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

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

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Databricks Certified Professional Data Engineer Exam Sample Questions (Q69-Q74):

NEW QUESTION # 69
The data engineering team has configured a job to process customer requests to be forgotten (have their data deleted). All user data that needs to be deleted is stored in Delta Lake tables using default table settings.
The team has decided to process all deletions from the previous week as a batch job at 1am each Sunday. The total duration of this job is less than one hour. Every Monday at 3am, a batch job executes a series ofVACUUMcommands on all Delta Lake tables throughout the organization.
The compliance officer has recently learned about Delta Lake's time travel functionality. They are concerned that this might allow continued access to deleted data.
Assuming all delete logic is correctly implemented, which statement correctly addresses this concern?

A. Because Delta Lake time travel provides full access to the entire history of a table, deleted records can always be recreated by users with full admin privileges.
B. Because the vacuum command permanently deletes all files containing deleted records, deleted records may be accessible with time travel for around 24 hours.
C. Because the default data retention threshold is 24 hours, data files containing deleted records will be retained until the vacuum job is run the following day.
D. Because the default data retention threshold is 7 days, data files containing deleted records will be retained until the vacuum job is run 8 days later.
E. Because Delta Lake's delete statements have ACID guarantees, deleted records will be permanently purged from all storage systems as soon as a delete job completes.

Answer: B

Explanation:
Explanation
This is the correct answer because Delta Lake's delete statements do not physically remove the data files that contain the deleted records, but only mark them as logically deleted in the transaction log. These files are still accessible with time travel until they are permanently deleted by the vacuum command. The default data retention threshold for vacuum is 7 days, but in this case it is overridden by setting it to 24 hours in each vacuum command. Therefore, deleted records may be accessible with time travel for around 24 hours after they are deleted, until they are vacuumed. Verified References: [Databricks Certified Data Engineer Professional], under "Delta Lake" section; [Databricks Documentation], under "Optimizations - Vacuum" section.

NEW QUESTION # 70
When investigating a data issue you realized that a process accidentally updated the table, you want to query the same table with yesterday's version of the data so you can review what the prior version looks like, what is the best way to query historical data so you can do your analysis?

A. SHOW HISTORY table_name AS OF date_sub(current_date(), 1)
B. TIME_TRAVEL FROM table_name WHERE time_stamp = date_sub(current_date(), 1)
C. SELECT * FROM TIME_TRAVEL(table_name) WHERE time_stamp = 'timestamp'
D. SELECT * FROM table_name TIMESTAMP AS OF date_sub(current_date(), 1)
E. DISCRIBE HISTORY table_name AS OF date_sub(current_date(), 1)

Answer: D

Explanation:
Explanation
The answer is SELECT * FROM table_name TIMESTAMP as of date_sub(current_date(), 1) FYI, Time travel supports two ways one is using timestamp and the second way is using version number, Timestamp:
1.SELECT count(*) FROM my_table TIMESTAMP AS OF "2019-01-01"
2.SELECT count(*) FROM my_table TIMESTAMP AS OF date_sub(current_date(), 1)
3.SELECT count(*) FROM my_table TIMESTAMP AS OF "2019-01-01 01:30:00.000" Version Number:
1.SELECT count(*) FROM my_table VERSION AS OF 5238
2.SELECT count(*) FROM my_table@v5238
3.SELECT count(*) FROM delta.`/path/to/my/table@v5238`
https://databricks.com/blog/2019/02/04/introducing-delta-time-travel-for-large-scale-data-lakes.html

NEW QUESTION # 71
Which statement describes integration testing?

A. Requires manual intervention
B. Validates interactions between subsystems of your application
C. Validates an application use case
D. Requires an automated testing framework
E. Validates behavior of individual elements of your application

Answer: B

Explanation:
This is the correct answer because it describes integration testing. Integration testing is a type of testing that validates interactions between subsystems of your application, such as modules, components, or services.
Integration testing ensures that the subsystems work together as expected and produce the correct outputs or results. Integration testing can be done at different levels of granularity, such as component integration testing, system integration testing, or end-to-end testing. Integration testing can help detect errors or bugs that may not be found by unit testing, which only validates behavior of individual elements of your application.
Verified References: [Databricks Certified Data Engineer Professional], under "Testing" section; Databricks Documentation, under "Integration testing" section.

NEW QUESTION # 72
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 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.
B. 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.
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 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.

Answer: A

Explanation:
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 # 73
You were asked to create a notebook that can take department as a parameter and process the data accordingly, which is the following statements result in storing the notebook parameter into a py-thon variable

A. department = notebook.param.get("department")
B. department = notebook.widget.get("department")
C. department = dbutils.widget.get("department")
D. ASSIGN department == dbutils.widget.get("department")
E. SET department = dbutils.widget.get("department")

Answer: C

Explanation:
Explanation
The answer is department = dbutils.widget.get("department")
Refer to additional documentation here
https://docs.databricks.com/notebooks/widgets.html

NEW QUESTION # 74
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