FREE PDF QUIZ DATABRICKS - DATABRICKS-CERTIFIED-PROFESSIONAL-DATA-ENGINEER - ACCURATE DATABRICKS CERTIFIED PROFESSIONAL DATA ENGINEER EXAM EXAM INTRODUCTION

Free PDF Quiz Databricks - Databricks-Certified-Professional-Data-Engineer - Accurate Databricks Certified Professional Data Engineer Exam Exam Introduction

Free PDF Quiz Databricks - Databricks-Certified-Professional-Data-Engineer - Accurate Databricks Certified Professional Data Engineer Exam Exam Introduction

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Databricks Certified Professional Data Engineer (Databricks-Certified-Professional-Data-Engineer) certification exam is a highly sought-after certification for individuals who want to demonstrate their expertise in building reliable, scalable, and performant data pipelines using Databricks. Databricks Certified Professional Data Engineer Exam certification is designed to validate the skills and knowledge required to design, implement, and maintain data pipelines for big data processing using Databricks.

To prepare for the exam, Databricks offers a range of training resources, including online courses, workshops, and certification bootcamps. These resources cover topics such as data engineering, data science, machine learning, and data analytics on the Databricks platform. Additionally, candidates can also access the Databricks Academy, which provides self-paced learning modules and practice exams to help them prepare for the certification exam.

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Databricks Certified Professional Data Engineer Exam Sample Questions (Q20-Q25):

NEW QUESTION # 20
Review the following error traceback:

Which statement describes the error being raised?

  • A. The code executed was PvSoark but was executed in a Scala notebook.
  • B. There is a type error because a DataFrame object cannot be multiplied.
  • C. There is a syntax error because the heartrate column is not correctly identified as a column.
  • D. There is no column in the table named heartrateheartrateheartrate
  • E. There is a type error because a column object cannot be multiplied.

Answer: D

Explanation:
Explanation
The error being raised is an AnalysisException, which is a type of exception that occurs when Spark SQL cannot analyze or execute a query due to some logical or semantic error1. In this case, the error message indicates that the query cannot resolve the column name 'heartrateheartrateheartrate' given the input columns
'heartrate' and 'age'. This means that there is no column in the table named 'heartrateheartrateheartrate', and the query is invalid. A possible cause of this error is a typo or a copy-paste mistake in the query. To fix this error, the query should use a valid column name that exists in the table, such as
'heartrate'. References: AnalysisException


NEW QUESTION # 21
Which of the following developer operations in the CI/CD can only be implemented through a GIT provider when using Databricks Repos.

  • A. Create a new branch
  • B. Commit and push code
  • C. Trigger Databricks Repos pull API to update the latest version
  • D. Pull request and review process
  • E. Create and edit code

Answer: D

Explanation:
Explanation
The answer is Pull request and review process, please note: the question is asking for steps that are being implemented in GIT provider not Databricks Repos.
See below diagram to understand the role of Databricks Repos and Git provider plays when building a CI/CD workdlow.
All the steps highlighted in yellow can be done Databricks Repo, all the steps highlighted in Gray are done in a git provider like Github or Azure Devops.
Diagram Description automatically generated

Bottom of Form
Top of Form


NEW QUESTION # 22
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 an average of 8 minutes to start the cluster, what feature can be used to start the cluster in a timely fashion?

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

Answer: E

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 and follow some best practices,
https://www.youtube.com/watch?v=FVtITxOabxg&ab_channel=DatabricksAcademy


NEW QUESTION # 23
Which of the following is true, when building a Databricks SQL dashboard?

  • A. A dashboard can only use results from one query
  • B. A dashboard can only have one refresh schedule
  • C. Only one visualization can be developed with one query result
  • D. A dashboard can only connect to one schema/Database
  • E. More than one visualization can be developed using a single query result

Answer: E

Explanation:
Explanation
the answer is, More than one visualization can be developed using a single query result.
In the query editor pane + Add visualization tab can be used for many visualizations for a single query result.
Graphical user interface, text, application Description automatically generated


NEW QUESTION # 24
The data governance team is reviewing user for deleting records for compliance with GDPR. The following logic has been implemented to propagate deleted requests from the user_lookup table to the user aggregate table.

Assuming that user_id is a unique identifying key and that all users have requested deletion have been removed from the user_lookup table, which statement describes whether successfully executing the above logic guarantees that the records to be deleted from the user_aggregates table are no longer accessible and why?

  • A. Yes: Delta Lake ACID guarantees provide assurance that the DELETE command successed fully and permanently purged these records.
  • B. No: the Delta Lake DELETE command only provides ACID guarantees when combined with the MERGE INTO command
  • C. No: the change data feed only tracks inserts and updates not deleted records.
  • D. No: files containing deleted records may still be accessible with time travel until a BACUM command is used to remove invalidated data files.

Answer: D

Explanation:
The DELETE operation in Delta Lake is ACID compliant, which means that once the operation is successful, the records are logically removed from the table. However, the underlying files that contained these records may still exist and be accessible via time travel to older versions of the table. To ensure that these records are physically removed and compliance with GDPR is maintained, a VACUUM command should be used to clean up these data files after a certainretention period. The VACUUM command will remove the files from the storage layer, and after this, the records will no longer be accessible.


NEW QUESTION # 25
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