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  3. Databricks-Certified-Professional-Data-Engineer

Databricks Databricks-Certified-Professional-Data-Engineer Exam (page: 1)
Databricks Certified Data Engineer Professional
Updated on: 09-Apr-2026

Viewing Page 1 of 37
Databricks-Certified-Professional-Data-Engineer PDF 339 Questions

An upstream system has been configured to pass the date for a given batch of data to the Databricks Jobs API as a parameter. The notebook to be scheduled will use this parameter to load data with the following code: df = spark.read.format("parquet").load(f"/mnt/source/(date)")
Which code block should be used to create the date Python variable used in the above code block?

  1. date = spark.conf.get("date")
  2. input_dict = input()
    date= input_dict["date"]
  3. import sys
    date = sys.argv[1]
  4. date = dbutils.notebooks.getParam("date")
  5. dbutils.widgets.text("date", "null")
    date = dbutils.widgets.get("date")

Answer(s): E



The Databricks workspace administrator has configured interactive clusters for each of the data engineering groups. To control costs, clusters are set to terminate after 30 minutes of inactivity. Each user should be able to execute workloads against their assigned clusters at any time of the day.
Assuming users have been added to a workspace but not granted any permissions, which of the following describes the minimal permissions a user would need to start and attach to an already configured cluster.

  1. "Can Manage" privileges on the required cluster
  2. Workspace Admin privileges, cluster creation allowed, "Can Attach To" privileges on the required cluster
  3. Cluster creation allowed, "Can Attach To" privileges on the required cluster
  4. "Can Restart" privileges on the required cluster
  5. Cluster creation allowed, "Can Restart" privileges on the required cluster

Answer(s): D



When scheduling Structured Streaming jobs for production, which configuration automatically recovers from query failures and keeps costs low?


  1. Cluster: New Job Cluster;
    Retries: Unlimited;
    Maximum Concurrent Runs: Unlimited

  2. Cluster: New Job Cluster;
    Retries: None;
    Maximum Concurrent Runs: 1

  3. Cluster: Existing All-Purpose Cluster;
    Retries: Unlimited;
    Maximum Concurrent Runs: 1

  4. Cluster: New Job Cluster;
    Retries: Unlimited;
    Maximum Concurrent Runs: 1

  5. Cluster: Existing All-Purpose Cluster;
    Retries: None;
    Maximum Concurrent Runs: 1

Answer(s): D



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?

  1. The total average temperature across all sensors exceeded 120 on three consecutive executions of the query
  2. The recent_sensor_recordings table was unresponsive for three consecutive runs of the query
  3. The source query failed to update properly for three consecutive minutes and then restarted
  4. The maximum temperature recording for at least one sensor exceeded 120 on three consecutive executions of the query
  5. The average temperature recordings for at least one sensor exceeded 120 on three consecutive executions of the query

Answer(s): E



A junior developer complains that the code in their notebook isn't producing the correct results in the development environment. A shared screenshot reveals that while they're using a notebook versioned with Databricks Repos, they're using a personal branch that contains old logic. The desired branch named dev-2.3.9 is not available from the branch selection dropdown.
Which approach will allow this developer to review the current logic for this notebook?

  1. Use Repos to make a pull request use the Databricks REST API to update the current branch to dev-2.3.9
  2. Use Repos to pull changes from the remote Git repository and select the dev-2.3.9 branch.
  3. Use Repos to checkout the dev-2.3.9 branch and auto-resolve conflicts with the current branch
  4. Merge all changes back to the main branch in the remote Git repository and clone the repo again
  5. Use Repos to merge the current branch and the dev-2.3.9 branch, then make a pull request to sync with the remote repository

Answer(s): B



Viewing Page 1 of 37



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