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

Section	Objectives
Topic 1: Production Pipelines and Orchestration	- Job scheduling and monitoring - Error handling and recovery strategies - Databricks Workflows
Topic 2: Data Ingestion and Processing	- Batch and streaming ingestion with Auto Loader - ETL pipeline design patterns - Structured Streaming fundamentals
Topic 3: Databricks Lakehouse Platform Architecture	- Data governance concepts (Unity Catalog basics) - Workspace and cluster architecture - Medallion architecture (Bronze, Silver, Gold)
Topic 4: Data Modeling and Transformation	- Performance optimization techniques - Spark SQL transformations - Dimensional modeling concepts
Topic 5: Delta Lake and Data Management	- Time travel and versioning - Schema evolution and enforcement - Delta Lake transactions and ACID properties

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Databricks Certified Data Engineer Professional Exam Sample Questions (Q141-Q146):

NEW QUESTION # 141
A nightly job ingests data into a Delta Lake table using the following code:

The next step in the pipeline requires a function that returns an object that can be used to manipulate new records that have not yet been processed to the next table in the pipeline.
Which code snippet completes this function definition?
def new_records():

A. return spark.readStream.table("bronze")
B.
C. return spark.read.option("readChangeFeed", "true").table ("bronze")
D.
E. return spark.readStream.load("bronze")

Answer: D

Explanation:
https://docs.databricks.com/en/delta/delta-change-data-feed.html

NEW QUESTION # 142
A data engineer is using Structured Streaming to read in transaction data from a bronze Delta table. It was discovered that the data has quality issues where sometimes the transaction value is negative, and when that occurs, the rows need to be routed to a separate quarantine table. They have low latency requirements for the good data since it is used by downstream systems, but the bad data will only be analyzed periodically and has no production dependencies. The quarantine job needs to be implemented so that it cannot affect the production processes that depend on the good data, and the cost of the job needs to be minimized. How should the quarantine process be implemented in order to satisfy these requirements?

A. The existing streaming job for the good data should be updated to incorporate the quarantining of the bad data. Inside a foreachBatch function, the dataframe should be filtered so that records with a transaction value greater than or equal to 0 are written to the good data table and records with a transaction value less than 0 are written to a quarantine table. Try/Catch can be added around the writes in the foreachBatch function so that the stream can't fail.
B. The streaming job for the good data needs to be modified to filter out records with a transaction value less than 0 before writing. The streaming job for the quarantine data needs to filter out records with a transaction value greater than or equal to 0 before writing. Both should run as separate streams on the same cluster to minimize cost.
C. The streaming job for the good data needs to be modified to filter out records with a transaction value less than 0 before writing, and should not share compute with other processes. The streaming job for the quarantine data needs to filter out records with a transaction value greater than or equal to 0 before writing, and should be implemented on a separate small cluster and only run once a day to minimize cost.
D. The existing streaming job for the good data should be updated to incorporate the quarantining of the bad data. A new boolean column called "quarantine" should be added to the dataframe, and its value should be set to true if the transaction value is less than 0 and false if the transaction value is greater than or equal to 0. Processing and storing all the data together will save costs.

Answer: C

Explanation:
Separating the quarantine processing from the low-latency production stream ensures the good- data pipeline cannot be slowed down or destabilized by quarantine writes or failures. Filtering out negative values in the production stream preserves latency and reliability for downstream systems. Running the quarantine stream on a small, independent cluster on a daily schedule minimizes cost while still capturing the bad records for periodic analysis, and it fully isolates quarantine operations from production dependencies.

NEW QUESTION # 143
A junior data engineer has manually configured a series of jobs using the Databricks Jobs UI.
Upon reviewing their work, the engineer realizes that they are listed as the "Owner" for each job.
They attempt to transfer "Owner" privileges to the "DevOps" group, but cannot successfully accomplish this task.
Which statement explains what is preventing this privilege transfer?
Get Latest & Actual Certified-Data-Engineer-Professional Exam's Question and Answers from

A. Only workspace administrators can grant "Owner" privileges to a group.
B. Databricks jobs must have exactly one owner; "Owner" privileges cannot be assigned to a group.
C. A user can only transfer job ownership to a group if they are also a member of that group.
D. Other than the default "admins" group, only individual users can be granted privileges on jobs.
E. The creator of a Databricks job will always have "Owner" privileges; this configuration cannot be changed.

Answer: B

Explanation:
A job cannot have more than one owner. A job cannot have a group as an owner.

NEW QUESTION # 144
A data engineer is working in an interactive notebook with many transformations before outputting the result from display(df.collect() ). The notebook includes wide transformations and a cross join.
The data engineer is getting the following error: "The spark driver has stopped unexpectedly and is restarting. Your notebook will be automatically reattached." Which action should the data engineer take?

A. Look at the compute metrics UI to see if the executors have higher than 90% memory utilization.
B. Check into the Spark UI to see how many jobs are assigned to each stage as they are employing fewer executors.
C. Run the notebook on a single node cluster to keep driver from falling.
D. Rewrite their code to avoid putting memory pressure on the driver node.

Answer: D

Explanation:
Calling df.collect() on a large DataFrame forces all data to be loaded into the driver's memory.
With wide transformations and a cross join, this can easily exceed the driver's capacity, causing it to crash. The data engineer should rewrite the code to avoid collecting large datasets on the driver, using operations like display(df) or writing to storage instead.

NEW QUESTION # 145
The data governance team is reviewing user for deleting records for compliance with GDPR. The following logic has been implemented to propagate deleted requests from the user_lookup table to the user aggregate table.

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

A. No; the change data feed only tracks inserts and updates not deleted records.
B. Yes; the change data feed uses foreign keys to ensure delete consistency throughout the Lakehouse.
C. No; the Delta Lake DELETE command only provides ACID guarantees when combined with the MERGE INTO command
D. No; files containing deleted records may still be accessible with time travel until a BACUM command is used to remove invalidated data files.
E. Yes; Delta Lake ACID guarantees provide assurance that the DELETE command successed fully and permanently purged these records.

Answer: D

Explanation:
The DELETE operation in Delta Lake is ACID compliant, which means that once the operation is successful, the records are logically removed from the table. However, the underlying files that contained these records may still exist and be accessible via time travel to older versions of the table. To ensure that these records are physically removed and compliance with GDPR is maintained, a VACUUM command should be used to clean up these data files after a certain retention period. The VACUUM command will remove the files from the storage layer, and after this, the records will no longer be accessible.

NEW QUESTION # 146
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