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Databricks Databricks-Certified-Professional-Data-Engineer Exam | Updated Databricks-Certified-Professional-Data-Engineer Dumps - High Pass Rate Test Databricks-Certified-Professional-Data-Engineer Objectives Pdf

Databricks Databricks-Certified-Professional-Data-Engineer Exam | Updated Databricks-Certified-Professional-Data-Engineer Dumps - High Pass Rate Test Databricks-Certified-Professional-Data-Engineer Objectives Pdf

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Databricks Certified Professional Data Engineer Certification Exam is created to challenge data engineers with the significant knowledge of Databricks’ data engineering principles and techniques. To become Databricks certified, a candidate must pass the online certification exam designed for data engineers. Databricks-Certified-Professional-Data-Engineer Exam is scenario-based, comprises of 80 multiple-choice questions, and has a time limit of 120 minutes. The Certification exam tests the candidate's knowledge in topics such as data ingestion, data processing, data engineering, ETL, and data warehousing.

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Databricks Certified Professional Data Engineer Exam Sample Questions (Q66-Q71):

NEW QUESTION # 66
You have written a notebook to generate a summary data set for reporting, Notebook was scheduled using the job cluster, but you realized it takes 8 minutes to start the cluster, what feature can be used to start the cluster in a timely fashion so your job can run immediatley?

  • A. Use the Databricks cluster pools feature to reduce the startup time
  • B. Use Databricks Premium edition instead of Databricks standard edition
  • C. Setup an additional job to run ahead of the actual job so the cluster is running second job starts
  • D. Pin the cluster in the cluster UI page so it is always available to the jobs
  • E. Disable auto termination so the cluster is always running

Answer: A

Explanation:
Explanation
Cluster pools allow us to reserve VM's ahead of time, when a new job cluster is created VM are grabbed from the pool. Note: when the VM's are waiting to be used by the cluster only cost incurred is Azure. Databricks run time cost is only billed once VM is allocated to a cluster.
Here is a demo of how to setup a pool and follow some best practices,
Graphical user interface, text Description automatically generated


NEW QUESTION # 67
Where in the Spark UI can one diagnose a performance problem induced by not leveraging predicate push-down?

  • A. In the Query Detail screen, by interpreting the Physical Plan
  • B. In the Stage's Detail screen, in the Completed Stages table, by noting the size of data read from the Input column
  • C. In the Executor's log file, by gripping for "predicate push-down"
  • D. In the Delta Lake transaction log. by noting the column statistics
  • E. In the Storage Detail screen, by noting which RDDs are not stored on disk

Answer: A

Explanation:
This is the correct answer because it is where in the Spark UI one can diagnose a performance problem induced by not leveraging predicate push-down. Predicate push-down is an optimization technique that allows filtering data at the source before loading it into memory or processing it further. This can improve performance and reduce I/O costs by avoiding reading unnecessary data. To leverage predicate push-down, one should use supported data sources and formats, such as Delta Lake, Parquet, or JDBC, and use filter expressions that can be pushed down to the source. To diagnose a performance problem induced by not leveraging predicate push-down, one can use the Spark UI to access the Query Detail screen, which shows information about a SQL query executed on a Spark cluster. The Query Detail screen includes the Physical Plan, which is the actual plan executed by Spark to perform the query. The Physical Plan shows the physical operators used by Spark, such as Scan, Filter, Project, or Aggregate, and their input and output statistics, such as rows and bytes. By interpreting the Physical Plan, one can see if the filter expressions are pushed down to the source or not, and how much data is read or processed by each operator. Verified Reference: [Databricks Certified Data Engineer Professional], under "Spark Core" section; Databricks Documentation, under "Predicate pushdown" section; Databricks Documentation, under "Query detail page" section.


NEW QUESTION # 68
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 nameddev-2.3.9is not available from the branch selection dropdown.
Which approach will allow this developer to review the current logic for this notebook?

  • A. Use Repos to make a pull request use the Databricks REST API to update the current branch to dev-2.3.9
  • B. Merge all changes back to the main branch in the remote Git repository and clone the repo again
  • C. Use Repos to pull changes from the remote Git repository and select the dev-2.3.9 branch.
  • D. 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
  • E. Use Repos to checkout the dev-2.3.9 branch and auto-resolve conflicts with the current branch

Answer: C

Explanation:
This is the correct answer because it will allow the developer to update their local repository with the latest changes from the remote repository and switch to the desired branch. Pulling changes will not affect the current branch or create any conflicts, as it will only fetch the changes and not merge them. Selecting the dev-2.3.9 branch from the dropdown will checkout that branch and display its contents in the notebook.
Verified References: [Databricks Certified Data Engineer Professional], under "Databricks Tooling" section; Databricks Documentation, under "Pull changes from a remote repository" section.


NEW QUESTION # 69
The data science team has created and logged a production model using MLflow. The following code correctly imports and applies the production model to output the predictions as a new DataFrame namedpredswith the schema "customer_id LONG, predictions DOUBLE, date DATE".

The data science team would like predictions saved to a Delta Lake table with the ability to compare all predictions across time. Churn predictions will be made at most once per day.
Which code block accomplishes this task while minimizing potential compute costs?

  • A.
  • B. preds.write.mode("append").saveAsTable("churn_preds")
  • C. preds.write.format("delta").save("/preds/churn_preds")
  • D.
  • E.

Answer: B


NEW QUESTION # 70
How to determine if a table is a managed table vs external table?

  • A. Run SQL command DESCRIBE EXTENDED table_name and check type
  • B. All managed tables are stored in unity catalog
  • C. All external tables are stored in data lake, managed tables are stored in DELTA lake
  • D. Run IS_MANAGED('table_name') function
  • E. Run SQL command SHOW TABLES to see the type of the table

Answer: A

Explanation:
Explanation
The answer is Run SQL command DESCRIBE EXTENDED table_name and check type Example of External table Graphical user interface, text, application Description automatically generated

Example of managed table
Graphical user interface, text, application, Teams Description automatically generated


NEW QUESTION # 71
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