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

Databricks Certified Data Engineer Professional Sample Questions:

1. A company wants to implement Lakehouse Federation across multiple data sources but is concerned about data consistency and ensuring that all teams access the same authoritative version of their data. Which statement is applicable for Lakehouse Federations to maintain data consistency?

A) Federation provides read-only access that reflects the current state of source systems.
B) Federation creates local copies that must be manually refreshed.
C) Federation implements change data capture (CDC) from all sources.
D) A separate data synchronization service must be deployed.

2. A data engineer wants to refactor the following DLT code, which includes multiple table definitions with very similar code.

In an attempt to programmatically create these tables using a parameterized table definition, the data engineer writes the following code.

The pipeline runs an update with this refactored code, but generates a different DAG showing incorrect configuration values for these tables.
How can the data engineer fix this?

A) Load the configuration values for these tables from a separate file, located at a path provided by a pipeline parameter.
B) Convert the list of configuration values to a dictionary of table settings, using table names as keys.
C) Convert the list of configuration values to a dictionary of table settings, using different input the for loop.
D) Wrap the loop inside another table definition, using generalized names and properties to replace with those from the inner table

3. A Delta Lake table in the Lakehouse named customer_parsams is used in churn prediction by the machine learning team. The table contains information about customers derived from a number of upstream sources. Currently, the data engineering team populates this table nightly by overwriting the table with the current valid values derived from upstream data sources.
Immediately after each update succeeds, the data engineer team would like to determine the difference between the new version and the previous of the table. Given the current implementation, which method can be used?

A) Execute DESCRIBE HISTORY customer_churn_params to obtain the full operation metrics for the update, including a log of all records that have been added or modified.
B) Parse the Spark event logs to identify those rows that were updated, inserted, or deleted.
C) Execute a query to calculate the difference between the new version and the previous version using Delta Lake's built-in versioning and time travel functionality.
D) Parse the Delta Lake transaction log to identify all newly written data files.

4. A task orchestrator has been configured to run two hourly tasks. First, an outside system writes Parquet data to a directory mounted at /mnt/raw_orders/. After this data is written, a Databricks job containing the following code is executed:

Assume that the fields customer_id and order_id serve as a composite key to uniquely identify each order, and that the time field indicates when the record was queued in the source system.
If the upstream system is known to occasionally enqueue duplicate entries for a single order hours apart, which statement is correct?

A) Duplicate records arriving more than 2 hours apart will be dropped, but duplicates that arrive in the same batch may both be written to the orders table.
B) The orders table will contain only the most recent 2 hours of records and no duplicates will be present.
C) The orders table will not contain duplicates, but records arriving more than 2 hours late will be ignored and missing from the table.
D) Duplicate records enqueued more than 2 hours apart may be retained and the orders table may contain duplicate records with the same customer_id and order_id.
E) All records will be held in the state store for 2 hours before being deduplicated and committed to the orders table.

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

Solutions: