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

Databricks Certified Data Engineer Professional Sample Questions:

1. A Delta Lake table representing metadata about content from user has the following schema:
user_id LONG, post_text STRING, post_id STRING, longitude FLOAT, latitude FLOAT, post_time TIMESTAMP, date DATE Based on the above schema, which column is a good candidate for partitioning the Delta Table?

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

2. What describes a primary technical challenge in ensuring consistent PII masking across all nodes in large-scale, distributed Databricks batch and streaming pipelines?

A) Native masking in Databricks automatically synchronizes with all downstream external Databricks systems.
B) Masking functions must be standardized and managed through Unity Catalog, with enforcement applied across all relevant datasets to avoid any data inconsistency.
C) PII masking is only required for direct identifiers.
D) Dynamic data masking is applied only at rest, so it does not affect query performance.

3. A data engineer is configuring a pipeline that will potentially see late-arriving, duplicate records.
In addition to de-duplicating records within the batch, which of the following approaches allows the data engineer to deduplicate data against previously processed records as it is inserted into a Delta table?

A) VACUUM the Delta table after each batch completes.
B) Perform an insert-only merge with a matching condition on a unique key.
C) Set the configuration delta.deduplicate = true.
D) Perform a full outer join on a unique key and overwrite existing data.
E) Rely on Delta Lake schema enforcement to prevent duplicate records.

4. A distributed team of data analysts share computing resources on an interactive cluster with autoscaling configured. In order to better manage costs and query throughput, the workspace administrator is hoping to evaluate whether cluster upscaling is caused by many concurrent users or resource-intensive queries.
In which location can one review the timeline for cluster resizing events?

A) Executor's log file
B) Ganglia
C) Cluster Event Log
D) Driver's log file
E) Workspace audit logs

5. 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) Convert the list of configuration values to a dictionary of table settings, using table names as keys.
B) Load the configuration values for these tables from a separate file, located at a path provided by a pipeline parameter.
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

Solutions: