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

Section	Weight	Objectives
Topic 1: Data Ingestion	15-20%	- Streaming ingestion 1. Structured streaming fundamentals 2. Kafka integration - Batch ingestion methods 1. DBR autoloader 2. Integration with external systems 3. Spark APIs for ingestion
Topic 2: Data Warehouse and Lakehouse Architecture	15-20%	- Lakehouse architecture principles 1. Bronze, silver, gold data layers 2. Differences between data lake, data warehouse, and lakehouse 3. Data governance fundamentals
Topic 3: Pipeline Development and Orchestration	10-15%	- Databricks workflows 1. Monitoring and alerting 2. Task dependencies and orchestration 3. Jobs and job scheduling
Topic 4: Data Processing with Spark	25-30%	- Spark DataFrames and Spark SQL 1. DataFrame operations and transformations 2. Spark SQL queries and functions 3. Window functions - Python and SQL for data engineering 1. Built-in and user-defined functions 2. Spark APIs in Python 3. Performance optimization techniques
Topic 5: Delta Lake	20-25%	- Delta Lake operations 1. Delta Live Tables 2. Schema evolution and enforcement 3. Merge, update, delete operations - Delta Lake fundamentals 1. Time travel and data versioning 2. Optimize and Z-order 3. ACID transactions

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Databricks Certified Professional Data Engineer Exam Sample Questions (Q207-Q212):

NEW QUESTION # 207
The data governance team is reviewing code used for deleting records for compliance with GDPR. They note the following logic is used to delete records from the Delta Lake table named users.

Assuming that user_id is a unique identifying key and that delete_requests contains all users that have requested deletion, which statement describes whether successfully executing the above logic guarantees that the records to be deleted are no longer accessible and why?

A. No; the Delta Lake delete command only provides ACID guarantees when combined with the merge into command.
B. No; the Delta cache may return records from previous versions of the table until the cluster is restarted.
C. No; files containing deleted records may still be accessible with time travel until a vacuum command is used to remove invalidated data files.
D. Yes; Delta Lake ACID guarantees provide assurance that the delete command succeeded fully and permanently purged these records.
E. Yes; the Delta cache immediately updates to reflect the latest data files recorded to disk.

Answer: C

Explanation:
The code uses the DELETE FROM command to delete records from the users table that match a condition based on a join with another table called delete_requests, which contains all users that have requested deletion.
The DELETE FROM command deletes records from a Delta Lake table by creating a new version of the table that does not contain the deleted records. However, this does not guarantee that the records to be deleted are no longer accessible, because Delta Lake supports time travel, which allows querying previous versions of the table using a timestamp or version number. Therefore, files containing deleted records may still be accessible with time travel until a vacuum command is used to remove invalidated data files from physical storage.
Verified References: [Databricks Certified Data Engineer Professional], under "Delta Lake" section; Databricks Documentation, under "Delete from a table" section; Databricks Documentation, under "Remove files no longer referenced by a Delta table" section.

NEW QUESTION # 208
The data engineering team has configured a Databricks SQL query and alert to monitor the values in a Delta Lake table. Therecent_sensor_recordingstable contains an identifyingsensor_idalongside thetimestampandtemperaturefor the most recent 5 minutes of recordings.
The below query is used to create the alert:

The query is set to refresh each minute and always completes in less than 10 seconds. The alert is set to trigger whenmean (temperature) > 120. Notifications are triggered to be sent at most every 1 minute.
If this alert raises notifications for 3 consecutive minutes and then stops, which statement must be true?

A. The average temperature recordings for at least one sensor exceeded 120 on three consecutive executions of the query
B. The maximum temperature recording for at least one sensor exceeded 120 on three consecutive executions of the query
C. The source query failed to update properly for three consecutive minutes and then restarted
D. The total average temperature across all sensors exceeded 120 on three consecutive executions of the query
E. Therecent_sensor_recordingstable was unresponsive for three consecutive runs of the query

Answer: A

Explanation:
This is the correct answer because the query is using a GROUP BY clause on the sensor_id column, which means it will calculate the mean temperature for each sensor separately. The alert will trigger when the mean temperature for any sensor is greater than 120, which means at least one sensor had an average temperature above 120 for three consecutive minutes. The alert will stop when the mean temperature for all sensors drops below 120. Verified References: [Databricks Certified Data Engineer Professional], under "SQL Analytics" section; Databricks Documentation, under "Alerts" section.

NEW QUESTION # 209
The data science team has created and logged a production model using MLflow. The following code correctly imports and applies the production model to output the predictions as a new DataFrame named preds with the schema "customer_id LONG, predictions DOUBLE, date DATE".

The data science team would like predictions saved to a Delta Lake table with the ability to compare all predictions across time. Churn predictions will be made at most once per day.
Which code block accomplishes this task while minimizing potential compute costs?

A.
B.
C. preds.write.mode("append").saveAsTable("churn_preds")
D. preds.write.format("delta").save("/preds/churn_preds")
E.

Answer: C

NEW QUESTION # 210
A Data Engineer is building a simple data pipeline using Lakeflow Declarative Pipelines (LDP) in Databricks to ingest customer data. The raw customer data is stored in a cloud storage location in JSON format. The task is to create Lakeflow Declarative Pipelines that read the raw JSON data and write it into a Delta table for further processing.
Which code snippet will correctly ingest the raw JSON data and create a Delta table using LDP?

A. import dlt
@dlt.table
def raw_customers():
return spark.read.format( " csv " ).load( " s3://my-bucket/raw-customers/ " )
B. import dlt
@dlt.view
def raw_customers():
return spark.format.json( " s3://my-bucket/raw-customers/ " )
C. import dlt
@dlt.table
def raw_customers():
return spark.read.json( " s3://my-bucket/raw-customers/ " )
D. import dlt
@dlt.table
def raw_customers():
return spark.read.format( " parquet " ).load( " s3://my-bucket/raw-customers/ " )

Answer: C

Explanation:
The correct method to define a table using Lakeflow Declarative Pipelines (LDP) is with the @dlt.table decorator, which persists the output as a managed Delta table. When ingesting raw JSON data, spark.read.
json() or spark.read.format( " json " ).load() is the standard approach. This reads JSON-formatted files from the source and stores them in Delta format automatically managed by Databricks.
Reference Source: Databricks Lakeflow Declarative Pipelines Developer Guide - "Create tables from raw JSON and Delta sources."

NEW QUESTION # 211
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
@dlt.expect_all_or_drop(rules)
def trips_data_quarantine():
return spark.readStream.table("raw_trips_data")
B. @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))
)
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(name="trips_data_quarantine")
def trips_data_quarantine():
return (
spark.readStream.table("raw_trips_data")
.filter(expr(quarantine_rules))
)

Answer: D

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 # 212
......

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