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

Section	Weight	Objectives
Topic 1: Data Warehouse and Lakehouse Architecture	15-20%	- Lakehouse architecture principles 1. Data governance fundamentals 2. Differences between data lake, data warehouse, and lakehouse 3. Bronze, silver, gold data layers
Topic 2: Data Ingestion	15-20%	- Batch ingestion methods 1. Integration with external systems 2. DBR autoloader 3. Spark APIs for ingestion - Streaming ingestion 1. Structured streaming fundamentals 2. Kafka integration
Topic 3: Pipeline Development and Orchestration	10-15%	- Databricks workflows 1. Task dependencies and orchestration 2. Jobs and job scheduling 3. Monitoring and alerting
Topic 4: Data Processing with Spark	25-30%	- Python and SQL for data engineering 1. Spark APIs in Python 2. Performance optimization techniques 3. Built-in and user-defined functions - Spark DataFrames and Spark SQL 1. Spark SQL queries and functions 2. Window functions 3. DataFrame operations and transformations
Topic 5: Delta Lake	20-25%	- Delta Lake operations 1. Delta Live Tables 2. Merge, update, delete operations 3. Schema evolution and enforcement - Delta Lake fundamentals 1. Time travel and data versioning 2. ACID transactions 3. Optimize and Z-order

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Databricks Certified Professional Data Engineer Exam Sample Questions (Q168-Q173):

NEW QUESTION # 168
A data engineer is using Lakeflow Declarative Pipeline to propagate row deletions from a source bronze table (user_bronze) to a target silver table (user_silver) . The engineer wants deletions in user_bronze to automatically delete corresponding rows in user_silver during pipeline execution.
Which configuration ensures deletions in the bronze table are propagated to the silver table?

A. Enable CDF on user_silver, read its transaction log, and use MERGE to sync deletions.
B. Configure VACUUM on user_bronze to delete files, then rebuild user_silver from scratch.
C. Use apply_changes without CDF and filter rows where _soft_deleted is true.
D. Enable Change Data Feed (CDF) on user_bronze, read its CDF stream, and use apply_changes() with apply_as_deletes=True for user_silver.

Answer: D

Explanation:
According to Databricks documentation, Change Data Feed (CDF) allows pipelines to read incremental data changes, including inserts, updates, and deletes, from a Delta table. When deletions occur in the source table, reading the CDF stream ensures downstream consumers receive the deletion records. The Lakeflow Declarative Pipelines API provides the apply_changes() function (or auto-CDC pipelines) with the apply_as_deletes parameter to correctly apply those deletions to the target table. This enables automatic synchronization between bronze and silver layers. Options A and D either require manual handling or complete rebuilds, and C incorrectly applies CDF to the target rather than the source. Therefore, enabling CDF on the bronze table and using apply_as_deletes=True is the correct, Databricks-supported configuration.

NEW QUESTION # 169
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 not be computed for historic records.
B. Updating the table schema will invalidate the Delta transaction log metadata.
C. Updating the table schema requires a default value provided for each file added.
D. Spark cannot capture the topic partition fields from the kafka source.

Answer: A

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.
References:
* Databricks documentation on Delta Lake schema management: https://docs.databricks.com/delta/delta- batch.html#schema-management

NEW QUESTION # 170
A data engineer, while designing a Pandas UDF to process financial time-series data with complex calculations that require maintaining state across rows within each stock symbol group, must ensure the function is efficient and scalable.
Which approach will solve the problem with minimum overhead while preserving data integrity?

A. Use a SCALAR Pandas UDF that processes the entire dataset at once, implementing custom partitioning logic within the UDF to group by stock symbol and maintain state using global variables shared across all executor processes.
B. Use applyInPandas() on a Spark DataFrame that receives all rows for each stock symbol as a Pandas DataFrame, allowing processing within each group while maintaining state variables local to each group's processing function.
C. Use a SCALAR_ITER Pandas UDF with iterator-based processing, implementing state management through persistent storage (Delta tables) that gets updated after each batch to maintain continuity across iterator chunks.
D. Use a grouped_agg Pandas UDF that processes each stock symbol group independently, maintaining state through intermediate aggregation results that get passed between successive UDF calls via broadcast variables.

Answer: B

Explanation:
Comprehensive and Detailed Explanation From Exact Extract of Databricks Data Engineer Documents:
The Databricks documentation recommends applyInPandas() for complex per-group operations where maintaining internal state within each group is necessary. When using applyInPandas(), Spark provides all records for each grouping key as a Pandas DataFrame to the function, allowing efficient vectorized operations with local state management. This approach ensures high performance and scalability while maintaining logical isolation between groups. In contrast, SCALAR and SCALAR_ITER UDFs operate on individual rows or batches and cannot maintain inter-row state effectively. grouped_agg UDFs are limited to computing aggregates and do not support complex multi-row transformations. Therefore, applyInPandas() is the correct and Databricks-recommended solution for stateful per-group time-series computations.

NEW QUESTION # 171
Assuming that the Databricks CLI has been installed and configured correctly, which Databricks CLI command can be used to upload a custom Python Wheel to object storage mounted with the DBFS for use with a production job?

A. fs
B. workspace
C. libraries
D. jobs
E. configure

Answer: C

Explanation:
Explanation
The libraries command group allows you to install, uninstall, and list libraries on Databricks clusters. You can use the libraries install command to install a custom Python Wheel on a cluster by specifying the --whl option and the path to the wheel file. For example, you can use the following command to install a custom Python Wheel named mylib-0.1-py3-none-any.whl on a cluster with the id 1234-567890-abcde123:
databricks libraries install --cluster-id 1234-567890-abcde123 --whl
dbfs:/mnt/mylib/mylib-0.1-py3-none-any.whl
This will upload the custom Python Wheel to the cluster and make it available for use with a production job.
You can also use the libraries uninstall command to uninstall a library from a cluster, and the libraries list command to list the libraries installed on a cluster.
References:
Libraries CLI (legacy): https://docs.databricks.com/en/archive/dev-tools/cli/libraries-cli.html Library operations: https://docs.databricks.com/en/dev-tools/cli/commands.html#library-operations Install or update the Databricks CLI: https://docs.databricks.com/en/dev-tools/cli/install.html

NEW QUESTION # 172
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. "event_time"
B. window("event_time", "5 minutes").alias("time")
C. to_interval("event_time", "5 minutes").alias("time")
D. window("event_time", "10 minutes").alias("time")
E. lag("event_time", "10 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". Verified Reference: [Databricks Certified Data Engineer Professional], under "Structured Streaming" section; Databricks Documentation, under "WINDOW" section. https://www.databricks.com/blog/2017/05/08/event-time-aggregation-watermarking-apache-sparks-structured-streaming.html

NEW QUESTION # 173
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