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

Section	Weight	Objectives
Monitoring and Troubleshooting	16%	- Performance Optimization - Troubleshooting - Monitoring
Data Modeling and Storage	20%	- Data Modeling - File Formats - Storage Optimization
Data Processing	28%	- Structured Streaming - Data Transformation - Spark SQL - ETL Pipelines
Data Quality and Governance	12%	- Governance - Data Lineage - Data Quality
Databricks Lakehouse Platform	24%	- Data Management - Lakehouse Architecture - Delta Lake - Unity Catalog

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Databricks Certified Data Engineer Professional Exam Sample Questions (Q87-Q92):

NEW QUESTION # 87
A data engineer is tasked with ensuring that a Delta table in Databricks continuously retains deleted files for 15 days (instead of the default 7 days), in order to permanently comply with the organization's data retention policy. Which code snippet correctly sets this retention period for deleted files?

A. from delta.tables import *
deltaTable = DeltaTable.forPath(spark, "/mnt/data/my_table")
deltaTable.deletedFileRetentionDuration = "interval 15 days"
B. spark.conf.set("spark.databricks.delta.deletedFileRetentionDuration", "15 days")
C. spark.sql("ALTER TABLE my_table SET TBLPROPERTIES
('delta.deletedFileRetentionDuration' = 'interval 15 days')")
D. spark.sql("VACUUM my_table RETAIN 15 HOURS")

Answer: C

Explanation:
The deleted file retention period in Delta Lake is controlled by the table property delta.deletedFileRetentionDuration. Setting this property via ALTER TABLE ensures the retention policy is persistently enforced at the table level, extending deleted file retention to 15 days in compliance with organizational requirements.

NEW QUESTION # 88
Review the following error traceback:
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Which statement describes the error being raised?

A. There is a type error because a DataFrame object cannot be multiplied.
B. There is a syntax error because the heartrate column is not correctly identified as a column.
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: E

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

NEW QUESTION # 89
A data pipeline uses Structured Streaming to ingest data from kafka to Delta Lake. Data is being stored in a bronze table, and includes the Kafka_generated timesamp, key, and value. Three months after the pipeline is deployed the data engineering team has noticed some latency issued during certain times of the day.
A senior data engineer updates the Delta Table's schema and ingestion logic to include the current timestamp (as recoded by Apache Spark) as well the Kafka topic and partition. The team plans to use the additional metadata fields to diagnose the transient processing delays.
Which limitation will the team face while diagnosing this problem?

A. New fields cannot be added to a production Delta table.
B. Updating the table schema requires a default value provided for each file added.
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C. Spark cannot capture the topic partition fields from the kafka source.
D. New fields not be computed for historic records.
E. Updating the table schema will invalidate the Delta transaction log metadata.

Answer: D

Explanation:
When adding new fields to a Delta table's schema, these fields will not be retrospectively applied to historical records that were ingested before the schema change. Consequently, while the team can use the new metadata fields to investigate transient processing delays moving forward, they will be unable to apply this diagnostic approach to past data that lacks these fields.

NEW QUESTION # 90
A data engineer is designing a system to process batch patient encounter data stored in an S3 bucket, creating a Delta table (patient_encounters) with columns encounter_id, patient_id, encounter_date, diagnosis_code, and treatment_cost. The table is queried frequently by patient_id and encounter_date, requiring fast performance. Fine-grained access controls must be enforced. The engineer wants to minimize maintenance and boost performance. How should the data engineer create the patient_encounters table?

A. Create a managed table in Unity Catalog. Configure Unity Catalog permissions for access controls, schedule jobs to run OPTIMIZE and VACUUM commands daily to achieve best performance.
B. Create a managed table in Unity Catalog. Configure Unity Catalog permissions for access controls, and rely on predictive optimization to enhance query performance and simplify maintenance.
C. Create a managed table in Hive Metastore. Configure Hive Metastore permissions for access controls, and rely on predictive optimization to enhance query performance and simplify maintenance.
D. Create an external table in Unity Catalog, specifying an S3 location for the data files. Enable predictive optimization through table properties, and configure Unity Catalog permissions for access controls.

Answer: B

Explanation:
Databricks documentation specifies that Unity Catalog managed tables are the preferred choice for secure, low-maintenance Delta Lake architectures. Managed tables provide full lifecycle management, including metadata, file storage, and access control integration with Unity Catalog.
Fine-grained permissions can be enforced at the column and row level through built-in Unity Catalog governance.
Additionally, Predictive Optimization (Auto Optimize + Auto Compaction) automatically manages file sizes, metadata pruning, and layout optimization, eliminating the need for manual maintenance such as scheduling OPTIMIZE or VACUUM.
External tables (A) require manual path management, and Hive Metastore tables (D) do not support Unity Catalog access policies. Therefore, creating a managed Unity Catalog table with predictive optimization provides both the security and performance benefits needed, making B the correct solution.

NEW QUESTION # 91
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?
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A. Other than the default "admins" group, only individual users can be granted privileges on jobs.
B. The creator of a Databricks job will always have "Owner" privileges; this configuration cannot be changed.
C. A user can only transfer job ownership to a group if they are also a member of that group.
D. Databricks jobs must have exactly one owner; "Owner" privileges cannot be assigned to a group.
E. Only workspace administrators can grant "Owner" privileges to a group.

Answer: D

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

NEW QUESTION # 92
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