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| Certification Vendor: | Databricks |
|---|---|
| Exam Name: | Databricks Certified Professional Data Engineer Exam |
| Exam Number: | Databricks-Certified-Professional-Data-Engineer |
| Exam Format: | Multiple choice, Multiple select, Scenario-based questions |
| Related Certifications: | Databricks Certified Data Engineer Associate |
| Exam Price: | USD 200 |
| Available Languages: | English |
| Exam Duration: | 120 minutes |
| Certificate Validity Period: | 2 years |
| Real Exam Qty: | 60-70 |
| Passing Score: | 70% |
| Recommended Training: | Databricks Academy |
| Exam Registration: | Databricks Certification Portal |
| Sample Questions: | Databricks Databricks-Certified-Professional-Data-Engineer Sample Questions |
| Exam Way: | Online proctored exam |
| Pre Condition: | Recommended: Databricks Certified Data Engineer Associate or equivalent experience with Spark and Databricks platform. |
| Official Syllabus URL: | https://www.databricks.com/learn/certification |
>> Latest Databricks-Certified-Professional-Data-Engineer Exam Questions <<
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NEW QUESTION # 179
The data engineering team has configured a Databricks SQL query and alert to monitor the values in a Delta Lake table. The recent_sensor_recordings table contains an identifying sensor_id alongside the timestamp and temperature for the most recent 5 minutes of recordings.
The below query is used to create the alert:
The query is set to refresh each minute and always completes in less than 10 seconds. The alert is set to trigger when mean (temperature) > 120. Notifications are triggered to be sent at most every 1 minute.
If this alert raises notifications for 3 consecutive minutes and then stops, which statement must be true?
Answer: B
Explanation:
This is the correct answer because the query is using a GROUP BY clause on the sensor_id column, which means it will calculate the mean temperature for each sensor separately. The alert will trigger when the mean temperature for any sensor is greater than 120, which means at least one sensor had an average temperature above 120 for three consecutive minutes. The alert will stop when the mean temperature for all sensors drops below 120. Verified Reference: [Databricks Certified Data Engineer Professional], under "SQL Analytics" section; Databricks Documentation, under "Alerts" section.
NEW QUESTION # 180
An engineering manager uses a Databricks SQL query to monitor their team's progress on fixes related to
customer-reported bugs. The manager checks the results of the query every day, but they are manually
rerunning the query each day and waiting for the results.
Which of the following approaches can the manager use to ensure the results of the query are up-dated each
day?
Answer: D
NEW QUESTION # 181
A transactions table has been liquid clustered on the columns product_id, user_id, and event_date.
Which operation lacks support for cluster on write?
Answer: C
Explanation:
Delta Lake's Liquid Clustering is an advanced feature that improves query performance by dynamically clustering data without requiring costly compaction steps like traditional Z-ordering.
When performing writes to a Liquid Clustered table, some write operations automatically maintain clustering, while others do not.
Explanation of Each Option:
(A) spark.writestream.format('delta').mode('append') (Correct Answer)
Reason: Streaming writes (writestream) do not support Liquid Clustering because streaming data arrives in micro-batches.
Since Liquid Clustering needs efficient global reorganization of files, streaming append operations don't provide sufficient data volume at a time to be effectively clustered.
Delta Lake documentation states that Liquid Clustering is only supported for batch writes.
(B) CTAS and RTAS statements
Reason: CREATE TABLE AS SELECT (CTAS) and REPLACE TABLE AS SELECT (RTAS) are batch operations and can enforce Liquid Clustering.
These operations create or replace a table based on a query result, and since they are batch-based, Liquid Clustering applies.
(C) INSERT INTO operations
Reason: INSERT INTO is supported for Liquid Clustering because it is a batch operation.
While it may not be as efficient as MERGE or COPY INTO, clustering is applied upon execution.
(D) spark.write.format('delta').mode('append')
Reason: Batch append operations are supported for Liquid Clustering.
Unlike streaming append, batch writes allow the optimizer to re-cluster data efficiently.
Conclusion:
Since streaming append operations do not support Liquid Clustering, option (A) is the correct answer.
Reference:
Liquid Clustering in Delta Lake - Databricks Documentation
NEW QUESTION # 182
Which approach demonstrates a modular and testable way to use DataFrame.transform for ETL code in PySpark?
Answer: A
Explanation:
Databricks and Apache Spark recommend building modular and reusable ETL transformations by leveraging the DataFrame.transform() API. This method allows you to chain multiple transformation functions in a clean and testable way.
Option A: Encapsulating the logic in a class (Pipeline) works, but it reduces modularity and flexibility. It does not show the true intended use of DataFrame.transform() which is chaining functional transformations.
Option B: This is the correct approach. It defines small, reusable functions (upper_value, filter_positive) that each take a DataFrame and return a transformed DataFrame. By chaining them with df.transform(func), you can compose ETL pipelines in a clear and declarative manner. This enables unit testing of individual functions and makes the ETL pipeline modular, testable, and production-ready.
Option C: This shows a single transformation wrapped in a function and tested, but it lacks pipeline composition - it is not demonstrating modular chaining across multiple transformations.
Option D: This simply defines a transformation function with hardcoded logic. It does not leverage DataFrame.transform() nor demonstrate modularity through composition.
Therefore, Option B is the best demonstration of how to use DataFrame.transform() in PySpark ETL pipelines.
Databricks documentation explicitly highlights that DataFrame.transform() allows developers to "chain together reusable functions in a readable and modular way, improving testability and maintainability of ETL code." This makes B the correct and officially supported pattern.
NEW QUESTION # 183
What is a method of installing a Python package scoped at the notebook level to all nodes in the currently active cluster?
Answer: A
Explanation:
Installing a Python package scoped at the notebook level to all nodes in the currently active cluster in Databricks can be achieved by using the Libraries tab in the cluster UI. This interface allows you to install libraries across all nodes in the cluster. While the %pip command in a notebook cell would only affect the driver node, using the cluster UI ensures that the package is installed on all nodes.
Reference:
Databricks Documentation on Libraries: Libraries
NEW QUESTION # 184
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