Databricks-Certified-Professional-Data-Engineer Valid Exam Format, Databricks-Certified-Professional-Data-Engineer Reliable Practice Questions

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Databricks Certified Professional Data Engineer Exam is a comprehensive exam that covers a wide range of topics related to data engineering. It includes questions on data ingestion, data transformation, data storage, data processing, and data management using Databricks. Databricks-Certified-Professional-Data-Engineer Exam also covers topics such as cluster management, security, and performance optimization. Databricks-Certified-Professional-Data-Engineer exam is designed to test the candidate's ability to design, implement, and manage data engineering solutions using Databricks.

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Databricks is a leading company in the field of data engineering, providing a cloud-based platform for collaborative data analysis and processing. The company's platform is used by a wide range of companies and organizations, including Fortune 500 companies, government agencies, and academic institutions. Databricks offers a range of certifications to help professionals demonstrate their proficiency in using the platform, including the Databricks Certified Professional Data Engineer certification.

Databricks Certified Professional Data Engineer Exam Sample Questions (Q126-Q131):

NEW QUESTION # 126
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.
MERGE INTO customers
USING (
SELECT updates.customer_id as merge_ey, updates .*
FROM updates
UNION ALL
SELECT NULL as merge_key, updates .*
FROM updates JOIN customers
ON updates.customer_id = customers.customer_id
WHERE customers.current = true AND updates.address <> customers.address ) staged_updates ON customers.customer_id = mergekey WHEN MATCHED AND customers. current = true AND customers.address <> staged_updates.address THEN UPDATE SET current = false, end_date = staged_updates.effective_date WHEN NOT MATCHED THEN INSERT (customer_id, address, current, effective_date, end_date) VALUES (staged_updates.customer_id, staged_updates.address, true, staged_updates.effective_date, null) Which statement describes this implementation?

A. The customers table is implemented as a Type 2 table; old values are overwritten and new customers are appended.
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.
C. The customers table is implemented as a Type 0 table; all writes are append only with no changes to existing values.
D. The customers table is implemented as a Type 1 table; old values are overwritten by new values and no history is maintained.

Answer: B

Explanation:
The provided MERGE statement is a classic implementation of a Type 2 SCD in a data warehousing context.
In this approach, historical data is preserved by keeping old records (marking them as not current) and adding new records for changes. Specifically, when a match is found and there's a change in the address, the existing record in the customers table is updated to mark it as no longer current (current = false), and an end date is assigned (end_date = staged_updates.effective_date). A new record for the customer is then inserted with the updated information, marked as current. This method ensures that the full history of changes to customer information is maintained in the table, allowing for time-based analysis of customer data.References:
Databricks documentation on implementing SCDs using Delta Lake and the MERGE statement (https://docs.databricks.com/delta/delta-update.html#upsert-into-a-table-using-merge).

NEW QUESTION # 127
You are asked to setup two tasks in a databricks job, the first task runs a notebook to download the data from a remote system, and the second task is a DLT pipeline that can process this data, how do you plan to configure this in Jobs UI

A. Single job can be used to setup both notebook and DLT pipeline, use two different tasks with linear dependency.
B. Jobs UI does not support DTL pipeline, setup the first task using jobs UI and setup the DLT to run in continuous mode.
C. Jobs UI does not support DTL pipeline, setup the first task using jobs UI and setup the DLT to run in trigger mode.
D. Add first step in the DLT pipeline and run the DLT pipeline as triggered mode in JOBS UI
E. Single job cannot have a notebook task and DLT Pipeline task, use two different jobs with linear dependency.

Answer: A

Explanation:
Explanation
The answer is Single job can be used to set up both notebook and DLT pipeline, use two different tasks with linear dependency, Here is the JOB UI
1.Create a notebook task
2.Create DLT task
a.add notebook task as dependency
3.Final view
Create the notebook task
Graphical user interface, text, application, email Description automatically generated

DLT task
Graphical user interface, text, application, email Description automatically generated

Final view
Graphical user interface, text, application, PowerPoint Description automatically generated

Bottom of Form
Top of Form

NEW QUESTION # 128
A CHECK constraint has been successfully added to the Delta table named activity_details using the following logic:

A batch job is attempting to insert new records to the table, including a record where latitude = 45.50 and longitude = 212.67.
Which statement describes the outcome of this batch insert?

A. The write will include all records in the target table; any violations will be indicated in the boolean column named valid_coordinates.
B. The write will insert all records except those that violate the table constraints; the violating records will be reported in a warning log.
C. The write will fail completely because of the constraint violation and no records will be inserted into the target table.
D. The write will insert all records except those that violate the table constraints; the violating records will be recorded to a quarantine table.
E. The write will fail when the violating record is reached; any records previously processed will be recorded to the target table.

Answer: C

Explanation:
The CHECK constraint is used to ensure that the data inserted into the table meets the specified conditions. In this case, the CHECK constraint is used to ensure that the latitude and longitude values are within the specified range. If the data does not meet the specified conditions, the write operation will fail completely and no records will be inserted into the target table. This is because Delta Lake supports ACID transactions, which means that either all the data is written or none of it is written. Therefore, the batch insert will fail when it encounters a record that violates the constraint, and the target table will not be updated. Reference:
Constraints: https://docs.delta.io/latest/delta-constraints.html
ACID Transactions: https://docs.delta.io/latest/delta-intro.html#acid-transactions

NEW QUESTION # 129
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.
MERGE INTO customers
USING (
SELECT updates.customer_id as merge_ey, updates .*
FROM updates
UNION ALL
SELECT NULL as merge_key, updates .*
FROM updates JOIN customers
ON updates.customer_id = customers.customer_id
WHERE customers.current = true AND updates.address <> customers.address ) staged_updates ON customers.customer_id = mergekey WHEN MATCHED AND customers. current = true AND customers.address <> staged_updates.address THEN UPDATE SET current = false, end_date = staged_updates.effective_date WHEN NOT MATCHED THEN INSERT (customer_id, address, current, effective_date, end_date) VALUES (staged_updates.customer_id, staged_updates.address, true, staged_updates.effective_date, null) Which statement describes this implementation?

A. The customers table is implemented as a Type 2 table; old values are overwritten and new customers are appended.
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.
C. The customers table is implemented as a Type 0 table; all writes are append only with no changes to existing values.
D. The customers table is implemented as a Type 1 table; old values are overwritten by new values and no history is maintained.

Answer: B

Explanation:
The provided MERGE statement is a classic implementation of a Type 2 SCD in a data warehousing context. In this approach, historical data is preserved by keeping old records (marking them as not current) and adding new records for changes. Specifically, when a match is found and there's a change in the address, the existing record in the customers table is updated to mark it as no longer current (current = false), and an end date is assigned (end_date = staged_updates.effective_date). A new record for the customer is then inserted with the updated information, marked as current. This method ensures that the full history of changes to customer information is maintained in the table, allowing for time-based analysis of customer data.
Reference: Databricks documentation on implementing SCDs using Delta Lake and the MERGE statement (https://docs.databricks.com/delta/delta-update.html#upsert-into-a-table-using-merge).

NEW QUESTION # 130
A Databricks SQL dashboard has been configured to monitor the total number of records present in a collection of Delta Lake tables using the following query pattern:
SELECT COUNT (*) FROM table -
Which of the following describes how results are generated each time the dashboard is updated?

A. The total count of rows is calculated by scanning all data files
B. The total count of records is calculated from the Hive metastore
C. The total count of records is calculated from the parquet file metadata
D. The total count of records is calculated from the Delta transaction logs
E. The total count of rows will be returned from cached results unless REFRESH is run

Answer: D

Explanation:
https://delta.io/blog/2023-04-19-faster-aggregations-metadata/#:~:text=You%20can%20get%20the%
20number,a%20given%20Delta%20table%20version.

NEW QUESTION # 131
......

Databricks-Certified-Professional-Data-Engineer Reliable Practice Questions: https://www.fast2test.com/Databricks-Certified-Professional-Data-Engineer-premium-file.html