Free CERTIFIED-DATA-ENGINEER-PROFESSIONAL Databricks CERTIFIED-DATA-ENGINEER-PROFESSIONAL Practice Test Question - Page 2

Question 4 (Topic 1)

The view updates represents an incremental batch of all newly ingested data to be inserted or updated in the customers table. The following logic is used to process these records. Which statement describes this implementation?

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○ The customers table is implemented as a Type 3 table; old values are maintained as a new column alongside the current value.
○ The customers table is implemented as a Type 2 table; old values are maintained but marked as no longer current and new values are inserted.
○ The customers table is implemented as a Type 0 table; all writes are append only with no changes to existing values.
○ The customers table is implemented as a Type 1 table; old values are overwritten by new values and no history is maintained.
○ The customers table is implemented as a Type 2 table; old values are overwritten and new customers are appended.

Reference / correct answer:

The customers table is implemented as a Type 2 table; old values are maintained but marked as no longer current and new values are inserted.

Most accepted answer: B. The customers table is implemented as a Type 2 table; old values are maintained but marked as no longer current and new values are inserted.

Community votes: B=4

Selected Answer: B Correct: B - In the 'WHEN MATCHED' clause, the existing row is updated to 'current = false' and given an 'end_date', which 'closes' the old version but keeps it in the table as history. In the 'WHEN NOT MATCHED' clause, a new row is inserted with 'current = true', a new 'effective_date' and 'end_date = null', representing the latest version of that customer. This pattern (expire old row + insert new current row with effective/end dates) is exactly the definition of a Slowly Changing Dimension Type 2 implementation. Incorrect: A - Old values are maintained as a rows, not as new columns. C - Old values are marked by setting 'current = false' and specifying an 'end_date'. D - History in maintained. E - This contradicts Type 2, as Type 2 never overwrites old values. upvoted 1 times

Selected Answer: B Suretly B upvoted 1 times

B is correct upvoted 1 times

B. The customers table is implemented as a Type 2 table; old values are maintained but marked as no longer current and new values are inserted. A Type 1 table does not track changes in dimensional attributes - the new value overwrites the existing value. Here, we do not preserve historical changes in data. A Type 2 Table tracks change over time by creating new rows for each change. A new dimension record is inserted with a high-end date or one with NULL. The previous record is "closed" with an end date. This approach maintains a complete history of changes and allows for as-was reporting use cases. A data warehousing method called Slowly Changing Dimension (SCD) Type 3 is used to track both the old and new values while managing historical changes in data over time. To reflect the historical and present values of an attribute, SCD Type 3 keeps two extra columns in the dimension table. upvoted 3 times

Selected Answer: B B is correct upvoted 3 times

Question 5 (Topic 1)

The data governance team is reviewing code used for deleting records for compliance with GDPR. They note the following logic is used to delete records from the Delta Lake table named users. Assuming that user_id is a unique identifying key and that delete_requests contains all users that have requested deletion, which statement describes whether successfully executing the above logic guarantees that the records to be deleted are no longer accessible and why?

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○ Yes; Delta Lake ACID guarantees provide assurance that the DELETE command succeeded fully and permanently purged these records.
○ No; the Delta cache may return records from previous versions of the table until the cluster is restarted.
○ Yes; the Delta cache immediately updates to reflect the latest data files recorded to disk.
○ No; the Delta Lake DELETE command only provides ACID guarantees when combined with the MERGE INTO command.
○ No; files containing deleted records may still be accessible with time travel until a VACUUM command is used to remove invalidated data files.

Question 6 (Topic 1)

Review the following error traceback: Which statement describes the error being raised?

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○ The code executed was PySpark but was executed in a Scala notebook.
○ There is no column in the table named heartrateheartrateheartrate
○ There is a type error because a column object cannot be multiplied.
○ There is a type error because a DataFrame object cannot be multiplied.
○ There is a syntax error because the heartrate column is not correctly identified as a column.

Free CERTIFIED-DATA-ENGINEER-PROFESSIONAL – Databricks CERTIFIED-DATA-ENGINEER-PROFESSIONAL Practice Test Question