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

Section	Objectives
Topic 1: Data Modeling and Storage	- Schema evolution and data partitioning strategies - Delta Lake table design and optimization - Design scalable data lakehouse architectures
Topic 2: Security, Governance, Monitoring, and Optimization	- Monitor and optimize Spark workloads - Cost optimization and performance tuning - Implement Unity Catalog governance and access control
Topic 3: Data Ingestion and Transformation	- Ingest data using Apache Spark and Databricks - Handle batch and streaming data pipelines - Transform and clean datasets using Spark SQL and DataFrame APIs
Topic 4: Production Pipelines and Orchestration	- Automate ETL pipelines and scheduling - Build and manage workflows using Databricks Jobs - Pipeline reliability and fault tolerance

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Databricks Certified Professional Data Engineer Exam Sample Questions (Q33-Q38):

NEW QUESTION # 33
A data architect has determined that a table of the following format is necessary:
Which of the following code blocks uses SQL DDL commands to create an empty Delta table in the above
format regardless of whether a table already exists with this name?

A. 1. CREATE OR REPLACE TABLE table_name
2. WITH COLUMNS ( id STRING, birthDate DATE, avgRating FLOAT ) USING DELTA
B. 1. CREATE OR REPLACE TABLE table_name ( id STRING, birthDate DATE, avgRating FLOAT )
C. 1. CREATE TABLE table_name AS
2. SELECT id STRING, birthDate DATE, avgRating FLOAT
D. 1. CREATE TABLE IF NOT EXISTS table_name ( id STRING, birthDate DATE, avgRating FLOAT )
E. 1. CREATE OR REPLACE TABLE table_name AS
2. SELECT id STRING, birthDate DATE, avgRating FLOAT USING DELTA

Answer: B

NEW QUESTION # 34
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.

A. Cluster creation allowed. "Can Restart" privileges on the required cluster
B. "Can Manage" privileges on the required cluster
C. Cluster creation allowed. "Can Attach To" privileges on the required cluster
D. Workspace Admin privileges, cluster creation allowed. "Can Attach To" privileges on the required cluster
E. "Can Restart" privileges on the required cluster

Answer: E

Explanation:
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

NEW QUESTION # 35
You are tasked to set up a set notebook as a job for six departments and each department can run the task parallelly, the notebook takes an input parameter dept number to process the data by department, how do you go about to setup this up in job?

A. Use a single notebook as task in the job and use dbutils.notebook.run to run each note-book with parameter in a different cell
B. A task in the job cannot take an input parameter, create six notebooks with hardcoded dept number and setup six tasks with linear dependency in the job
C. A parameter can only be passed at the job level, create six jobs pass department number to each job with no job dependency
D. A parameter can only be passed at the job level, create six jobs pass department number to each job with linear job dependency
E. A task accepts key-value pair parameters, creates six tasks pass department number as parameter foreach task with no dependency in the job as they can all run in parallel.
(Correct)

Answer: E

Explanation:
Explanation
Here is how you setup
Create a single job and six tasks with the same notebook and assign a different parameter for each task , Graphical user interface, text, application, email Description automatically generated

All tasks are added in a single job and can run parallel either using single shared cluster or with individual clusters.
Graphical user interface, application, Teams Description automatically generated

NEW QUESTION # 36
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(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))
)
B. @dlt.table(name="trips_data_quarantine")
def trips_data_quarantine():
return (
spark.readStream.table("raw_trips_data")
.filter(expr(quarantine_rules))
)
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
@dlt.expect_all_or_drop(rules)
def trips_data_quarantine():
return spark.readStream.table("raw_trips_data")

Answer: B

Explanation:
Comprehensive and Detailed
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).
Why not the others?
B: Uses @dlt.expect_or_drop, which drops invalid records instead of quarantining them. This violates the requirement that quarantined data should be available.
C: Same as B, but applies expectations in bulk with expect_all_or_drop. Again, bad data is dropped, not quarantined.
D: Adds an is_quarantined flag in the same table. While it marks bad records, it does not separate them into a distinct quarantine table as required by the business use case.
Therefore, Option A is the only solution aligned with Databricks documentation for quarantining invalid data into a dedicated table while keeping valid data in the main pipeline.

NEW QUESTION # 37
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. Unless all tasks complete successfully, no changes will be committed to the Lakehouse; because task A failed, all commits will be rolled back automatically.
C. Tasks B and C will be skipped; task A will not commit any changes because of stage failure.
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.

Answer: D

Explanation:
Explanation
When a Databricks job runs multiple tasks with dependencies, the tasks are executed in a dependency graph. If a task fails, the downstream tasks that depend on it are skipped and marked as Upstream failed. However, the failed task may have already committed some changes to the Lakehouse before the failure occurred, and those changes are not rolled back automatically. Therefore, the job run may result in a partial update of the Lakehouse. To avoid this, you can use the transactional writes feature of Delta Lake to ensure that the changes are only committed when the entire job run succeeds. Alternatively, you can use the Run if condition to configure tasks to run even when some or all of their dependencies have failed, allowing your job to recover from failures and continue running. References:
transactional writes: https://docs.databricks.com/delta/delta-intro.html#transactional-writes Run if: https://docs.databricks.com/en/workflows/jobs/conditional-tasks.html

NEW QUESTION # 38
......

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