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

Databricks Certified Data Engineer Professional Sample Questions:

1. A data engineering workspace was automatically enabled for Unity Catalog, creating a workspace catalog. New team members report they can create tables in the default schema but cannot access table in other schemas within the same workspace catalog. Why are the new team members unable to access tables in other schemas?

A) Workspace catalog permissions are not subject to inheritance rules.
B) New users only receive CREATE TABLE privileges on the default schema.
C) Tables in other schemas require additional BROWSEprivileges that new users don't receive automatically
D) Workspace users receive USE CATALOG and specific privileges on default schema only.

2. 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) By default, both the DBFS root and mounted data sources are only accessible to workspace administrators.
B) 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.
C) 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.
D) Neither the DBFS root nor mounted storage can be accessed when using %sh in a Databricks notebook.
E) The DBFS root is the most secure location to store data, because mounted storage volumes must have full public read and write permissions.

3. The data governance team is reviewing user for deleting records for compliance with GDPR. The following logic has been implemented to propagate deleted requests from the user_lookup table to the user aggregate table.

Assuming that user_id is a unique identifying key and that all users have requested deletion have been removed from the user_lookup table, which statement describes whether successfully executing the above logic guarantees that the records to be deleted from the user_aggregates table are no longer accessible and why?

A) No; files containing deleted records may still be accessible with time travel until a BACUM command is used to remove invalidated data files.
B) Yes; Delta Lake ACID guarantees provide assurance that the DELETE command successed fully and permanently purged these records.
C) No; the change data feed only tracks inserts and updates not deleted records.
D) Yes; the change data feed uses foreign keys to ensure delete consistency throughout the Lakehouse.
E) No; the Delta Lake DELETE command only provides ACID guarantees when combined with the MERGE INTO command

4. A junior data engineer seeks to leverage Delta Lake's Change Data Feed functionality to create a Type 1 table representing all of the values that have ever been valid for all rows in a bronze table created with the property delta.enableChangeDataFeed = true. They plan to execute the following code as a daily job:

Which statement describes the execution and results of running the above query multiple times?

A) Each time the job is executed, the target table will be overwritten using the entire history of inserted or updated records, giving the desired result.
B) Each time the job is executed, the entire available history of inserted or updated records will be appended to the target table, resulting in many duplicate entries.
C) Each time the job is executed, the differences between the original and current versions are calculated; this may result in duplicate entries for some records.
D) Each time the job is executed, newly updated records will be merged into the target table, overwriting previous values with the same primary keys.
E) Each time the job is executed, only those records that have been inserted or updated since the last execution will be appended to the target table giving the desired result.

5. A table in the Lakehouse named customer_churn_params 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.
The churn prediction model used by the ML team is fairly stable in production. The team is only interested in making predictions on records that have changed in the past 24 hours.
Which approach would simplify the identification of these changed records?

A) Modify the overwrite logic to include a field populated by calling
spark.sql.functions.current_timestamp() as data are being written; use this field to identify records written on a particular date.
B) Replace the current overwrite logic with a merge statement to modify only those records that have changed; write logic to make predictions on the changed records identified by the change data feed.
C) Calculate the difference between the previous model predictions and the current customer_churn_params on a key identifying unique customers before making new predictions; only make predictions on those customers not in the previous predictions.
D) Convert the batch job to a Structured Streaming job using the complete output mode; configure a Structured Streaming job to read from the customer_churn_params table and incrementally predict against the churn model.
E) Apply the churn model to all rows in the customer_churn_params table, but implement logic to perform an upsert into the predictions table that ignores rows where predictions have not changed.

Solutions: