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Microsoft DP-203 Deutsch Exam Overview:
| Certification Vendor: | Microsoft |
|---|---|
| Exam Name: | Data Engineering on Microsoft Azure |
| Exam Number: | DP-203 |
| Certificate Validity Period: | 1 year |
| Exam Price: | $165 USD |
| Exam Format: | Scenario-based Questions, Case Study, Multiple Choice, Drag and Drop |
| Related Certifications: | Microsoft Certified: Azure Data Engineer Associate |
| Exam Duration: | 100-120 |
| Real Exam Qty: | 40-60 |
| Passing Score: | 700/1000 |
| Available Languages: | Chinese (Simplified), Spanish, German, Italian, Portuguese (Brazil), Japanese, English, French, Korean |
| Sample Questions: |  |
| Exam Way: | Online proctored exam or test center delivery |
| Pre Condition: | Experience with Azure data services, SQL, Python or Scala is recommended. No mandatory prerequisite exam. |
| Official Syllabus URL: | https://learn.microsoft.com/en-us/credentials/certifications/resources/study-guides/dp-203 |
Microsoft DP-203 Deutsch Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Design and Implement Data Security | 10-15% | - Implement data security
|
| Topic 2: Monitor and Optimize Data Storage and Processing | 10-15% | - Monitor data solutions
|
| Topic 3: Design and Develop Data Processing | 25-30% | - Develop batch processing solutions
|
| Topic 4: Design and Implement Data Storage | 40-45% | - Design a data storage structure
|
Microsoft DP-203 Deutsch: What Candidates Ask Us
Experience with Azure data services, SQL, Python or Scala is recommended. No mandatory prerequisite exam. These requirements belong to Microsoft and may be revised, so check the current rules on the official exam page: https://learn.microsoft.com/en-us/credentials/certifications/resources/study-guides/dp-203 before committing to a Microsoft Data Engineering on Microsoft Azure (DP-203 Deutsch Version) registration.
The Microsoft Data Engineering on Microsoft Azure (DP-203 Deutsch Version) exam contains 40-60 questions within 100-120. Knowing the content matters, but the clock decides who finishes — practice committing and moving on. Full-length timed sessions in the ExamsTorrent test engine are the most direct way to rehearse that discipline before the real DP-203 Deutsch exam.
Microsoft spreads the Microsoft Data Engineering on Microsoft Azure (DP-203 Deutsch Version) syllabus over 4 areas, with Design and Develop Data Processing (25-30%), Monitor and Optimize Data Storage and Processing (10-15%), Design and Implement Data Storage (40-45%) carrying the most weight. Study proportionally to those shares — the complete outline is in the section above this page.
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The DP-203 Deutsch exam is Microsoft's official certification test — pass it and the Microsoft Certified: Azure Data Engineer Associate certificate is yours, a credential at the Associate level. It exists to measure practical IT skills objectively, which is why hiring managers treat it as a reliable benchmark — and why candidate numbers keep rising. It also relates to Microsoft Certified: Azure Data Engineer Associate, so the value compounds as you progress.
One Microsoft Data Engineering on Microsoft Azure (DP-203 Deutsch Version) attempt costs $165 USD, with 700/1000 required to pass. Retakes charge the full fee again, so passing on your own terms means walking in overprepared. Use ExamsTorrent practice questions until timed mock scores clear 700/1000 comfortably, then book the DP-203 Deutsch exam.
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Microsoft Data Engineering on Microsoft Azure (DP-203 Deutsch Version) Sample Questions:
Sie implementieren ein Sternschema in einem dedizierten SQL-Pool von Azure Synapse Analytics.
Sie planen, eine Tabelle mit dem Namen DimProduct zu erstellen.
DimProduct muss eine SCO-Tabelle (Slow Changing Dimensions) vom Typ 3 sein, die die folgenden Anforderungen erfüllt:
* Die Werte in zwei Spalten namens ProductKey und ProductSourceID bleiben gleich.
* Die Werte in den drei Spalten „ProductName“, „ProductDescription“ und „Color“ können sich ändern.
Sie müssen zusätzliche Spalten hinzufügen, um die folgende Tabellendefinition zu vervollständigen.
- A.
- B.
- C.
- D.
- E.
- F.
Correct Answer: B,C,D 🗳️
Sie erstellen eine Datenbank in einem serverlosen SQL-Pool von Azure Synapse Analytics.
Sie haben Daten in Parquet-Dateien in einem Azure Data Lake Storage Gen2-Container gespeichert.
Datensätze sind wie im folgenden Beispiel dargestellt strukturiert.
{
"id": 123,
"address_housenumber": "19c",
„address_line“: „Speicherspur“,
„applicant1_name“: „Jane“,
„applicant2_name“: „Entwickler“
}
In den Unterlagen sind höchstens zwei Bewerber enthalten.
Sie müssen eine Tabelle erstellen, die nur die Adressfelder enthält.
Wie sollten Sie die Transact-SQL-Anweisung vervollständigen? Um zu antworten, wählen Sie im Antwortbereich die entsprechenden Optionen aus.
HINWEIS: Jede richtige Auswahl ist einen Punkt wert.
Correct Answer:
Explanation:
Box 1: CREATE EXTERNAL TABLE
An external table points to data located in Hadoop, Azure Storage blob, or Azure Data Lake Storage. External tables are used to read data from files or write data to files in Azure Storage. With Synapse SQL, you can use external tables to read external data using dedicated SQL pool or serverless SQL pool.
Syntax:
CREATE EXTERNAL TABLE { database_name.schema_name.table_name | schema_name.table_name | table_name } ( <column_definition> [ ,...n ] ) WITH ( LOCATION = 'folder_or_filepath', DATA_SOURCE = external_data_source_name, FILE_FORMAT = external_file_format_name Box 2. OPENROWSET When using serverless SQL pool, CETAS is used to create an external table and export query results to Azure Storage Blob or Azure Data Lake Storage Gen2.
Example:
AS
SELECT decennialTime, stateName, SUM(population) AS population
FROM
OPENROWSET(BULK 'https://azureopendatastorage.blob.core.windows.net/censusdatacontainer/release
/us_population_county/year=*/*.parquet',
FORMAT='PARQUET') AS [r]
GROUP BY decennialTime, stateName
GO
Reference:
https://docs.microsoft.com/en-us/azure/synapse-analytics/sql/develop-tables-external-tables
Sie verfügen über einen serverlosen Azure Synapse Analytics-SQL-Pool, einen dedizierten Azure Synapse Analytics-SQL-Pool, einen Apache Spark-Pool und ein Azure Data Lake Storage Gen2-Konto.
Sie müssen eine Tabelle in einer Lake-Datenbank erstellen. Die Tabelle muss sowohl für den serverlosen SQL-Pool als auch für den Spark-Pool verfügbar sein.
Wo sollten Sie die Tabelle erstellen und welches Dateiformat sollten Sie für die Daten in der Tabelle verwenden? Um zu antworten, wählen Sie die entsprechenden Optionen im Antwortbereich aus.
HINWEIS: Jede richtige Auswahl ist einen Punkt wert.
Correct Answer:
Explanation:
The dedicated SQL pool
Apache Parquet
Sie verwenden Azure Data Lake Storage Gen2 zum Speichern von Daten, die Datenwissenschaftler und Dateningenieure mithilfe interaktiver Azure Databricks-Notebooks abfragen. Benutzer haben nur Zugriff auf die Data Lake Storage-Ordner, die sich auf die Projekte beziehen, an denen sie arbeiten.
Sie müssen empfehlen, welche Authentifizierungsmethoden für Databricks und Data Lake Storage verwendet werden sollen, um den Benutzern den entsprechenden Zugriff zu gewähren. Die Lösung muss den Verwaltungsaufwand und den Entwicklungsaufwand minimieren.
Welche Authentifizierungsmethode sollten Sie für jeden Azure-Dienst empfehlen? Um zu antworten, wählen Sie im Antwortbereich die entsprechenden Optionen aus.
HINWEIS: Jede richtige Auswahl ist einen Punkt wert.
Correct Answer:
Explanation:
Box 1: Personal access tokens
You can use storage shared access signatures (SAS) to access an Azure Data Lake Storage Gen2 storage account directly. With SAS, you can restrict access to a storage account using temporary tokens with fine- grained access control.
You can add multiple storage accounts and configure respective SAS token providers in the same Spark session.
Box 2: Azure Active Directory credential passthrough
You can authenticate automatically to Azure Data Lake Storage Gen1 (ADLS Gen1) and Azure Data Lake Storage Gen2 (ADLS Gen2) from Azure Databricks clusters using the same Azure Active Directory (Azure AD) identity that you use to log into Azure Databricks. When you enable your cluster for Azure Data Lake Storage credential passthrough, commands that you run on that cluster can read and write data in Azure Data Lake Storage without requiring you to configure service principal credentials for access to storage.
After configuring Azure Data Lake Storage credential passthrough and creating storage containers, you can access data directly in Azure Data Lake Storage Gen1 using an adl:// path and Azure Data Lake Storage Gen2 using an abfss:// path:
Reference:
https://docs.microsoft.com/en-us/azure/databricks/data/data-sources/azure/adls-gen2/azure-datalake-gen2-sas- access
https://docs.microsoft.com/en-us/azure/databricks/security/credential-passthrough/adls-passthrough
Sie verwenden Azure Stream Analytics, um Daten von Azure Event Hubs zu empfangen und in einem Azure Blob Storage-Konto auszugeben. Sie müssen die Anzahl der in den letzten fünf Minuten empfangenen Datensätze minütlich ausgeben. Welche Fensterfunktion sollten Sie verwenden?
- A. Gleiten
- B. Hüpfen
- C. Taumeln
- D. Sitzung
Correct Answer: B 🗳️
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