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Databricks Databricks-Certified-Data-Engineer-Professional Exam Syllabus Topics:

Section	Weight	Objectives
Data Quality and Governance	12%	- Data Lineage - Data Quality - Governance
Data Modeling and Storage	20%	- File Formats - Data Modeling - Storage Optimization
Data Processing	28%	- Structured Streaming - Data Transformation - ETL Pipelines - Spark SQL
Databricks Lakehouse Platform	24%	- Delta Lake - Lakehouse Architecture - Unity Catalog - Data Management
Monitoring and Troubleshooting	16%	- Performance Optimization - Troubleshooting - Monitoring

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Databricks Certified Data Engineer Professional Exam Sample Questions (Q124-Q129):

NEW QUESTION # 124
A table named user_ltv is being used to create a view that will be used by data analysts on various teams. Users in the workspace are configured into groups, which are used for setting up data access using ACLs.
The user_ltv table has the following schema:
email STRING, age INT, ltv INT
The following view definition is executed:

An analyst who is not a member of the auditing group executes the following query:
SELECT * FROM user_ltv_no_minors
Which statement describes the results returned by this query?

A. All columns will be displayed normally for those records that have an age greater than 18; records not meeting this condition will be omitted.
B. All columns will be displayed normally for those records that have an age greater than 17; records not meeting this condition will be omitted.
C. All values for the age column will be returned as null values, all other columns will be returned with the values in user_ltv.
D. All records from all columns will be displayed with the values in user_ltv.
E. All age values less than 18 will be returned as null values all other columns will be returned with the values in user_ltv.

Answer: A

Explanation:
Get Latest & Actual Certified-Data-Engineer-Professional Exam's Question and Answers from Explanation:
Given the CASE statement in the view definition, the result set for a user not in the auditing group would be constrained by the ELSE condition, which filters out records based on age. Therefore, the view will return all columns normally for records with an age greater than 18, as users who are not in the auditing group will not satisfy the is_member('auditing') condition. Records not meeting the age > 18 condition will not be displayed.

NEW QUESTION # 125
A data engineer, User A, has promoted a new pipeline to production by using the REST API to programmatically create several jobs. A DevOps engineer, User B, has configured an external orchestration tool to trigger job runs through the REST API. Both users authorized the REST API calls using their personal access tokens.
Which statement describes the contents of the workspace audit logs concerning these events?

A. Because the REST API was used for job creation and triggering runs, a Service Principal will be automatically used to identity these events.
B. Because the REST API was used for job creation and triggering runs, user identity will not be captured in the audit logs.
C. Because User B last configured the jobs, their identity will be associated with both the job creation events and the job run events.
D. Because these events are managed separately, User A will have their identity associated with the job creation events and User B will have their identity associated with the job run events.
E. Because User A created the jobs, their identity will be associated with both the job creation events and the job run events.

Answer: D

Explanation:
The events are that a data engineer, User A, has promoted a new pipeline to production by using the REST API to programmatically create several jobs, and a DevOps engineer, User B, has configured an external orchestration tool to trigger job runs through the REST API. Both users authorized the REST API calls using their personal access tokens. The workspace audit logs are logs that record user activities in a Databricks workspace, such as creating, updating, or deleting objects like clusters, jobs, notebooks, or tables. The workspace audit logs also capture the identity of the user who performed each activity, as well as the time and details of the activity.
Because these events are managed separately, User A will have their identity associated with the job creation events and User B will have their identity associated with the job run events in the workspace audit logs.

NEW QUESTION # 126
A data engineer is creating a data ingestion pipeline to understand where customers are taking their rented bicycles during use. The engineer noticed that, over time, data being transmitted from the bicycle sensors fail to include key details like latitude and longitude. Downstream analysts need both the clean records and the quarantined records available for separate processing.
The data engineer already has this code:
import dlt
from pyspark.sql.functions import expr
rules = {
"valid_lat": "(lat IS NOT NULL)",
"valid_long": "(long IS NOT NULL)"
}
quarantine_rules = "NOT({})".format(" AND ".join(rules.values()))
@dlt.view
def raw_trips_data():
return spark.readStream.table("ride_and_go.telemetry.trips")
How should the data engineer meet the requirements to capture good and bad data?

A. @dlt.table(name="trips_data_quarantine")
def trips_data_quarantine():
return (
spark.readStream.table("raw_trips_data")
.filter(expr(quarantine_rules))
)
B. @dlt.table
@dlt.expect_all_or_drop(rules)
def trips_data_quarantine():
return spark.readStream.table("raw_trips_data")
C. @dlt.view
@dlt.expect_or_drop("lat_long_present", "(lat IS NOT NULL AND long IS NOT NULL)") def trips_data_quarantine():
return spark.readStream.table("ride_and_go.telemetry.trips")
D. @dlt.table(partition_cols=["is_quarantined", ])
@dlt.expect_all(rules)
def trips_data_quarantine():
return (
spark.readStream.table("raw_trips_data")
.withColumn("is_quarantined", expr(quarantine_rules))
)

Answer: A

Explanation:
The requirement is that both valid (good) and invalid (bad) records must be captured and available separately for downstream processing. Invalid records should not simply be dropped; they must be quarantined in a dedicated table.
In Databricks Lakeflow Declarative Pipelines (DLT), this is achieved by creating separate output tables:
One table for valid records (Silver table) that pass the expectations.
Another quarantine table that explicitly captures records failing the expectations.
Option A correctly implements this by:
Declaring a DLT table trips_data_quarantine.
Using .filter(expr(quarantine_rules)) to isolate invalid records (records where latitude or longitude is NULL).
This ensures analysts can query both good records (from the main Silver pipeline table) and bad records (from the quarantine table).

NEW QUESTION # 127
A junior data engineer has manually configured a series of jobs using the Databricks Jobs UI.
Upon reviewing their work, the engineer realizes that they are listed as the "Owner" for each job.
They attempt to transfer "Owner" privileges to the "DevOps" group, but cannot successfully accomplish this task.
Which statement explains what is preventing this privilege transfer?
Get Latest & Actual Certified-Data-Engineer-Professional Exam's Question and Answers from

A. Only workspace administrators can grant "Owner" privileges to a group.
B. A user can only transfer job ownership to a group if they are also a member of that group.
C. Other than the default "admins" group, only individual users can be granted privileges on jobs.
D. The creator of a Databricks job will always have "Owner" privileges; this configuration cannot be changed.
E. Databricks jobs must have exactly one owner; "Owner" privileges cannot be assigned to a group.

Answer: E

Explanation:
A job cannot have more than one owner. A job cannot have a group as an owner.

NEW QUESTION # 128
The Databricks CLI is use to trigger a run of an existing job by passing the job_id parameter. The response that the job run request has been submitted successfully includes a filed run_id.
Which statement describes what the number alongside this field represents?

A. The total number of jobs that have been run in the workspace.
B. The number of times the job definition has been run in the workspace.
C. The job_id and number of times the job has been are concatenated and returned.
D. The globally unique ID of the newly triggered run.
E. The job_id is returned in this field.

Answer: D

Explanation:
When triggering a job run using the Databricks CLI, the run_id field in the response represents a globally unique identifier for that particular run of the job. This run_id is distinct from the job_id.
While the job_id identifies the job definition and is constant across all runs of that job, the run_id is unique to each execution and is used to track and query the status of that specific job run within the Databricks environment. This distinction allows users to manage and reference individual executions of a job directly.

NEW QUESTION # 129
......

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