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

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

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Databricks Certified Data Engineer Professional Exam Sample Questions (Q182-Q187):

NEW QUESTION # 182
An external object storage container has been mounted to the location /mnt/finance_eda_bucket.
The following logic was executed to create a database for the finance team:

After the database was successfully created and permissions configured, a member of the finance team runs the following code:

If all users on the finance team are members of the finance group, which statement describes how the tx_sales table will be created?

A. A logical table will persist the query plan to the Hive Metastore in the Databricks control plane.
B. A managed table will be created in the DBFS root storage container.
C. An managed table will be created in the storage container mounted to /mnt/finance_eda_bucket.
D. A logical table will persist the physical plan to the Hive Metastore in the Databricks control plane.
E. An external table will be created in the storage container mounted to /mnt/finance eda bucket.

Answer: C

Explanation:
https://docs.databricks.com/en/data-governance/unity-catalog/create-schemas.html#language- SQL

NEW QUESTION # 183
A junior data engineer has been asked to develop a streaming data pipeline with a grouped aggregation using DataFrame df. The pipeline needs to calculate the average humidity and average temperature for each non-overlapping five-minute interval. Events are recorded once per minute per device.
Streaming DataFrame df has the following schema:
"device_id INT, event_time TIMESTAMP, temp FLOAT, humidity FLOAT"
Code block:

Choose the response that correctly fills in the blank within the code block to complete this task.

A. lag("event_time", "10 minutes").alias("time")
B. window("event_time", "5 minutes").alias("time")
C. window("event_time", "10 minutes").alias("time")
D. "event_time"
E. to_interval("event_time", "5 minutes").alias("time")

Answer: B

Explanation:
This is the correct answer because the window function is used to group streaming data by time intervals. The window function takes two arguments: a time column and a window duration. The window duration specifies how long each window is, and must be a multiple of 1 second. In this case, the window duration is "5 minutes", which means each window will cover a non-overlapping five- minute interval. The window function also returns a struct column with two fields: start and end, which represent the start and end time of each window. The alias function is used to rename the struct column as "time".

NEW QUESTION # 184
The Databricks workspace administrator has configured interactive clusters for each of the data engineering groups. To control costs, clusters are set to terminate after 30 minutes of inactivity.
Each user should be able to execute workloads against their assigned clusters at any time of the day.
Assuming users have been added to a workspace but not granted any permissions, which of the following describes the minimal permissions a user would need to start and attach to an already configured cluster.

A. Cluster creation allowed. "Can Restart" privileges on the required cluster
B. Cluster creation allowed. "Can Attach To" privileges on the required cluster
C. "Can Manage" privileges on the required cluster
D. Workspace Admin privileges, cluster creation allowed. "Can Attach To" privileges on the required cluster
E. "Can Restart" privileges on the required cluster

Answer: E

Explanation:
https://learn.microsoft.com/en-us/azure/databricks/security/auth-authz/access-control/cluster-acl
https://docs.databricks.com/en/security/auth-authz/access-control/cluster-acl.html

NEW QUESTION # 185
A data engineer inherits a Delta table with historical partitions by country that are badly skewed.
Queries often filter by high-cardinality customer_id and vary across dimensions over time. The engineer wants a strategy that avoids a disruptive full rewrite, reduces sensitivity to skewed partitions, and sustains strong query performance as access patterns evolve. Which two actions should the data engineer take? (Choose two.)

A. Periodically run OPTIMIZE table_name.
B. Keep existing partitions and rely on bin-packing OPTIMIZE only; ZORDER and clustering are unnecessary for multi-dimensional filters.
C. Switch from static partitioning to liquid clustering and select initial clustering keys that reflect common filters such as customer_id.
D. Disable data skipping statistics to avoid maintenance overhead; rely on adaptive query execution instead.
E. Depend solely on optimized writes; Databricks will automatically replace partitioning with clustering over time.

Answer: A,C

Explanation:
Liquid Clustering replaces traditional partitioning and ZORDER optimization by automatically organizing data according to clustering keys. It supports evolving clustering strategies without requiring a full table rewrite. To maintain cluster balance and improve performance, the OPTIMIZE command should be run periodically. OPTIMIZE groups data files by clustering keys and helps reduce small file overhead.

NEW QUESTION # 186
A data engineer is designing a pipeline in Databricks that processes records from a Kafka stream where late-arriving data is common. Which approach should the data engineer use?

A. Use batch processing and overwrite the entire output table each time to ensure late data is incorporated correctly.
B. Implement a custom solution using Databricks Jobs to periodically reprocess all historical data.
C. Use an Auto CDC pipeline with batch tables to simplify late data handling.
D. Use a watermark to specify the allowed lateness to accommodate records that arrive after their expected window, ensuring correct aggregation and state management.

Answer: D

Explanation:
In Structured Streaming, event-time watermarks control how long the engine waits for late- arriving data before finalizing aggregations. By setting an appropriate watermark, Databricks can handle late data gracefully -- incorporating records that arrive within the defined window while discarding excessively delayed events.
This approach ensures accurate aggregations, minimizes state size, and prevents memory leaks.
Manual reprocessing (A) or overwriting entire datasets (B) is inefficient and costly, while Auto CDC (C) is used for change tracking in Delta tables, not for streaming event lateness.
Thus, using watermarking is the recommended and official approach for managing late data in streaming pipelines.

NEW QUESTION # 187
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