Avoid granting unintended permissions to other users/groups.

How should the data engineer deploy the job while meeting the requirements?



Answer : D

Databricks Asset Bundles (DABs) allow jobs, clusters, and permissions to be defined as code in YAML configuration files. According to the Databricks documentation on job permissions and bundle deployment, when defining permissions within a job resource, they must be scoped directly under that specific job's definition. This ensures that permissions are applied only to the intended job resource and not inadvertently propagated to other jobs or resources.

In this scenario, the data engineer must grant the data-engineers group CAN_MANAGE access, allowing them to configure, edit, and manage the job, while the auditors group should only have CAN_VIEW, giving them read-only access to see configurations and results without the ability to modify or execute. Importantly, no additional groups should be granted permissions, in order to follow the principle of least privilege.

Options A and B introduce unnecessary or unintended groups (like admin-team in A) or define permissions outside of the job scope (as in B). Option C improperly separates the permissions block outside the job resource, which is not aligned with Databricks bundle best practices.

Option D is the correct approach because it defines the job resource my-job with its name, tasks, clusters, and the exact intended permissions (CAN_MANAGE for data-engineers and CAN_VIEW for auditors). This aligns with Databricks' principle of least privilege and ensures compliance with governance standards in Unity Catalog-enabled workspaces.


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