DatabricksProfessional levelPage 2 of 4

Databricks Data Engineer Professional exam dumps: questions 101 to 200 of 312

Page 2 of the free Databricks Data Engineer Professional question bank for the Databricks Certified Data Engineer Professional exam. Questions 101 to 200 are listed below, the first 5 in full with answers and explanations. Back to page 1 for the exam overview and FAQ.

Question bank last updated January 2025

Start timed practice exam Questions 101 to 200

Free Databricks Data Engineer Professional practice questions

Questions 101 to 105 of 312

Pick an answer before you open the explanation. Each question also has its own page with a permalink.

Databricks Data Engineer Professional Question 101

Select 3

You are working on a Delta Lake table that receives frequent updates and deletions. Previously, your data pipeline processed incremental data using a normal Structured Streaming read. To optimize your pipeline, you want to enable Change Data Feed (CDF) on the table and redesign your data processing steps to handle CDC (Change Data Capture) output. Which of the following steps should you take to achieve this?

A
Enable Change Data Feed (CDF) on the Delta Lake table by setting the table property delta.enableChangeDataFeed to true.
B
Use the input_file_name() function to identify changed rows in the Delta table.
C
Replace your Structured Streaming read with a query on the _change_type column available in the CDF output to identify inserted, updated, and deleted rows.
D
Query the CDC output using the table_changes function with a specific start and end version or timestamp.
E
Disable schema evolution on the Delta table to prevent schema conflicts when processing CDC data.

Show answer and explanation

Correct answers: A, C, D

Explanation

To redesign your data pipeline to process CDC output after enabling Change Data Feed (CDF) on a Delta Lake table, you must enable CDF by setting the appropriate table property, query the CDC data using the table_changes function, and use the _change_type column to identify the type of changes (insert, update, delete). Other options, such as using input_file_name() or disabling schema evolution, are unrelated to CDC processing and do not contribute to solving the problem.

A. Correct.
This is correct. To enable Change Data Feed on a Delta Lake table, the table property delta.enableChangeDataFeed must be set to true either during table creation or by altering the table properties.
B. Incorrect.
This is incorrect. The input_file_name() function identifies the file origin of a row but does not directly support CDC processing or detect changes like inserts, updates, and deletes.
C. Correct.
This is correct. After enabling CDF, the _change_type column in the CDF output can be used to filter rows based on their change type (e.g., inserts, updates, deletes).
D. Correct.
This is correct. The table_changes function allows you to query the CDC output for a range of versions or timestamps to fetch only the changed data.
E. Incorrect.
This is incorrect. Disabling schema evolution is not related to processing CDC data and could introduce limitations when dealing with schema changes in your pipeline.

Databricks Data Engineer Professional Question 102

Select 3

You are building a data pipeline on Databricks to process change data capture (CDC) data from a Delta Lake table. Previously, the pipeline used incremental reads with Structured Streaming. However, due to a new requirement to track record-level changes (inserts, updates, and deletes), you decide to enable Change Data Feed (CDF) on the Delta table and redesign your processing steps. After enabling CDF on the Delta table, which steps should you implement to correctly process the CDC output?

A
Use the table_changes function to fetch CDC records and filter by the required version range.
B
Enable CDF by setting the delta.enableChangeDataFeed property to true for the Delta table.
C
Use the _change_type column in the CDC output to handle inserts, updates, and deletes appropriately.
D
Continue using the default Structured Streaming read without any modifications.
E
Set up a Delta Live Table to automatically process CDC changes without enabling CDF explicitly.

Show answer and explanation

Correct answers: A, B, C

Explanation

To process change data capture (CDC) data from a Delta Lake table, you must enable Change Data Feed (CDF) by setting the delta.enableChangeDataFeed property on the table. Once CDF is enabled, you can use the table_changes function to fetch CDC records for a specific version range. The _change_type column in the CDC output provides the type of change (insert, update, delete), which is critical to handle the data correctly. The default Structured Streaming read does not support CDC, and additional configuration is required to ensure the pipeline processes CDC output.

A. Correct.
Correct: The table_changes function is a key feature of Delta Lake with CDF enabled and must be used to fetch the CDC output for a given version range.
B. Correct.
Correct: Enabling CDF requires setting the delta.enableChangeDataFeed property to true at the table level before CDC data can be generated.
C. Correct.
Correct: The _change_type column in the CDC output is essential to distinguish between inserts, updates, and deletes, and is a critical part of processing CDC data.
D. Incorrect.
Incorrect: The default Structured Streaming read does not process CDC data with record-level change information. You need to use the CDF features to explicitly handle these changes.
E. Incorrect.
Incorrect: Delta Live Tables can process Delta tables, but enabling CDF explicitly is still required to capture record-level changes for your pipeline.

Databricks Data Engineer Professional Question 103

Select 3

You are working on a Delta Lake table that tracks customer orders. The table is frequently updated, and sometimes rows are deleted. You need to propagate these deletes to a downstream system that consumes this table using Change Data Feed (CDF). Which of the following steps are required to ensure that deletes are correctly propagated to the downstream system?

A
Enable Change Data Feed (CDF) on the Delta table using table properties.
B
Query the _change_type column in the Change Data Feed to identify deleted rows.
C
Configure the downstream system to accept updates but ignore deletes.
D
When reading the Change Data Feed, filter rows where _change_type is 'delete'.
E
Manually generate delete events in the downstream system based on the Delta table state.

Show answer and explanation

Correct answers: A, B, D

Explanation

To propagate deletes using Change Data Feed (CDF), you must first enable CDF on the Delta table. Then, by querying the _change_type column, you can identify deleted rows. You must explicitly filter for rows where _change_type is 'delete' to ensure those deletions are handled correctly in the downstream system. CDF automates the process of capturing changes, so manual generation of delete events is unnecessary.

A. Correct.
This is correct. Change Data Feed must be enabled on the Delta table to capture row-level changes, including deletes.
B. Correct.
This is correct. The _change_type column in the Change Data Feed helps identify the type of change, such as 'insert', 'update', or 'delete'.
C. Incorrect.
This is incorrect. To propagate deletes, the downstream system must be configured to process deletes rather than ignoring them.
D. Correct.
This is correct. Filtering for rows where _change_type is 'delete' ensures that the deleted rows are correctly identified and propagated to the downstream system.
E. Incorrect.
This is incorrect. Manually generating delete events is not required when using Change Data Feed, as CDF automatically captures and propagates delete operations.

Databricks Data Engineer Professional Question 104

Single answer

You are working on a data engineering project where a source table contains customer data. The source table is being updated using Change Data Feed (CDF), and occasionally, records are deleted from the source table. You need to ensure that these deletions are propagated to a target Delta table that is used for downstream analytics. What is the best approach to implement this functionality?

A
Use the 'MERGE INTO' operation with a condition to match deleted records using the '_change_type' column from the source CDF data.
B
Perform an 'INSERT INTO' operation from the source CDF data into the target Delta table to capture deletions.
C
Filter the CDF data for rows where '_change_type' is 'delete' and use the 'DELETE' statement on the target Delta table.
D
Replace the entire target Delta table with the source Delta table to ensure deletions are reflected in the target.

Show answer and explanation

Correct answer: A

Explanation

Change Data Feed (CDF) provides the '_change_type' column, which can be used to determine if a record was inserted, updated, or deleted. The 'MERGE INTO' operation is specifically designed to handle all these scenarios in a single atomic operation, making it the most efficient and reliable way to propagate deletes (and other changes) from a source table to a target Delta table. Other approaches either lack the ability to handle all change types or are inefficient.

A. Correct.
Correct: The 'MERGE INTO' operation is the most appropriate way to propagate deletions because it allows you to handle updates, inserts, and deletes in a single transaction. By using the '_change_type' column provided by CDF, you can identify deleted records and apply the necessary changes to the target table.
B. Incorrect.
Incorrect: 'INSERT INTO' only appends data to the target table and does not handle deletions. This approach would not propagate deletions from the source table.
C. Incorrect.
Incorrect: While filtering the CDF data for '_change_type' as 'delete' is correct, the 'DELETE' statement alone cannot handle other changes (e.g., updates or inserts) and is not optimal for comprehensive CDF handling.
D. Incorrect.
Incorrect: Replacing the entire target Delta table with the source table is inefficient and does not leverage the benefits of CDF for incremental updates and deletes. This approach is not recommended in a production setting.

Databricks Data Engineer Professional Question 105

Select 3

You are managing a Delta Lake table in Databricks that tracks customer orders. The table is configured with Change Data Feed (CDF) enabled. Your team is building a downstream application that requires both new and updated records while also ensuring that deleted records are processed and removed correctly from the downstream system. Which of the following steps should you take to propagate the deletes to the downstream system using CDF?

A
Query the Change Data Feed using the readChangeFeed API and filter for rows where the _change_type column is 'delete'.
B
Update the downstream target system by removing rows matching the primary key of records with _change_type as 'delete'.
C
Query the Delta table directly using the read API and filter for rows where the _change_type column is 'delete'.
D
Ensure that the downstream system supports processing of delete operations based on primary keys.
E
Write the delete records back to the Delta Lake table to mark them as processed.

Show answer and explanation

Correct answers: A, B, D

Explanation

To propagate deletes using Delta Lake's Change Data Feed (CDF), you should query the table using the readChangeFeed API to filter rows where _change_type is 'delete'. These rows should then be processed by removing them from the downstream system based on their primary keys. Additionally, the downstream system must support handling deletes to ensure proper propagation.

A. Correct.
Correct. The readChangeFeed API is the correct method to query the Change Data Feed and filter for deletes using the _change_type column.
B. Correct.
Correct. To propagate deletes, the downstream system needs to remove records matching the primary key of deleted rows.
C. Incorrect.
Incorrect. The read API does not provide access to change data feed-specific metadata like _change_type. This makes it unsuitable for identifying deletes.
D. Correct.
Correct. The downstream system must handle deletions properly by processing the primary keys of deleted records.
E. Incorrect.
Incorrect. Writing delete records back to the Delta Lake table is unnecessary and does not propagate deletes to downstream systems.

Timed practice exam

Take a Databricks Data Engineer Professional practice test under exam conditions

60 questions in 120 minutes, drawn from this bank, with a score report and a per-question review when you finish.

Start timed exam

Databricks Data Engineer Professional practice questions 101 to 200 of 312

Every question has a page with the answer and explanation. Numbers are stable, so you can bookmark or share them. The bank is split into 4 pages of up to 100 questions.

More Databricks exam dumps

All Databricks exams

531 questions

Databricks Data Engineer Associate exam dumps

Databricks Certified Data Engineer Associate

305 questions

Databricks Generative AI Engineer Associate exam dumps

Databricks Certified Generative AI Engineer Associate

655 questions

Databricks Machine Learning Associate exam dumps

Databricks Certified Machine Learning Associate

279 questions

Databricks Machine Learning Professional exam dumps

Databricks Certified Machine Learning Professional