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Databricks Certified Professional Data Engineer Exam Sample Questions (Q118-Q123):

NEW QUESTION # 118
In order to facilitate near real-time workloads, a data engineer is creating a helper function to leverage the schema detection and evolution functionality of Databricks Auto Loader. The desired function will automatically detect the schema of the source directly, incrementally process JSON files as they arrive in a source directory, and automatically evolve the schema of the table when new fields are detected.
The function is displayed below with a blank:

Which response correctly fills in the blank to meet the specified requirements?

A. Option C
B. Option E
C. Option B
D. Option D
E. Option A

Answer: C

Explanation:
Option B correctly fills in the blank to meet the specified requirements. Option B uses the
"cloudFiles.schemaLocation" option, which is required for the schema detection andevolution functionality of Databricks Auto Loader. Additionally, option B uses the "mergeSchema" option, which is required for the schema evolution functionality of Databricks Auto Loader. Finally, option B uses the "writeStream" method, which is required for the incremental processing of JSON files as they arrive in a source directory. The other options are incorrect because they either omit the required options, use the wrong method, or use the wrong format. References:
* Configure schema inference and evolution in Auto Loader:
https://docs.databricks.com/en/ingestion/auto-loader/schema.html
* Write streaming data:
https://docs.databricks.com/spark/latest/structured-streaming/writing-streaming-data.html

NEW QUESTION # 119
A table named user_ltv is being used to create a view that will be used by data analysis on various teams.
Users in the workspace are configured into groups, which are used for setting up data access using ACLs.
The user_ltv table has the following schema:

An analyze who is not a member of the auditing group executing the following query:

Which result will be returned by this query?

A. All age values less than 18 will be returned as null values all other columns will be returned with the values in user_ltv.
B. All columns will be displayed normally for those records that have an age greater than 17; records not meeting this condition will be omitted.
C. All records from all columns will be displayed with the values in user_ltv.
D. All columns will be displayed normally for those records that have an age greater than 18; records not meeting this condition will be omitted.

Answer: D

Explanation:
Given the CASE statement in the view definition, the result set for a user not in the auditing group would be constrained by the ELSE condition, which filters out records based on age. Therefore, the view will return all columns normally for records with an age greater than 18, as users who are not in the auditing group will not satisfy the is_member('auditing') condition. Records not meeting the age > 18 condition will not be displayed.

NEW QUESTION # 120
Which of the following locations in the Databricks product architecture hosts the notebooks and jobs?

A. Control plane
B. Databricks web application
C. Databricks Filesystem
D. JDBC data source
E. Data plane

Answer: A

Explanation:
Explanation
The answer is Control Pane,
Databricks operates most of its services out of a control plane and a data plane, please note serverless features like SQL Endpoint and DLT compute use shared compute in Control pane.
Control Plane: Stored in Databricks Cloud Account
*The control plane includes the backend services that Databricks manages in its own Azure account. Notebook commands and many other workspace configurations are stored in the control plane and encrypted at rest.
Data Plane: Stored in Customer Cloud Account
*The data plane is managed by your Azure account and is where your data resides. This is also where data is processed. You can use Azure Databricks connectors so that your clusters can connect to external data sources outside of your Azure account to ingest data or for storage.
Timeline Description automatically generated

NEW QUESTION # 121
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 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.
B. Because User A created the jobs, their identity will be associated with both the job creation events and the job run events.
C. Because the REST API was used for job creation and triggering runs, a Service Principal will be automatically used to identity these events.
D. Because User B last configured the jobs, their identity will be associated with both the job creation events and the job run events.
E. Because the REST API was used for job creation and triggering runs, user identity will not be captured in the audit logs.

Answer: A

Explanation:
Explanation
The events are that a data engineer, User A, has promoted a new pipeline to production by using the REST API to programmatically create several jobs, and 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. The workspace audit logs are logs that record user activities in a Databricks workspace, such as creating, updating, or deleting objects like clusters, jobs, notebooks, or tables. The workspace audit logs also capture the identity of the user who performed each activity, as well as the time and details of the activity. 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 in the workspace audit logs. Verified References: [Databricks Certified Data Engineer Professional], under
"Databricks Workspace" section; Databricks Documentation, under "Workspace audit logs" section.

NEW QUESTION # 122
The data engineering team maintains the following code:

Assuming that this code produces logically correct results and the data in the source tables has been de-duplicated and validated, which statement describes what will occur when this code is executed?

A. The enriched_itemized_orders_by_account table will be overwritten using the current valid version of data in each of the three tables referenced in the join logic.
B. An incremental job will leverage information in the state store to identify unjoined rows in the source tables and write these rows to the enriched_iteinized_orders_by_account table.
C. No computation will occur until enriched_itemized_orders_by_account is queried; upon query materialization, results will be calculated using the current valid version of data in each of the three tables referenced in the join logic.
D. A batch job will update the enriched_itemized_orders_by_account table, replacing only those rows that have different values than the current version of the table, using accountID as the primary key.
E. An incremental job will detect if new rows have been written to any of the source tables; if new rows are detected, all results will be recalculated and used to overwrite the enriched_itemized_orders_by_account table.

Answer: A

Explanation:
Explanation
This is the correct answer because it describes what will occur when this code is executed. The code uses three Delta Lake tables as input sources: accounts, orders, and order_items. These tables are joined together using SQL queries to create a view called new_enriched_itemized_orders_by_account, which contains information about each order item and its associated account details. Then, the code uses write.format("delta").mode("overwrite") to overwrite a target table called enriched_itemized_orders_by_account using the data from the view. This means that every time this code is executed, it will replace all existing data in the target table with new data based on the current valid version of data in each of the three input tables. Verified References: [Databricks Certified Data Engineer Professional], under "Delta Lake" section; Databricks Documentation, under "Write to Delta tables" section.

NEW QUESTION # 123
......

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