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

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

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Databricks Certified Data Engineer Professional Exam Sample Questions (Q27-Q32):

NEW QUESTION # 27
Two of the most common data locations on Databricks are the DBFS root storage and external object storage mounted with dbutils.fs.mount().
Which of the following statements is correct?

A. DBFS is a file system protocol that allows users to interact with files stored in object storage using syntax and guarantees similar to Unix file systems.
B. Neither the DBFS root nor mounted storage can be accessed when using %sh in a Databricks notebook.
C. By default, both the DBFS root and mounted data sources are only accessible to workspace administrators.
D. The DBFS root stores files in ephemeral block volumes attached to the driver, while mounted directories will always persist saved data to external storage between sessions.
E. The DBFS root is the most secure location to store data, because mounted storage volumes must have full public read and write permissions.

Answer: A

Explanation:
DBFS is a file system protocol that allows users to interact with files stored in object storage using syntax and guarantees similar to Unix file systems. DBFS is not a physical file system, but a layer over the object storage that provides a unified view of data across different data sources. By default, the DBFS root is accessible to all users in the workspace, and the access to mounted data sources depends on the permissions of the storage account or container. Mounted storage volumes do not need to have full public read and write permissions, but they do require a valid connection string or access key to be provided when mounting. Both the DBFS root and mounted storage can be accessed when using %sh in a Databricks notebook, as long as the cluster has FUSE enabled. The DBFS root does not store files in ephemeral block volumes attached to the driver, but in the object storage associated with the workspace. Mounted directories will persist saved data to external storage between sessions, unless they are unmounted or deleted.

NEW QUESTION # 28
A Delta table of weather records is partitioned by date and has the below schema:
date DATE, device_id INT, temp FLOAT, latitude FLOAT, longitude FLOAT
To find all the records from within the Arctic Circle, you execute a query with the below filter:
latitude > 66.3
Which statement describes how the Delta engine identifies which files to load?

A. All records are cached to attached storage and then the filter is applied Get Latest & Actual Certified-Data-Engineer-Professional Exam's Question and Answers from
B. All records are cached to an operational database and then the filter is applied
C. The Hive metastore is scanned for min and max statistics for the latitude column
D. The Parquet file footers are scanned for min and max statistics for the latitude column
E. The Delta log is scanned for min and max statistics for the latitude column

Answer: E

Explanation:
This is the correct answer because Delta Lake uses a transaction log to store metadata about each table, including min and max statistics for each column in each data file. The Delta engine can use this information to quickly identify which files to load based on a filter condition, without scanning the entire table or the file footers. This is called data skipping and it can improve query performance significantly. Verified Reference: [Databricks Certified Data Engineer Professional], under "Delta Lake" section; [Databricks Documentation], under "Optimizations - Data Skipping" section.
In the Transaction log, Delta Lake captures statistics for each data file of the table. These statistics indicate per file:
- Total number of records
- Minimum value in each column of the first 32 columns of the table
- Maximum value in each column of the first 32 columns of the table
- Null value counts for in each column of the first 32 columns of the table When a query with a selective filter is executed against the table, the query optimizer uses these statistics to generate the query result. it leverages them to identify data files that may contain records matching the conditional filter.
For the SELECT query in the question, The transaction log is scanned for min and max statistics for the price column.

NEW QUESTION # 29
A table is registered with the following code:
Get Latest & Actual Certified-Data-Engineer-Professional Exam's Question and Answers from

Both users and orders are Delta Lake tables. Which statement describes the results of querying recent_orders?

A. All logic will execute when the table is defined and store the result of joining tables to the DBFS; this stored data will be returned when the table is queried.
B. All logic will execute at query time and return the result of joining the valid versions of the source tables at the time the query began.
C. All logic will execute at query time and return the result of joining the valid versions of the source tables at the time the query finishes.
D. Results will be computed and cached when the table is defined; these cached results will incrementally update as new records are inserted into source tables.
E. The versions of each source table will be stored in the table transaction log; query results will be saved to DBFS with each query.

Answer: A

Explanation:
Table is created and data of join will be stored on DBFS and it will be returned on query time.

NEW QUESTION # 30
What is a method of installing a Python package scoped at the notebook level to all nodes in the currently active cluster?

A. Run source env/bin/activate in a notebook setup script
B. Install libraries from PyPi using the cluster UI
C. Use &Pip install in a notebook cell
D. Use &sh install in a notebook cell

Answer: B

Explanation:
Installing a Python package scoped at the notebook level to all nodes in the currently active cluster in Databricks can be achieved by using the Libraries tab in the cluster UI. This interface allows you to install libraries across all nodes in the cluster. While the %pip command in a notebook cell would only affect the driver node, using the cluster UI ensures that the package is installed on all nodes.

NEW QUESTION # 31
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 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.
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 read() function to read tasks_status table; enrich with employee table; store the result in a materialized view.
D. 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.

Answer: A

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 # 32
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