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Die zertifizierte Zertifizierungsprüfung für Datenbanken zertifizierte Dateningenieur ist eine strenge und herausfordernde Prüfung, die ein tiefes Verständnis der Konzepte für Daten technische und der Datenbankplattform erfordert. Die Kandidaten müssen über eine starke Grundlage für Informatik und Datentechnik sowie praktische Erfahrung mit der Datenbankplattform verfügen. Die Prüfung besteht aus Multiple-Choice-Fragen und praktischen Übungen, die die Fähigkeit eines Kandidaten zum Entwerfen, Erstellen und Wartieren von Datenpipelines mithilfe der Datenbankplattform testen.

Durch das Bestehen der DCPDE -Prüfung können Dateningenieure ihre Kenntnisse in der Verwendung der Datenbankplattform nachweisen, um skalierbare und zuverlässige Datenpipelines zu erstellen. Diese Zertifizierung kann den Dateningenieuren helfen, ihre Karriere voranzutreiben und ihr Verdienstpotential zu erhöhen, indem sie ihre Fachkenntnisse im Datenentwicklung auf Datenbanken vorstellen.

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

Databricks-Certified-Professional-Data-Engineer Unterlagen mit echte Prüfungsfragen der Databricks Zertifizierung

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DATABRICKS Certified Professional Data Engineer ist eine Zertifizierungsprüfung, die die Fähigkeiten und Kenntnisse testet, die zum Entwerfen und Implementieren von Datenlösungen mithilfe von Datenbanken erforderlich sind. Databricks ist eine Cloud-basierte Datenplattform, mit der Unternehmen große Datenmengen verwalten und verarbeiten können. Die Zertifizierungsprüfung ist für Dateningenieure ausgelegt, die für die Erstellung und Wartung von Datenpipelines, die Verwaltung von Datenspeichern und die Implementierung von Datenlösungen verantwortlich sind.

Databricks Certified Professional Data Engineer Exam Databricks-Certified-Professional-Data-Engineer Prüfungsfragen mit Lösungen (Q72-Q77):

72. Frage
An upstream source writes Parquet data as hourly batches to directories named with the current date. A nightly batch job runs the following code to ingest all data from the previous day as indicated by thedatevariable:

Assume that the fieldscustomer_idandorder_idserve as a composite key to uniquely identify each order.
If the upstream system is known to occasionally produce duplicate entries for a single order hours apart, which statement is correct?

A. Each write to the orders table will only contain unique records; if existing records with the same key are present in the target table, these records will be overwritten.
B. Each write to the orders table will only contain unique records, and only those records without duplicates in the target table will be written.
C. Each write to the orders table will only contain unique records; if existing records with the same key are present in the target table, the operation will tail.
D. Each write to the orders table will run deduplication over the union of new and existing records, ensuring no duplicate records are present.
E. Each write to the orders table will only contain unique records, but newly written records may have duplicates already present in the target table.

Antwort: E

Begründung:
This is the correct answer because the code uses the dropDuplicates method to remove any duplicate records within each batch of data before writing to the orders table. However, this method does not check for duplicates across different batches or in the target table, so it is possible that newly written records may have duplicates already present in the target table. To avoid this, a better approach would be to use Delta Lake and perform an upsert operation using mergeInto. Verified References: [Databricks Certified Data Engineer Professional], under "Delta Lake" section; Databricks Documentation, under "DROP DUPLICATES" section.

73. Frage
A user wants to use DLT expectations to validate that a derived table report contains all records from the source, included in the table validation_copy.
The user attempts and fails to accomplish this by adding an expectation to the report table definition.
Which approach would allow using DLT expectations to validate all expected records are present in this table?

A. Define a temporary table that perform a left outer join on validation_copy and report, and define an expectation that no report key values are null
B. Define a function that performs a left outer join on validation_copy and report and report, and check against the result in a DLT expectation for the report table
C. Define a SQL UDF that performs a left outer join on two tables, and check if this returns null values for report key values in a DLT expectation for the report table.
D. Define a view that performs a left outer join on validation_copy and report, and reference this view in DLT expectations for the report table

Antwort: D

Begründung:
To validate that all records from the source are included in the derived table, creating a view that performs a left outer join between the validation_copy table and the report table is effective. The view can highlight any discrepancies, such as null values in the report table's key columns, indicating missing records. This view can then be referenced in DLT (Delta Live Tables) expectations for the report table to ensure data integrity. This approach allows for a comprehensive comparison between the source and the derived table.
Reference:
Databricks Documentation on Delta Live Tables and Expectations: Delta Live Tables Expectations

74. Frage
What steps need to be taken to set up a DELTA LIVE PIPELINE as a job using the workspace UI?

A. DELTA LIVE TABLES do not support job cluster
B. Select Workflows UI and Delta live tables tab, under task type select Delta live tables pipeline and select the pipeline JSON file
C. Select Workflows UI and Delta live tables tab, under task type select Delta live tables pipeline and select the notebook
D. Use Pipeline creation UI, select a new pipeline and job cluster

Antwort: C

Begründung:
Explanation
The answer is,
Select Workflows UI and Delta live tables tab, under task type select Delta live tables pipeline and select the notebook.
Create a pipeline
To create a new pipeline using the Delta Live Tables notebook:
1.Click Workflows in the sidebar, click the Delta Live Tables tab, and click Create Pipeline.
2.Give the pipeline a name and click to select a notebook.
3.Optionally enter a storage location for output data from the pipeline. The system uses a de-fault location if you leave Storage Location empty.
4.Select Triggered for Pipeline Mode.
5.Click Create.
The system displays the Pipeline Details page after you click Create. You can also access your pipeline by clicking the pipeline name in the Delta Live Tables tab.

75. Frage
The security team is exploring whether or not the Databricks secrets module can be leveraged for connecting to an external database.
After testing the code with all Python variables being defined with strings, they upload the password to the secrets module and configure the correct permissions for the currently active user. They then modify their code to the following (leaving all other variables unchanged).

Which statement describes what will happen when the above code is executed?

A. An interactive input box will appear in the notebook; if the right password is provided, the connection will succeed and the password will be printed in plain text.
B. An interactive input box will appear in the notebook; if the right password is provided, the connection will succeed and the encoded password will be saved to DBFS.
C. The connection to the external table will succeed; the string "redacted" will be printed.
D. The connection to the external table will fail; the string "redacted" will be printed.
E. The connection to the external table will succeed; the string value of password will be printed in plain text.

Antwort: C

Begründung:
This is the correct answer because the code is using the dbutils.secrets.get method to retrieve the password from the secrets module and store it in a variable. The secrets module allows users to securely store and access sensitive information such as passwords, tokens, or API keys. The connection to the external table will succeed because the password variable will contain the actual password value. However, when printing the password variable, the string "redacted" will be displayed instead of the plain text password, as a security measure to prevent exposing sensitive information in notebooks. Verified References: [Databricks Certified Data Engineer Professional], under "Security & Governance" section; Databricks Documentation, under
"Secrets" section.

76. Frage
The data engineering team has configured a Databricks SQL query and alert to monitor the values in a Delta Lake table. Therecent_sensor_recordingstable contains an identifyingsensor_idalongside thetimestampandtemperaturefor 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 whenmean (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?

A. Therecent_sensor_recordingstable was unresponsive for three consecutive runs of the query
B. The average temperature recordings for at least one sensor exceeded 120 on three consecutive executions of the query
C. The source query failed to update properly for three consecutive minutes and then restarted
D. The maximum temperature recording for at least one sensor exceeded 120 on three consecutive executions of the query
E. The total average temperature across all sensors exceeded 120 on three consecutive executions of the query

Antwort: B

Begründung:
This is the correct answer because the query is using a GROUP BY clause on the sensor_id column, which means it will calculate the mean temperature for each sensor separately. The alert will trigger when the mean temperature for any sensor is greater than 120, which means at least one sensor had an average temperature above 120 for three consecutive minutes. The alert will stop when the mean temperature for all sensors drops below 120. Verified References: [Databricks Certified Data Engineer Professional], under "SQL Analytics" section; Databricks Documentation, under "Alerts" section.

77. Frage
......

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Eli Ford Eli Ford

Biography

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Databricks-Certified-Professional-Data-Engineer Unterlagen mit echte Prüfungsfragen der Databricks Zertifizierung

Databricks Certified Professional Data Engineer Exam Databricks-Certified-Professional-Data-Engineer Prüfungsfragen mit Lösungen (Q72-Q77):

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