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

Section	Objectives
Topic 1: Production Pipelines and Orchestration	- Automate ETL pipelines and scheduling - Build and manage workflows using Databricks Jobs - Pipeline reliability and fault tolerance
Topic 2: Data Modeling and Storage	- Schema evolution and data partitioning strategies - Design scalable data lakehouse architectures - Delta Lake table design and optimization
Topic 3: Data Ingestion and Transformation	- Ingest data using Apache Spark and Databricks - Transform and clean datasets using Spark SQL and DataFrame APIs - Handle batch and streaming data pipelines
Topic 4: Security, Governance, Monitoring, and Optimization	- Implement Unity Catalog governance and access control - Cost optimization and performance tuning - Monitor and optimize Spark workloads

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Databricks Certified Professional Data Engineer Exam Sample Questions (Q140-Q145):

NEW QUESTION # 140
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.sql("VACUUM my_table RETAIN 15 HOURS")
C. spark.sql("ALTER TABLE my_table SET TBLPROPERTIES ('delta.deletedFileRetentionDuration' = 'interval 15 days')")
D. spark.conf.set("spark.databricks.delta.deletedFileRetentionDuration", "15 days")

Answer: C

Explanation:
Comprehensive and Detailed
In Delta Lake, the property delta.deletedFileRetentionDuration controls how long deleted data files are retained before being permanently removed during a VACUUM operation.
By default, this retention duration is set to 7 days.
To comply with stricter retention requirements, organizations can explicitly update the table property using an ALTER TABLE statement.
Option A uses the correct SQL command:
ALTER TABLE my_table SET TBLPROPERTIES ('delta.deletedFileRetentionDuration' = 'interval 15 days') This updates the Delta table metadata so that all future operations respect the 15-day retention policy for deleted files.
Why not the others?
B: This code incorrectly tries to set the property via the DeltaTable API. Delta's Python API does not expose direct attributes like deletedFileRetentionDuration; instead, properties must be set through ALTER TABLE or DataFrameWriter options.
C: VACUUM ... RETAIN specifies a one-time file cleanup action (e.g., retaining 15 hours of history), not a persistent retention policy. It cannot be used to set a continuous retention duration.
D: Setting spark.conf applies a session-level configuration and does not permanently update the table's retention metadata. Once the session ends, this configuration is lost.
Therefore, Option A is the correct and documented approach for persistently enforcing a 15-day deleted file retention period in Delta Lake.

NEW QUESTION # 141
A task orchestrator has been configured to run two hourly tasks. First, an outside system writes Parquet data to a directory mounted at /mnt/raw_orders/. After this data is written, a Databricks job containing the following code is executed:
(spark.readStream
.format("parquet")
.load("/mnt/raw_orders/")
.withWatermark("time", "2 hours")
.dropDuplicates(["customer_id", "order_id"])
.writeStream
.trigger(once=True)
.table("orders")
)
Assume that the fields customer_id and order_id serve as a composite key to uniquely identify each order, and that the time field indicates when the record was queued in the source system. If the upstream system is known to occasionally enqueue duplicate entries for a single order hours apart, which statement is correct?

A. The orders table will not contain duplicates, but records arriving more than 2 hours late will be ignored and missing from the table.
B. The orders table will contain only the most recent 2 hours of records and no duplicates will be present.
C. Duplicate records enqueued more than 2 hours apart may be retained and the orders table may contain duplicate records with the same customer_id and order_id.
D. All records will be held in the state store for 2 hours before being deduplicated and committed to the orders table.

Answer: A

Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
Exact extract: "dropDuplicates with watermark performs stateful deduplication on the keys within the watermark delay." Exact extract: "Records older than the event-time watermark are considered late and may be dropped." Exact extract: "trigger(once) processes all available data once and then stops."

NEW QUESTION # 142
Which of the following data workloads will utilize a Silver table as its source?

A. A job that queries aggregated data that already feeds into a dashboard
B. A job that cleans data by removing malformatted records
C. A job that ingests raw data from a streaming source into the Lakehouse
D. A job that enriches data by parsing its timestamps into a human-readable format
E. A job that aggregates cleaned data to create standard summary statistics

Answer: E

NEW QUESTION # 143
Which method can be used to determine the total wall-clock time it took to execute a query?

A. In the Spark UI, take the sum of all task durations that ran across all stages for all jobs associated with that query.
B. In the Spark UI, take the job duration of the longest-running job associated with that query.
C. Open the Query Profiler associated with that query and use the Total wall-clock duration metric.
D. Open the Query Profiler associated with that query and use the Aggregated task time metric.

Answer: C

Explanation:
The Query Profiler in Databricks SQL and notebooks provides a detailed breakdown of query performance metrics. The "Total wall-clock duration" metric directly represents the total elapsed time from query start to completion, including all execution, planning, and waiting stages. In contrast, "Aggregated task time" reflects the cumulative duration across all parallel tasks, which does not equal the total elapsed wall time since tasks often run concurrently. Using job duration from Spark UI can underestimate or overestimate runtime when queries span multiple jobs. Therefore, the Query Profiler's total wall-clock duration is the officially documented method to determine actual query execution time.

NEW QUESTION # 144
Which statement regarding spark configuration on the Databricks platform is true?

A. Spark configuration set within an notebook will affect all SparkSession attached to the same interactive cluster
B. The Databricks REST API can be used to modify the Spark configuration properties for an interactive cluster without interrupting jobs.
C. Spark configuration properties set for an interactive cluster with the Clusters UI will impact all notebooks attached to that cluster.
D. When the same spar configuration property is set for an interactive to the same interactive cluster.

Answer: C

Explanation:
When Spark configuration properties are set for an interactive cluster using the Clusters UI in Databricks, those configurations are applied at the cluster level. This means that all notebooks attached to that cluster will inherit and be affected by these configurations. This approach ensures consistency across all executions within that cluster, as the Spark configuration properties dictate aspects such as memory allocation, number of executors, and other vital execution parameters. This centralized configuration management helps maintain standardized execution environments across different notebooks, aiding in debugging and performance optimization.
:
Databricks documentation on configuring clusters: https://docs.databricks.com/clusters/configure.html

NEW QUESTION # 145
......

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