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

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

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Databricks Certified Data Engineer Professional Exam Sample Questions (Q112-Q117):

NEW QUESTION # 112
A data engineer is working in an interactive notebook with many transformations before outputting the result from display(df.collect() ). The notebook includes wide transformations and a cross join.
The data engineer is getting the following error: "The spark driver has stopped unexpectedly and is restarting. Your notebook will be automatically reattached." Which action should the data engineer take?

A. Look at the compute metrics UI to see if the executors have higher than 90% memory utilization.
B. Rewrite their code to avoid putting memory pressure on the driver node.
C. Run the notebook on a single node cluster to keep driver from falling.
D. Check into the Spark UI to see how many jobs are assigned to each stage as they are employing fewer executors.

Answer: B

Explanation:
Calling df.collect() on a large DataFrame forces all data to be loaded into the driver's memory.
With wide transformations and a cross join, this can easily exceed the driver's capacity, causing it to crash. The data engineer should rewrite the code to avoid collecting large datasets on the driver, using operations like display(df) or writing to storage instead.

NEW QUESTION # 113
A job runs four independent tasks (X, Y, Z, W) in parallel to process regional sales data. The Data Engineering team recently updated its cluster policy to ban cost-prohibitive instance types. Task Y now fails due to the newly enforced cluster policy restricting the use of a specific instance type.
A data engineer needs to resolve the failure quickly without disrupting the other tasks. How should the data engineer resolve the failure of tasks?

A. Delete the failed run, disable the cluster policy, and re-execute all tasks.
B. Use "Repair run", override the cluster configuration for Task Y to use a permitted instance type, and let Databricks re-run only Task Y.
C. Edit the global cluster policy to allow the restricted instance type, then re-run the entire job.
D. Manually create a new cluster for Task Y, update the job configuration, and trigger a full re-run.

Answer: B

Explanation:
Repair run allows re-running only the failed task without affecting successfully completed tasks.
Overriding the cluster configuration for the specific task resolves the policy violation quickly while keeping the rest of the job intact and minimizing disruption.

NEW QUESTION # 114
A company has a task management system that tracks the most recent status of tasks. The system takes task events as input and processes events in near real-time using Lakeflow Declarative Pipelines. A new task event is ingested into the system when a task is created or the task status is changed. Lakeflow Declarative Pipelines provides a streaming table (tasks_status) for BI users to query.
The table represents the latest status of all tasks and includes 5 columns:
task_id (unique for each task)
task_name
task_owner
task_status
task_event_time
The table enables three properties: deletion vectors, row tracking, and change data feed (CDF).
A data engineer is asked to create a new Lakeflow Declarative Pipeline to enrich the tasks_status table in near real-time by adding one additional column representing task_owner's department, which can be looked up from a static dimension table (employee).
How should this enrichment be implemented?

A. Create a new Lakeflow Declarative Pipeline: use the read() function to read tasks_status table; enrich with employee table; store the result in a materialized view.
B. Create a new Lakeflow Declarative Pipeline: use the readStream() function to read tasks_status table; enrich with the employee table; store the result in a new streaming table.
C. Create a new Lakeflow Declarative Pipeline: use the readStream() function with the option skipChangeCommits to read the tasks_status table; enrich with the employee table; store the result in a new streaming table.
D. Create a new Lakeflow Declarative Pipeline: use readStream() function with option readChangeFeed to read tasks_status table CDF; enrich with the employee table; create a new streaming table as the result table and use apply_changes() function to process the changes from the enriched CDF.

Answer: D

Explanation:
Change Data Feed (CDF) allows downstream consumers to read incremental changes (inserts, updates, deletes) from a Delta table. The documentation explains that when streaming from a Delta table with CDF enabled, developers can use readStream().option("readChangeFeed","true") to capture incremental events. For maintaining a derived table with enrichment logic, the recommended practice is to use apply_changes(), which applies CDC semantics (insert/update/delete) correctly to the target streaming table. By joining with the static employee dimension, enriched rows are generated before being merged into the new streaming target. This ensures correctness, scalability, and minimal latency. Batch reads or skipping commits do not maintain correctness for CDC pipelines.

NEW QUESTION # 115
A Delta Lake table representing metadata about content from user has the following schema:
Based on the above schema, which column is a good candidate for partitioning the Delta Table?

A. Date
B. latitude
C. Post_id
D. User_id
E. Post_time

Answer: A

Explanation:
Partitioning a Delta Lake table improves query performance by organizing data into partitions based on the values of a column. In the given schema, the date column is a good candidate for partitioning for several reasons:
Time-Based Queries: If queries frequently filter or group by date, partitioning by the date column can significantly improve performance by limiting the amount of data scanned. Granularity: The date column likely has a granularity that leads to a reasonable number of partitions (not too many and not too few). This balance is important for optimizing both read and write performance.
Data Skew: Other columns like post_id or user_id might lead to uneven partition sizes (data skew), which can negatively impact performance.
Get Latest & Actual Certified-Data-Engineer-Professional Exam's Question and Answers from Partitioning by post_time could also be considered, but typically date is preferred due to its more manageable granularity.

NEW QUESTION # 116
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?

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. Only workspace administrators can grant "Owner" privileges to a group.
D. Databricks jobs must have exactly one owner; "Owner" privileges cannot be assigned to a group.
E. A user can only transfer job ownership to a group if they are also a member of that group.

Answer: D

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

NEW QUESTION # 117
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