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

Section	Objectives
Data Modeling and Transformation	- Performance optimization techniques - Dimensional modeling concepts - Spark SQL transformations
Delta Lake and Data Management	- Time travel and versioning - Schema evolution and enforcement - Delta Lake transactions and ACID properties
Databricks Lakehouse Platform Architecture	- Workspace and cluster architecture - Medallion architecture (Bronze, Silver, Gold) - Data governance concepts (Unity Catalog basics)
Production Pipelines and Orchestration	- Error handling and recovery strategies - Job scheduling and monitoring - Databricks Workflows
Data Ingestion and Processing	- ETL pipeline design patterns - Batch and streaming ingestion with Auto Loader - Structured Streaming fundamentals

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Databricks Certified Data Engineer Professional Exam Databricks-Certified-Data-Engineer-Professional Prüfungsfragen mit Lösungen (Q134-Q139):

134. Frage
Although the Databricks Utilities Secrets module provides tools to store sensitive credentials and avoid accidentally displaying them in plain text users should still be careful with which credentials are stored here and which users have access to using these secrets.
Which statement describes a limitation of Databricks Secrets?

A. Secrets are stored in an administrators-only table within the Hive Metastore; database administrators have permission to query this table by default.
B. Iterating through a stored secret and printing each character will display secret contents in plain text.
C. Account administrators can see all secrets in plain text by logging on to the Databricks Accounts console.
D. Because the SHA256 hash is used to obfuscate stored secrets, reversing this hash will display the value in plain text.
E. The Databricks REST API can be used to list secrets in plain text if the personal access token has proper credentials.

Antwort: E

Begründung:
This is the correct answer because it describes a limitation of Databricks Secrets. Databricks Secrets is a module that provides tools to store sensitive credentials and avoid accidentally displaying them in plain text. Databricks Secrets allows creating secret scopes, which are collections of secrets that can be accessed by users or groups. Databricks Secrets also allows creating and managing secrets using the Databricks CLI or the Databricks REST API. However, a limitation of Databricks Secrets is that the Databricks REST API can be used to list secrets in plain text if the personal access token has proper credentials. Therefore, users should still be careful with which credentials are stored in Databricks Secrets and which users have access to using these secrets.

135. Frage
A data engineering team uses Databricks Lakehouse Monitoring to track the percent_null metric for a critical column in their Delta table.
The profile metrics table (prod_catalog.prod_schema.customer_data_profile_metrics) stores hourly percent_null values.
The team wants to:
Trigger an alert when the daily average of percent_null exceeds 5% for
three consecutive days.
Ensure that notifications are not spammed during sustained issues.

A. SELECT percent_null
FROM prod_catalog.prod_schema.customer_data_profile_metrics
WHERE window.end >= CURRENT_TIMESTAMP - INTERVAL '1' DAY
Alert Condition: percent_null > 5
Notification Frequency: At most every 24 hours
B. SELECT AVG(percent_null) AS daily_avg
FROM prod_catalog.prod_schema.customer_data_profile_metrics
WHERE window.end >= CURRENT_TIMESTAMP - INTERVAL '3' DAY
Alert Condition: daily_avg > 5
Notification Frequency: Each time alert is evaluated
C. SELECT SUM(CASE WHEN percent_null > 5 THEN 1 ELSE 0 END) AS violation_days FROM prod_catalog.prod_schema.customer_data_profile_metrics WHERE window.end >= CURRENT_TIMESTAMP - INTERVAL '3' DAY Alert Condition: violation_days >= 3 Notification Frequency: Just once
D. WITH daily_avg AS (
SELECT DATE_TRUNC('DAY', window.end) AS day,
AVG(percent_null) AS avg_null
FROM prod_catalog.prod_schema.customer_data_profile_metrics
GROUP BY DATE_TRUNC('DAY', window.end)
)
SELECT day, avg_null
FROM daily_avg
ORDER BY day DESC
LIMIT 3
Alert Condition: ALL avg_null > 5 for the latest 3 rows
Notification Frequency: Just once

Antwort: D

Begründung:
The key requirement is to detect when the daily average of percent_null is greater than 5% for three consecutive days.
Option A only checks the last 24 hours, not consecutive days. It would trigger too frequently and cause spam.
Option C calculates an average across all records in the last 3 days, but this could be skewed by one high or low day -- it does not ensure consecutive daily violations.
Option D simply counts days where the threshold was exceeded, but it does not guarantee that those days were consecutive. This could incorrectly trigger on non-adjacent violations.
Option B is correct:
It aggregates hourly values into daily averages.
It checks that the last 3 consecutive days all had averages above 5%.
It avoids redundant alerts by using Notification Frequency: Just once.
This matches Databricks Lakehouse Monitoring best practices, where SQL alerts should be designed to aggregate metrics to the correct granularity (daily here) and ensure consecutive threshold violations before triggering.

136. Frage
Review the following error traceback:
Get Latest & Actual Certified-Data-Engineer-Professional Exam's Question and Answers from

Which statement describes the error being raised?

A. There is no column in the table named heartrateheartrateheartrate
B. There is a syntax error because the heartrate column is not correctly identified as a column.
C. The code executed was PvSoark but was executed in a Scala notebook.
D. There is a type error because a column object cannot be multiplied.
E. There is a type error because a DataFrame object cannot be multiplied.

Antwort: A

Begründung:
https://sparkbyexamples.com/spark/spark-cannot-resolve-given-input-columns/

137. Frage
A data architect is implementing Delta Sharing as part of their data governance strategy to enable secure data collaboration with external partners and internal business units. The architect must establish a permission framework that allows designated data stewards to create shares for their respective domains while maintaining security boundaries and audit compliance. Which specific permissions and roles must be assigned to enable users to create, configure, and manage Delta Shares while maintaining proper security governance and access controls?

A. Users need the MANAGE SHARES permission on the workspace
B. Only workspace admins can create and manage shares
C. Users need to be metastore admins or have CREATE SHARE privilege for the metastore
D. Any user with USE_CATALOG privilege can create shares

Antwort: C

Begründung:
Creating and managing Delta Shares requires elevated governance privileges at the metastore level. Assigning users as metastore admins or granting them the CREATE SHARE privilege allows designated data stewards to create and configure shares within defined security boundaries, while preserving centralized auditability and access control.

138. Frage
A data engineer is using Lakeflow Declarative Pipelines Expectations feature to track the data quality of their incoming sensor data. Periodically, sensors send bad readings that are out of range, and they are currently flagging those rows with a warning and writing them to the silver table along with the good data. They've been given a new requirement ?the bad rows need to be quarantined in a separate quarantine table and no longer included in the silver table.
This is the existing code for their silver table:
@dlt.table
@dlt.expect("valid_sensor_reading", "reading < 120")
def silver_sensor_readings():
return spark.readStream.table("bronze_sensor_readings")
What code will satisfy the requirements?

A. @dlt.table
@dlt.expect_or_drop("valid_sensor_reading", "reading < 120")
def silver_sensor_readings():
return spark.readStream.table("bronze_sensor_readings")
@dlt.table
@dlt.expect_or_drop("invalid_sensor_reading", "reading >= 120")
def quarantine_sensor_readings():
return spark.readStream.table("bronze_sensor_readings")
B. @dlt.table
@dlt.expect_or_drop("valid_sensor_reading", "reading < 120")
def silver_sensor_readings():
return spark.readStream.table("bronze_sensor_readings")
@dlt.table
@dlt.expect("invalid_sensor_reading", "reading >= 120")
def quarantine_sensor_readings():
return spark.readStream.table("bronze_sensor_readings")
C. @dlt.table
@dlt.expect("valid_sensor_reading", "reading < 120")
def silver_sensor_readings():
return spark.readStream.table("bronze_sensor_readings")
@dlt.table
@dlt.expect("invalid_sensor_reading", "reading >= 120")
def quarantine_sensor_readings():
return spark.readStream.table("bronze_sensor_readings")
D. @dlt.table
@dlt.expect_or_drop("valid_sensor_reading", "reading < 120")
def silver_sensor_readings():
return spark.readStream.table("bronze_sensor_readings")
@dlt.table
@dlt.expect("invalid_sensor_reading", "reading < 120")
def quarantine_sensor_readings():
return spark.readStream.table("bronze_sensor_readings")

Antwort: C

Begründung:
Lakeflow Declarative Pipelines (DLT) supports data quality enforcement using @dlt.expect,
@dlt.expect_or_drop, and @dlt.expect_all.
@dlt.expect applies a rule and records whether rows pass or fail the condition but does not drop failing rows. Instead, failing rows can be written to a quarantine table.
@dlt.expect_or_drop enforces that only rows passing the condition flow downstream, dropping bad records automatically.
In this case, the requirement is:
Good rows (reading < 120) go to the silver table.
Bad rows (reading >= 120) go to a quarantine table.
Bad rows should not be included in silver.
The correct implementation is Option A, where:
The silver table uses @dlt.expect to validate reading < 120. These rows flow normally.
The quarantine table applies an expectation for reading >= 120, ensuring bad records are captured separately.
Other options are incorrect:
Option B/D: These either use expect_or_drop incorrectly or apply wrong conditions, leading to dropped rows without quarantining properly.
Option C: Uses expect_or_drop for both tables, which would discard bad rows instead of persisting them into a quarantine table.
Thus, Option A meets the business requirement to split good and bad data streams while ensuring both are captured for auditing and processing.

139. Frage
......

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