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

Section	Weight	Objectives
Developing Code for Data Processing using Python and SQL	22%	- Data transformation and aggregation - Batch and incremental processing logic - Integration with Databricks APIs and tools
Data Governance	7%	- Data lineage and metadata tracking - Unity Catalog management - Policy enforcement
Debugging and Deploying	10%	- Deployment using bundles, CLI, and APIs - CI/CD and DevOps practices - Troubleshooting pipelines and errors
Monitoring and Alerting	10%	- Performance and health monitoring - Pipeline observability and logging - Setting up alerts and notifications
Data Transformation, Cleansing, and Quality	10%	- Handling missing or inconsistent data - Standardization and normalization - Data validation and quality checks
Data Modelling	6%	- Delta Lake table design - Medallion Architecture implementation - Schema design and management
Ensuring Data Security and Compliance	10%	- Data encryption and masking - Access control and permissions - Compliance standards implementation
Data Sharing and Federation	5%	- Unity Catalog data sharing - Cross-workspace and cross-cloud access
Cost & Performance Optimisation	13%	- Cluster configuration and scaling - Storage optimization (partitioning, Z-order, indexing) - Query optimization and caching
Data Ingestion & Acquisition	7%	- Schema inference and evolution - Auto Loader and streaming ingestion - Connecting to diverse data sources

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Databricks Certified Professional Data Engineer Exam Sample Questions (Q178-Q183):

NEW QUESTION # 178
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 total average temperature across all sensors exceeded 120 on three consecutive executions of the query
B. The recent_sensor_recordingstable was unresponsive for three consecutive runs of the query
C. The average temperature recordings for at least one sensor exceeded 120 on three consecutive executions of the query
D. The maximum temperature recording for at least one sensor exceeded 120 on three consecutive executions of the query
E. The source query failed to update properly for three consecutive minutes and then restarted

Answer: C

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 # 179
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( " valid_customer " , " customer_id IS NOT NULL " )
.expect( " valid_amount " , " amount > 0 " )
)
B. @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 " )
)
C. @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 " )
D. @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 " )

Answer: B

Explanation:
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)

NEW QUESTION # 180
A member of the data engineering team has submitted a short notebook that they wish to schedule as part of a larger data pipeline. Assume that the commands provided below produce the logically correct results when run as presented.

Which command should be removed from the notebook before scheduling it as a job?

A. Cmd 4
B. Cmd 6
C. Cmd 3
D. Cmd 2
E. Cmd 5

Answer: B

Explanation:
Cmd 6 is the command that should be removed from the notebook before scheduling it as a job. This command is selecting all the columns from the finalDF dataframe and displaying them in the notebook. This is not necessary for the job, as the finalDF dataframe is already written to a table in Cmd 7. Displaying the dataframe in the notebook will only consume resources and time, and it will not affect the output of the job. Therefore, Cmd 6 is redundant and should be removed.
The other commands are essential for the job, as they perform the following tasks:
Cmd 1: Reads the raw_data table into a Spark dataframe called rawDF.
Cmd 2: Prints the schema of the rawDF dataframe, which is useful for debugging and understanding the data structure.
Cmd 3: Selects all the columns from the rawDF dataframe, as well as the nested columns from the values struct column, and creates a new dataframe called flattenedDF.
Cmd 4: Drops the values column from the flattenedDF dataframe, as it is no longer needed after flattening, and creates a new dataframe called finalDF.
Cmd 5: Explains the physical plan of the finalDF dataframe, which is useful for optimizing and tuning the performance of the job.
Cmd 7: Writes the finalDF dataframe to a table called flat_data, using the append mode to add new data to the existing table.

NEW QUESTION # 181
While investigating a data issue in a Delta table, you wanted to review logs to see when and who updated the table, what is the best way to review this data?

A. Review workspace audit logs
B. Review event logs in the Workspace
C. Check Databricks SQL Audit logs
D. Run SQL SHOW HISTORY table_name
E. Run SQL command DESCRIBE HISTORY table_name

Answer: C

NEW QUESTION # 182
Review the following error traceback:
Which statement describes the error being raised?

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

Answer: A

Explanation:
The error being raised is an AnalysisException, which is a type of exception that occurs when Spark SQL cannot analyze or execute a query due to some logical or semantic error1. In this case, the error message indicates that the query cannot resolve the column name 'heartrateheartrateheartrate' given the input columns
'heartrate' and 'age'. This means that there is no column in the table named 'heartrateheartrateheartrate', and the query is invalid. A possible cause of this error is a typo or a copy-paste mistake in the query. To fix this error, the query should use a valid column name that exists in the table, such as
'heartrate'. References: AnalysisException

NEW QUESTION # 183
......

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