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Databricks Certified Data Engineer Professional Sample Questions:

1. 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) 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.
B) 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.
C) 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.
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) 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.

2. A data engineer, User A, has promoted a new pipeline to production by using the REST API to programmatically create several jobs. A DevOps engineer, User B, has configured an external orchestration tool to trigger job runs through the REST API. Both users authorized the REST API calls using their personal access tokens.
Which statement describes the contents of the workspace audit logs concerning these events?

A) Because User B last configured the jobs, their identity will be associated with both the job creation events and the job run events.
B) Because the REST API was used for job creation and triggering runs, user identity will not be captured in the audit logs.
C) Because User A created the jobs, their identity will be associated with both the job creation Get Latest & Actual Certified-Data-Engineer-Professional Exam's Question and Answers from events and the job run events.
D) Because the REST API was used for job creation and triggering runs, a Service Principal will be automatically used to identity these events.
E) Because these events are managed separately, User A will have their identity associated with the job creation events and User B will have their identity associated with the job run events.

3. A Delta Lake table was created with the below query:

Consider the following query:
DROP TABLE prod.sales_by_store
If this statement is executed by a workspace admin, which result will occur?

A) An error will occur because Delta Lake prevents the deletion of production data.
B) Data will be marked as deleted but still recoverable with Time Travel.
C) The table will be removed from the catalog and the data will be deleted.
D) Nothing will occur until a COMMIT command is executed.
E) The table will be removed from the catalog but the data will remain in storage.

4. A junior data engineer is migrating a workload from a relational database system to the Databricks Lakehouse. The source system uses a star schema, leveraging foreign key constrains and multi-table inserts to validate records on write.
Which consideration will impact the decisions made by the engineer while migrating this workload?

A) Foreign keys must reference a primary key field; multi-table inserts must leverage Delta Lake's upsert functionality.
B) Databricks supports Spark SQL and JDBC; all logic can be directly migrated from the source system without refactoring.
C) Committing to multiple tables simultaneously requires taking out multiple table locks and can lead to a state of deadlock.
D) All Delta Lake transactions are ACID compliance against a single table, and Databricks does not enforce foreign key constraints.
E) Databricks only allows foreign key constraints on hashed identifiers, which avoid collisions in highly-parallel writes.

5. The marketing team is looking to share data in an aggregate table with the sales organization, but the field names used by the teams do not match, and a number of marketing specific fields have not been approval for the sales org.
Which of the following solutions addresses the situation while emphasizing simplicity?

A) Create a new table with the required schema and use Delta Lake's DEEP CLONE functionality to sync up changes committed to one table to the corresponding table.
B) Use a CTAS statement to create a derivative table from the marketing table configure a production jon to propagation changes.
C) Instruct the marketing team to download results as a CSV and email them to the sales organization.
D) Add a parallel table write to the current production pipeline, updating a new sales table that varies Get Latest & Actual Certified-Data-Engineer-Professional Exam's Question and Answers from as required from marketing table.
E) Create a view on the marketing table selecting only these fields approved for the sales team alias the names of any fields that should be standardized to the sales naming conventions.

Solutions: