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

Databricks Certified Data Engineer Professional Sample Questions:

1. A data engineer is setting up a pipeline to ingest data from a message bus system that occasionally delivers duplicate messages. The duplicate messages can be a week apart. The target is a Databricks Delta Lake table where each record should appear exactly once. Which Databricks ingestion pattern should be implemented to handle potential duplicates where events can arrive outside of the configured watermark?

A) Use Delta Lake's change data feed to filter duplicate records
B) Configure Structured Streaming with dropDuplicates transformation
C) Implement a write operation using MERGE INTO with a unique key
D) Use Delta Lake time travel to identify and remove duplicates

2. When evaluating the Ganglia Metrics for a given cluster with 3 executor nodes, which indicator would signal proper utilization of the VM's resources?

A) Bytes Received never exceeds 80 million bytes per second
B) The five Minute Load Average remains consistent/flat
C) Total Disk Space remains constant
D) CPU Utilization is around 75%
E) Network I/O never spikes

3. A data engineer is optimizing a MERGE operation on an 800GB UC-managed table that experiences frequent updates and deletions. Which two actions should the engineer prioritize to improve MERGE performance? (Choose two.)

A) Use ZORDER on high-cardinality columns.
B) Overwrite the table instead of Merge.
C) Apply liquid clustering using the merge join keys.
D) Partition the table by date.
E) Enable deletion vectors on the table if not already enabled.

4. Review the following error traceback:

Which statement describes the error being raised?

A) The code executed was PvSoark but was executed in a Scala notebook.
B) There is a type error because a column object cannot be multiplied.
C) There is a syntax error because the heartrate column is not correctly identified as a column.
D) There is no column in the table named heartrateheartrateheartrate
E) There is a type error because a DataFrame object cannot be multiplied.

5. 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 Delta Lake transaction log to identify all newly written data files.
C) Parse the Spark event logs to identify those rows that were updated, inserted, or deleted.
D) 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.

Solutions: