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

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

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Databricks Certified Data Engineer Professional Exam Sample Questions (Q19-Q24):

NEW QUESTION # 19
A junior data engineer seeks to leverage Delta Lake's Change Data Feed functionality to create a Type 1 table representing all of the values that have ever been valid for all rows in a bronze table created with the property delta.enableChangeDataFeed = true. They plan to execute the following code as a daily job:
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Which statement describes the execution and results of running the above query multiple times?

A. Each time the job is executed, the target table will be overwritten using the entire history of inserted or updated records, giving the desired result.
B. Each time the job is executed, the differences between the original and current versions are calculated; this may result in duplicate entries for some records.
C. Each time the job is executed, newly updated records will be merged into the target table, overwriting previous values with the same primary keys.
D. Each time the job is executed, only those records that have been inserted or updated since the last execution will be appended to the target table giving the desired result.
E. Each time the job is executed, the entire available history of inserted or updated records will be appended to the target table, resulting in many duplicate entries.

Answer: E

Explanation:
Reading table's changes, captured by CDF, using spark.read means that you are reading them as a static source. So, each time you run the query, all table's changes (starting from the specified startingVersion) will be read.

NEW QUESTION # 20
A data engineer is troubleshooting a slow-running Delta Lake query on Databricks SQL involves complex joins and large datasets. They need to identify whether the root cause is related to poor data skipping, inefficient join strategies, or excessive data shuffling. Which approach should identify the specific bottlenecks using native Databricks tools?

A. Check the query's execution time in the Jobs UI and correlate it with cluster resource utilization metrics.
B. Analyze the Top Operators panel in the Query Profile to identify high-cost operations like BroadcastNestedLoopJoin
C. Enable the EXPLAIN command to review the parsed logical plan and manually estimate shuffle sizes.
D. Use the LIMIT clause to run a subset of the query and compare execution times with the full dataset.

Answer: B

Explanation:
The Query Profile's Top Operators panel surfaces the most expensive operators in the query execution, making it possible to directly identify bottlenecks such as inefficient join strategies, poor data skipping, or excessive shuffling. This native visualization highlights where time and resources are spent, enabling precise root-cause analysis for slow-running queries.

NEW QUESTION # 21
The business intelligence team has a dashboard configured to track various summary metrics for retail stories. This includes total sales for the previous day alongside totals and averages for a variety of time periods. The fields required to populate this dashboard have the following schema:

For Demand forecasting, the Lakehouse contains a validated table of all itemized sales updated incrementally in near real-time. This table named products_per_order, includes the following fields:

Because reporting on long-term sales trends is less volatile, analysts using the new dashboard only require data to be refreshed once daily. Because the dashboard will be queried interactively by many users throughout a normal business day, it should return results quickly and reduce total compute associated with each materialization.
Which solution meets the expectations of the end users while controlling and limiting possible costs?

A. Populate the dashboard by configuring a nightly batch job to save the required values as a table overwritten with each update.
B. Configure a webhook to execute an incremental read against products_per_order each time the dashboard is refreshed.
C. Use Structure Streaming to configure a live dashboard against the products_per_order table within a Databricks notebook.
D. Define a view against the products_per_order table and define the dashboard against this view.
E. Use the Delta Cache to persists the products_per_order table in memory to quickly the dashboard with each query.

Answer: A

NEW QUESTION # 22
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. Incremental state information should be maintained for 10 minutes for late-arriving data.
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. delayWrite("event_time", "10 minutes")
B. slidingWindow("event_time", "10 minutes")
C. await("event_time + `10 minutes'")
D. withWatermark("event_time", "10 minutes")
E. awaitArrival("event_time", "10 minutes")

Answer: D

Explanation:
This is because the question asks for incremental state information to be maintained for 10 minutes for late-arriving data. The withWatermark method is used to define the watermark for late data. The watermark is a timestamp column and a threshold that tells the system how long to wait for late data. In this case, the watermark is set to 10 minutes. The other options are incorrect because they are not valid methods or syntax for watermarking in Structured Streaming.

NEW QUESTION # 23
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. Updating the table schema requires a default value provided for each file added.
B. Spark cannot capture the topic partition fields from the kafka source.
C. Updating the table schema will invalidate the Delta transaction log metadata.
D. New fields will not be computed for historic records.
E. New fields cannot be added to a production Delta table.

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 # 24
......

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