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Free Certified Data Engineer Professional Exam Braindumps
Certified Data Engineer Professional Questions 89–96 (page: 13)

Updated On: 16-Apr-2026
Page 13 of 44
PDF with 339 Questions

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?

  1. Because all tasks are managed as a dependency graph, no changes will be committed to the Lakehouse until all tasks have successfully been completed.
  2. Tasks B and C will attempt to run as configured; any changes made in task A will be rolled back due to task failure.
  3. Unless all tasks complete successfully, no changes will be committed to the Lakehouse; because task A failed, all commits will be rolled back automatically.
  4. Tasks B and C will be skipped; some logic expressed in task A may have been committed before task failure.
  5. Tasks B and C will be skipped; task A will not commit any changes because of stage failure.

Answer(s): D



Which statement regarding Spark configuration on the Databricks platform is true?

  1. The Databricks REST API can be used to modify the Spark configuration properties for an interactive cluster without interrupting jobs currently running on the cluster.
  2. Spark configurations set within a notebook will affect all SparkSessions attached to the same interactive cluster.
  3. Spark configuration properties can only be set for an interactive cluster by creating a global init script.
  4. Spark configuration properties set for an interactive cluster with the Clusters UI will impact all notebooks attached to that cluster.
  5. When the same Spark configuration property is set for an interactive cluster and a notebook attached to that cluster, the notebook setting will always be ignored.

Answer(s): D



A developer has successfully configured their credentials for Databricks Repos and cloned a remote Git repository. They do not have privileges to make changes to the main branch, which is the only branch currently visible in their workspace.

Which approach allows this user to share their code updates without the risk of overwriting the work of their teammates?

  1. Use Repos to checkout all changes and send the git diff log to the team.
  2. Use Repos to create a fork of the remote repository, commit all changes, and make a pull request on the source repository.
  3. Use Repos to pull changes from the remote Git repository; commit and push changes to a branch that appeared as changes were pulled.
  4. Use Repos to merge all differences and make a pull request back to the remote repository.
  5. Use Repos to create a new branch, commit all changes, and push changes to the remote Git repository.

Answer(s): E



In order to prevent accidental commits to production data, a senior data engineer has instituted a policy that all development work will reference clones of Delta Lake tables. After testing both DEEP and SHALLOW CLONE, development tables are created using SHALLOW CLONE.

A few weeks after initial table creation, the cloned versions of several tables implemented as Type 1 Slowly Changing Dimension (SCD) stop working. The transaction logs for the source tables show that VACUUM was run the day before.

Which statement describes why the cloned tables are no longer working?

  1. Because Type 1 changes overwrite existing records, Delta Lake cannot guarantee data consistency for cloned tables.
  2. Running VACUUM automatically invalidates any shallow clones of a table; DEEP CLONE should always be used when a cloned table will be repeatedly queried.
  3. Tables created with SHALLOW CLONE are automatically deleted after their default retention threshold of 7 days.
  4. The metadata created by the CLONE operation is referencing data files that were purged as invalid by the VACUUM command.
  5. The data files compacted by VACUUM are not tracked by the cloned metadata; running REFRESH on the cloned table will pull in recent changes.

Answer(s): D



You are performing a join operation to combine values from a static userLookup table with a streaming DataFrame streamingDF.

Which code block attempts to perform an invalid stream-static join?

  1. userLookup.join(streamingDF, ["userid"], how="inner")
  2. streamingDF.join(userLookup, ["user_id"], how="outer")
  3. streamingDF.join(userLookup, ["user_id"], how="left")
  4. streamingDF.join(userLookup, ["userid"], how="inner")
  5. userLookup.join(streamingDF, ["user_id"], how="right")

Answer(s): B



Spill occurs as a result of executing various wide transformations. However, diagnosing spill requires one to proactively look for key indicators.

Where in the Spark UI are two of the primary indicators that a partition is spilling to disk?

  1. Query's detail screen and Job's detail screen
  2. Stage's detail screen and Executor's log files
  3. Driver's and Executor's log files
  4. Executor's detail screen and Executor's log files
  5. Stage's detail screen and Query's detail screen

Answer(s): B



A task orchestrator has been configured to run two hourly tasks. First, an outside system writes Parquet data to a directory mounted at /mnt/raw_orders/. After this data is written, a Databricks job containing the following code is executed:



Assume that the fields customer_id and order_id serve as a composite key to uniquely identify each order, and that the time field indicates when the record was queued in the source system.

If the upstream system is known to occasionally enqueue duplicate entries for a single order hours apart, which statement is correct?

  1. Duplicate records enqueued more than 2 hours apart may be retained and the orders table may contain duplicate records with the same customer_id and order_id.
  2. All records will be held in the state store for 2 hours before being deduplicated and committed to the orders table.
  3. The orders table will contain only the most recent 2 hours of records and no duplicates will be present.
  4. Duplicate records arriving more than 2 hours apart will be dropped, but duplicates that arrive in the same batch may both be written to the orders table.
  5. The orders table will not contain duplicates, but records arriving more than 2 hours late will be ignored and missing from the table.

Answer(s): A



A junior data engineer is migrating a workload from a relational database system to the Databricks Lakehouse. The source system uses a star schema, leveraging foreign key constraints and multi-table inserts to validate records on write.

Which consideration will impact the decisions made by the engineer while migrating this workload?

  1. Databricks only allows foreign key constraints on hashed identifiers, which avoid collisions in highly-parallel writes.
  2. Databricks supports Spark SQL and JDBC; all logic can be directly migrated from the source system without refactoring.
  3. Committing to multiple tables simultaneously requires taking out multiple table locks and can lead to a state of deadlock.
  4. All Delta Lake transactions are ACID compliant against a single table, and Databricks does not enforce foreign key constraints.
  5. Foreign keys must reference a primary key field; multi-table inserts must leverage Delta Lake's upsert functionality.

Answer(s): D



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