2026 Databricks-Certified-Data-Engineer-Professional: Databricks Certified Data Engineer Professional Exam–Professional Exam Study Solutions
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Databricks Databricks-Certified-Data-Engineer-Professional Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Data Modeling and Storage | 20% | - Storage Optimization - File Formats - Data Modeling |
| Data Processing | 28% | - Data Transformation - ETL Pipelines - Spark SQL - Structured Streaming |
| Monitoring and Troubleshooting | 16% | - Troubleshooting - Performance Optimization - Monitoring |
| Databricks Lakehouse Platform | 24% | - Data Management - Lakehouse Architecture - Delta Lake - Unity Catalog |
| Data Quality and Governance | 12% | - Governance - Data Lineage - Data Quality |
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Databricks Certified Data Engineer Professional Exam Sample Questions (Q62-Q67):
NEW QUESTION # 62
A Databricks job has been configured with 3 tasks, each of which is a Databricks notebook. Task A does not depend on other tasks. Tasks B and C run in parallel, with each having a serial dependency on Task A.
If task A fails during a scheduled run, which statement describes the results of this run?
- A. Tasks B and C will be skipped; task A will not commit any changes because of stage failure.
- B. Because all tasks are managed as a dependency graph, no changes will be committed to the Lakehouse until all tasks have successfully been completed.
- C. Unless all tasks complete successfully, no changes will be committed to the Lakehouse; because task A failed, all commits will be rolled back automatically.
- D. Tasks B and C will attempt to run as configured; any changes made in task A will be rolled back due to task failure.
- E. Tasks B and C will be skipped; some logic expressed in task A may have been committed before task failure.
Answer: E
Explanation:
When a Databricks job runs multiple tasks with dependencies, the tasks are executed in a dependency graph. If a task fails, the downstream tasks that depend on it are skipped and marked as Upstream failed. However, the failed task may have already committed some changes to the Lakehouse before the failure occurred, and those changes are not rolled back automatically. Therefore, the job run may result in a partial update of the Lakehouse. To avoid this, you can use the transactional writes feature of Delta Lake to ensure that the changes are only committed when the entire job run succeeds. Alternatively, you can use the Run if condition to configure tasks to run even when some or all of their dependencies have failed, allowing your job to recover from failures and continue running.
NEW QUESTION # 63
A table named user_ltv is being used to create a view that will be used by data analysts 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:
email STRING, age INT, ltv INT
The following view definition is executed:
An analyst who is not a member of the auditing group executes the following query:
SELECT * FROM user_ltv_no_minors
Which statement describes the results returned by this query?
- A. All values for the age column will be returned as null values, all other columns will be returned with the values in user_ltv.
- B. All age values less than 18 will be returned as null values all other columns will be returned with the values in user_ltv.
- C. All columns will be displayed normally for those records that have an age greater than 17; records not meeting this condition will be omitted.
- D. All records from all columns will be displayed with the values in user_ltv.
- E. 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: E
Explanation:
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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 # 64
Which statement describes a key benefit of an end-to-end test?
- A. It closely simulates real world usage of your application.
- B. It makes it easier to automate your test suite
- C. It provides testing coverage for all code paths and branches.
- D. It pinpoint errors in the building blocks of your application.
Answer: A
Explanation:
End-to-end testing is a methodology used to test whether the flow of an application, from start to finish, behaves as expected. The key benefit of an end-to-end test is that it closely simulates real- world, user behavior, ensuring that the system as a whole operates correctly.
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NEW QUESTION # 65
Which method can be used to determine the total wall-clock time it took to execute a query?
- A. In the Spark UI, take the job duration of the longest-running job associated with that query.
- B. Open the Query Profiler associated with that query and use the Total wall-clock duration metric.
- C. In the Spark UI, take the sum of all task durations that ran across all stages for all jobs associated with that query.
- D. Open the Query Profiler associated with that query and use the Aggregated task time metric.
Answer: B
Explanation:
The Query Profiler in Databricks SQL and notebooks provides a detailed breakdown of query performance metrics. The "Total wall-clock duration" metric directly represents the total elapsed time from query start to completion, including all execution, planning, and waiting stages. In contrast, "Aggregated task time" reflects the cumulative duration across all parallel tasks, which does not equal the total elapsed wall time since tasks often run concurrently. Using job duration from Spark UI can underestimate or overestimate runtime when queries span multiple jobs.
Therefore, the Query Profiler's total wall-clock duration is the officially documented method to determine actual query execution time.
NEW QUESTION # 66
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.
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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 3 table; old values are maintained as a new column alongside the current value.
- 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.
- E. 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.
Answer: E
NEW QUESTION # 67
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