Databricks-Certified-Professional-Data-Engineer Fragen & Antworten & Databricks-Certified-Professional-Data-Engineer Studienführer & Databricks-Certified-Professional-Data-Engineer Prüfungsvorbereitung

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

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

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Databricks-Certified-Professional-Data-Engineer Exam Fragen & Databricks-Certified-Professional-Data-Engineer Examsfragen

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Databricks Certified Professional Data Engineer Exam Databricks-Certified-Professional-Data-Engineer Prüfungsfragen mit Lösungen (Q71-Q76):

71. Frage
A data engineer is designing a Lakeflow Declarative Pipeline to process streaming order data. The pipeline uses Auto Loader to ingest data and must enforce data quality by ensuring customer_id and amount are greater than zero. Invalid records should be dropped.
Which Lakeflow Declarative Pipelines configurations implement this requirement using Python?

A. @dlt.table
def silver_orders():
return (
dlt.read_stream( " bronze_orders " )
.expect_or_drop( " valid_customer " , " customer_id IS NOT NULL " )
.expect_or_drop( " valid_amount " , " amount > 0 " )
)
B. @dlt.table
@dlt.expect_or_drop( " valid_customer " , " customer_id IS NOT NULL " )
@dlt.expect_or_drop( " valid_amount " , " amount > 0 " )
def silver_orders():
return dlt.read_stream( " bronze_orders " )
C. @dlt.table
def silver_orders():
return (
dlt.read_stream( " bronze_orders " )
.expect( " valid_customer " , " customer_id IS NOT NULL " )
.expect( " valid_amount " , " amount > 0 " )
)
D. @dlt.table
@dlt.expect( " valid_customer " , " customer_id IS NOT NULL " )
@dlt.expect( " valid_amount " , " amount > 0 " )
def silver_orders():
return dlt.read_stream( " bronze_orders " )

Antwort: A

Begründung:
Lakeflow Declarative Pipelines (LDP), formerly Delta Live Tables (DLT), supports enforcing data quality using expectations . Expectations can either:
* Track violations (expect) # records that do not meet conditions are flagged but still included in the pipeline.
* Drop violations (expect_or_drop) # records that do not meet conditions are excluded from downstream tables.
* Fail pipeline on violations (expect_or_fail) # records that fail conditions stop the pipeline.
In this scenario, the requirement explicitly states that invalid records (where customer_id is null or amount #
0) must be dropped . According to the official documentation, the correct method is .expect_or_drop( " expectation_name " , " SQL_predicate " ) applied on the streaming input.
* Option A is correct: It uses .expect_or_drop directly within the transformation chain for both rules, ensuring records that fail are removed before writing to the silver table.
* Option B incorrectly uses @dlt.expect decorators, which only track violations but do not drop invalid rows.
* Option C uses .expect, which also only flags rows, not drop them.
* Option D uses @dlt.expect_or_drop decorator syntax, which is not supported in Python API; expect_or_drop must be applied as a method on the DataFrame, not as a decorator.
Therefore, the correct solution is Option A , which ensures compliance by enforcing data quality and dropping invalid rows programmatically during ingestion.
Reference: Databricks Lakeflow Declarative Pipelines Documentation - Expectations (expect, expect_or_drop, expect_or_fail)

72. Frage
The data engineering team has configured a Databricks SQL query and alert to monitor the values in a Delta Lake table. The recent_sensor_recordings table contains an identifying sensor_id alongside the timestamp and temperature for 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 when mean (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 recent_sensor_recordings table was unresponsive for three consecutive runs of the query
B. The maximum temperature recording for at least one sensor exceeded 120 on three consecutive executions of the query
C. The average temperature recordings for at least one sensor exceeded 120 on three consecutive executions of the query
D. The total average temperature across all sensors exceeded 120 on three consecutive executions of the query
E. The source query failed to update properly for three consecutive minutes and then restarted

Antwort: C

Begründung:
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.

73. Frage
Which of the following is the correct statement for a session scoped temporary view?

A. Temporary views stored in memory
B. Temporary views can be still accessed even if the notebook is detached and attached
C. Temporary views can be still accessed even if cluster is restarted
D. Temporary views are lost once the notebook is detached and re-attached
E. Temporary views are created in local_temp database

Antwort: D

Begründung:
Explanation
The answer is Temporary views are lost once the notebook is detached and attached There are two types of temporary views that can be created, Session scoped and Global
*A local/session scoped temporary view is only available with a spark session, so another notebook in the same cluster can not access it. if a notebook is detached and reattached local temporary view is lost.
*A global temporary view is available to all the notebooks in the cluster, if a cluster restarts global temporary view is lost.

74. Frage
You have written a notebook to generate a summary data set for reporting, Notebook was scheduled using the job cluster, but you realized it takes an average of 8 minutes to start the cluster, what feature can be used to start the cluster in a timely fashion?

A. Pin the cluster in the cluster UI page so it is always available to the jobs
B. Use Databricks Premium edition instead of Databricks standard edition
C. Setup an additional job to run ahead of the actual job so the cluster is running second job starts
D. Disable auto termination so the cluster is always running
E. Use the Databricks cluster pools feature to reduce the startup time

Antwort: E

Begründung:
Explanation
Cluster pools allow us to reserve VM's ahead of time, when a new job cluster is created VM are grabbed from the pool. Note: when the VM's are waiting to be used by the cluster only cost incurred is Azure. Databricks run time cost is only billed once VM is allocated to a cluster.
Here is a demo of how to setup and follow some best practices,
https://www.youtube.com/watch?v=FVtITxOabxg&ab_channel=DatabricksAcademy

75. Frage
To reduce storage and compute costs, the data engineering team has been tasked with curating a series of aggregate tables leveraged by business intelligence dashboards, customer-facing applications, production machine learning models, and ad hoc analytical queries.
The data engineering team has been made aware of new requirements from a customer-facing application, which is the only downstream workload they manage entirely. As a result, an aggregate table used by numerous teams across the organization will need to have a number of fields renamed, and additional fields will also be added.
Which of the solutions addresses the situation while minimally interrupting other teams in the organization without increasing the number of tables that need to be managed?

A. Replace the current table definition with a logical view defined with the query logic currently writing the aggregate table; create a new table to power the customer-facing application.
B. Configure a new table with all the requisite fields and new names and use this as the source for the customer-facing application; create a view that maintains the original data schema and table name by aliasing select fields from the new table.
C. Create a new table with the required schema and new fields and use Delta Lake's deep clone functionality to sync up changes committed to one table to the corresponding table.
D. Add a table comment warning all users that the table schema and field names will be changing on a given date; overwrite the table in place to the specifications of the customer-facing application.
E. Send all users notice that the schema for the table will be changing; include in the communication the logic necessary to revert the new table schema to match historic queries.

Antwort: B

Begründung:
Explanation
This is the correct answer because it addresses the situation while minimally interrupting other teams in the organization without increasing the number of tables that need to be managed. The situation is that an aggregate table used by numerous teams across the organization will need to have a number of fields renamed, and additional fields will also be added, due to new requirements from a customer-facing application. By configuring a new table with all the requisite fields and new names and using this as the source for the customer-facing application, the data engineering team can meet the new requirements without affecting other teams that rely on the existing table schema and name. By creating a view that maintains the original data schema and table name by aliasing select fields from the new table, the data engineering team can also avoid duplicating data or creating additional tables that need to be managed. Verified References: [Databricks Certified Data Engineer Professional], under "Lakehouse" section; Databricks Documentation, under
"CREATE VIEW" section.

76. Frage
......

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