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Databricks-Certified-Professional-Data-Engineer Zertifizierungsfragen & Databricks-Certified-Professional-Data-Engineer Fragen Und Antworten

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>> Databricks-Certified-Professional-Data-Engineer Zertifizierungsfragen <<

Databricks-Certified-Professional-Data-Engineer Fragen Und Antworten - Databricks-Certified-Professional-Data-Engineer Examsfragen

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Databricks Certified Professional Data Engineer Exam Databricks-Certified-Professional-Data-Engineer Prüfungsfragen mit Lösungen (Q10-Q15):

10. Frage
You have noticed the Data scientist team is using the notebook versioning feature with git integra-tion, you have recommended them to switch to using Databricks Repos, which of the below reasons could be the reason the why the team needs to switch to Databricks Repos.

A. Databricks Repos automatically saves changes
B. Databricks Repos has a built-in version control system
C. Databricks Repos allows merge and conflict resolution
D. Databricks Repos allow you to add comments and select the changes you want to commit.
E. Databricks Repos allows multiple users to make changes

Antwort: D

Begründung:
Explanation
The answer is Databricks Repos allow you to add comments and select the changes you want to commit.

11. Frage
The marketing team is looking to share data in an aggregate table with the sales organization, but the field names used by the teams do not match, and a number of marketing specific fields have not been approval for the sales org.
Which of the following solutions addresses the situation while emphasizing simplicity?

A. Create a new table with the required schema and use Delta Lake's DEEP CLONE functionality to sync up changes committed to one table to the corresponding table.
B. Create a view on the marketing table selecting only these fields approved for the sales team alias the names of any fields that should be standardized to the sales naming conventions.
C. Add a parallel table write to the current production pipeline, updating a new sales table that varies as required from marketing table.
D. Use a CTAS statement to create a derivative table from the marketing table configure a production jon to propagation changes.

Antwort: B

Begründung:
Creating a view is a straightforward solution that can address the need for field name standardization and selective field sharing between departments. A view allows for presenting a transformed version of the underlying data without duplicating it. In this scenario, the view would only include the approved fields for the sales team and rename any fields as per their naming conventions.
References:
* Databricks documentation on using SQL views in Delta Lake: https://docs.databricks.com/delta/quick- start.html#sql-views

12. Frage
The DevOps team has configured a production workload as a collection of notebooks scheduled to run daily using the Jobs UI. A new data engineering hire is onboarding to the team and has requested access to one of these notebooks to review the production logic.
What are the maximum notebook permissions that can be granted to the user without allowing accidental changes to production code or data?

A. Can Manage
B. Can Read
C. Can Edit
D. No permissions
E. Can Run

Antwort: B

Begründung:
This is the correct answer because it is the maximum notebook permissions that can be granted to the user without allowing accidental changes to production code or data. Notebook permissions are used to control access to notebooks in Databricks workspaces. There are four types of notebook permissions: Can Manage, Can Edit, Can Run, and Can Read. Can Manage allows full control over the notebook, including editing, running, deleting, exporting, and changing permissions. Can Edit allows modifying and running the notebook, but not changing permissions or deleting it. Can Run allows executing commands in an existing cluster attached to the notebook, but not modifying or exporting it. Can Read allows viewing the notebook content, but not running or modifying it. In this case, granting Can Read permission to the user will allow them to review the production logic in the notebook without allowing them to make any changes to it or run any commands that may affect production data. Verified Reference: [Databricks Certified Data Engineer Professional], under "Databricks Workspace" section; Databricks Documentation, under "Notebook permissions" section.

13. Frage
You are asked to setup an AUTO LOADER to process the incoming data, this data arrives in JSON format and get dropped into cloud object storage and you are required to process the data as soon as it arrives in cloud storage, which of the following statements is correct

A. AUTO LOADER needs to be converted to a Structured stream process
B. AUTO LOADER has to be triggered from an external process when the file arrives in the cloud storage
C. AUTO LOADER can support file notification method so it can process data as it arrives
D. AUTO LOADER can only process continuous data when stored in DELTA lake
E. AUTO LOADER is native to DELTA lake it cannot support external cloud object storage

Antwort: C

Begründung:
Explanation
Auto Loader supports two modes when ingesting new files from cloud object storage Directory listing: Auto Loader identifies new files by listing the input directory, and uses a directory polling approach.
File notification: Auto Loader can automatically set up a notification service and queue service that subscribe to file events from the input directory.
Diagram Description automatically generated

File notification is more efficient and can be used to process the data in real-time as data arrives in cloud object storage.
Choosing between file notification and directory listing modes | Databricks on AWS

14. Frage
The following code has been migrated to a Databricks notebook from a legacy workload:

The code executes successfully and provides the logically correct results, however, it takes over 20 minutes to extract and load around 1 GB of data.
Which statement is a possible explanation for this behavior?

A. Python will always execute slower than Scala on Databricks. The run.py script should be refactored to Scala.
B. Instead of cloning, the code should use %sh pip install so that the Python code can get executed in parallel across all nodes in a cluster.
C. %sh does not distribute file moving operations; the final line of code should be updated to use %fs instead.
D. %sh triggers a cluster restart to collect and install Git. Most of the latency is related to cluster startup time.
E. %sh executes shell code on the driver node. The code does not take advantage of the worker nodes or Databricks optimized Spark.

Antwort: E

Begründung:
https://www.databricks.com/blog/2020/08/31/introducing-the-databricks-web-terminal.html The code is using %sh to execute shell code on the driver node. This means that the code is not taking advantage of the worker nodes or Databricks optimized Spark. This is why the code is taking longer to execute. A better approach would be to use Databricks libraries and APIs to read and write data from Git and DBFS, and to leverage the parallelism and performance of Spark. For example, you can use the Databricks Connect feature to run your Python code on a remote Databricks cluster, or you can use the Spark Git Connector to read data from Git repositories as Spark DataFrames.

15. Frage
......

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