Databricks-Certified-Professional-Data-Engineer Zertifizierungsfragen, Databricks-Certified-Professional-Data-Engineer Zertifikatsfragen

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Die Zertifizierung von Datenbäumen Certified Professional Data Engineer ist ein wertvoller Berechtigungsnachweis für Dateningenieure, die ihre Fähigkeiten und Fachkenntnisse bei der Verwendung von Datenbäumen validieren möchten. Die Zertifizierung zeigt den Arbeitgebern und Kunden, dass der Kandidat über das Wissen und die Fähigkeiten verfügt, die zur Gestaltung und Implementierung von Datenlösungen mithilfe von Datenbanken erforderlich sind. Es hilft auch Dateningenieuren, sich in einem wettbewerbsfähigen Arbeitsmarkt zu unterscheiden, und bietet Möglichkeiten für den beruflichen Aufstieg.

Die Zertifizierungsprüfung zum Databricks Certified Professional Data Engineer besteht aus 60 Multiple-Choice-Fragen, die innerhalb von 90 Minuten beantwortet werden müssen. Die Fragen sind darauf ausgelegt, das Wissen des Kandidaten über Databricks und dessen Funktionen sowie seine Fähigkeit zu testen, dieses Wissen auf reale Szenarien anzuwenden. Um die Prüfung zu bestehen, müssen die Kandidaten eine Mindestpunktzahl von 70% erreichen. Die Zertifizierung ist zwei Jahre lang gültig und kann durch eine Wiederholung der Prüfung oder durch Absolvieren einer Reihe von Fortbildungskursen erneuert werden. Der Erhalt der Zertifizierung als Databricks Certified Professional Data Engineer kann Datenfachleuten dabei helfen, ihre Expertise im Aufbau und der Implementierung von Datenengineering-Lösungen mit Databricks zu demonstrieren, was in der heutigen datengesteuerten Geschäftswelt von großem Wert sein kann.

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Databricks-Certified-Professional-Data-Engineer Zertifikatsfragen - Databricks-Certified-Professional-Data-Engineer Deutsche

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Um sich auf die DCPDE -Prüfung vorzubereiten, sollten die Kandidaten ein solides Verständnis der Konzepte für Daten technische Konzepte wie Datenmodellierung, Datenintegration, Datenumwandlung und Datenqualität haben. Sie sollten auch Erfahrung mit Big -Data -Technologien wie Apache Spark, Apache Kafka und Apache Hadoop haben.

Databricks Certified Professional Data Engineer Exam Databricks-Certified-Professional-Data-Engineer Prüfungsfragen mit Lösungen (Q73-Q78):

73. Frage
Data engineering team has provided 10 queries and asked Data Analyst team to build a dashboard and refresh the data every day at 8 AM, identify the best approach to set up data refresh for this dashaboard?

A. Setup JOB with linear dependency to all load all 10 queries into a table so the dashboard can be refreshed at once.
B. A dashboard can only refresh one query at a time, 10 schedules to set up the refresh.
C. The entire dashboard with 10 queries can be refreshed at once, single schedule needs to be set up to refresh at 8 AM.
D. Use Incremental refresh to run at 8 AM every day.
E. Each query requires a separate task and setup 10 tasks under a single job to run at 8 AM to refresh the dashboard

Antwort: C

Begründung:
Explanation
The answer is,
The entire dashboard with 10 queries can be refreshed at once, single schedule needs to be set up to refresh at
8 AM.
Automatically refresh a dashboard
A dashboard's owner and users with the Can Edit permission can configure a dashboard to auto-matically refresh on a schedule. To automatically refresh a dashboard:
* Click the Schedule button at the top right of the dashboard. The scheduling dialog appears.
* Graphical user interface, text, application, email, Teams Description automatically generated
* 2.In the Refresh every drop-down, select a period.
* 3.In the SQL Warehouse drop-down, optionally select a SQL warehouse to use for all the queries.
If you don't select a warehouse, the queries execute on the last used SQL ware-house.
* 4.Next to Subscribers, optionally enter a list of email addresses to notify when the dashboard is automatically updated.
* Each email address you enter must be associated with a Azure Databricks account or con-figured as an alert destination.
* 5.Click Save. The Schedule button label changes to Scheduled.

74. Frage
A junior member of the data engineering team is exploring the language interoperability of Databricks notebooks. The intended outcome of the below code is to register a view of all sales that occurred in countries on the continent of Africa that appear in the geo_lookup table.
Before executing the code, running SHOW TABLES on the current database indicates the database contains only two tables: geo_lookup and sales.

Which statement correctly describes the outcome of executing these command cells in order in an interactive notebook?

A. Cmd 1 will succeed and Cmd 2 will fail, countries at will be a Python variable representing a PySpark DataFrame.
B. Both commands will fail. No new variables, tables, or views will be created.
C. Both commands will succeed. Executing show tables will show that countries at and sales at have been registered as views.
D. Cmd 1 will succeed. Cmd 2 will search all accessible databases for a table or view named countries af: if this entity exists, Cmd 2 will succeed.
E. Cmd 1 will succeed and Cmd 2 will fail, countries at will be a Python variable containing a list of strings.

Antwort: E

Begründung:
This is the correct answer because Cmd 1 is written in Python and uses a list comprehension to extract the country names from the geo_lookup table and store them in a Python variable named countries af. This variable will contain a list of strings, not a PySpark DataFrame or a SQL view. Cmd 2 is written in SQL and tries to create a view named sales af by selecting from the sales table where city is in countries af. However, this command will fail because countries af is not a valid SQL entity and cannot be used in a SQL query. To fix this, a better approach would be to use spark.sql() to execute a SQL query in Python and pass the countries af variable as a parameter. Verified References: [Databricks Certified Data Engineer Professional], under
"Language Interoperability" section; Databricks Documentation, under "Mix languages" section.

75. Frage
You are working on IOT data where each device has 5 reading in an array collected in Celsius, you were asked to covert each individual reading from Celsius to Fahrenheit, fill in the blank with an appropriate function that can be used in this scenario.
Schema: deviceId INT, deviceTemp ARRAY<double>

SELECT deviceId, __(deviceTempC,i-> (i * 9/5) + 32) as deviceTempF
FROM sensors

A. TRANSFORM
B. ARRAYEXPR
C. APPLY
D. FORALL
E. MULTIPLY

Antwort: A

Begründung:
Explanation
TRANSFORM -> Transforms elements in an array in expr using the function func.
1.transform(expr, func)

76. Frage
Which statement regarding spark configuration on the Databricks platform is true?

A. Spark configuration set within an notebook will affect all SparkSession attached to the same interactive cluster
B. The Databricks REST API can be used to modify the Spark configuration properties for an interactive cluster without interrupting jobs.
C. Spark configuration properties set for an interactive cluster with the Clusters UI will impact all notebooks attached to that cluster.
D. When the same spar configuration property is set for an interactive to the same interactive cluster.

Antwort: C

Begründung:
When Spark configuration properties are set for an interactive cluster using the Clusters UI in Databricks, those configurations are applied at the cluster level. This means that all notebooks attached to that cluster will inherit and be affected by these configurations. This approach ensures consistency across all executions within that cluster, as the Spark configuration properties dictate aspects such as memory allocation, number of executors, and other vital execution parameters. This centralized configuration management helps maintain standardized execution environments across different notebooks, aiding in debugging and performance optimization.
References:
* Databricks documentation on configuring clusters: https://docs.databricks.com/clusters/configure.html

77. Frage
A data engineer wants to reflector the following DLT code, which includes multiple definition with very similar code:

In an attempt to programmatically create these tables using a parameterized table definition, the data engineer writes the following code.

The pipeline runs an update with this refactored code, but generates a different DAG showing incorrect configuration values for tables.
How can the data engineer fix this?

A. Convert the list of configuration values to a dictionary of table settings, using different input the for loop.
B. Convert the list of configuration values to a dictionary of table settings, using table names as keys.
C. Load the configuration values for these tables from a separate file, located at a path provided by a pipeline parameter.
D. Wrap the loop inside another table definition, using generalized names and properties to replace with those from the inner table

Antwort: B

Begründung:
The issue with the refactored code is that it tries to use string interpolation to dynamically create table names within thedlc.tabledecorator, which will not correctly interpret the table names. Instead, by using a dictionary with table names as keys and their configurations as values, the data engineer can iterate over the dictionary items and use the keys (table names) to properly configure the table settings. This way, the decorator can correctly recognize each table name, and the corresponding configuration settings can be applied appropriately.

78. Frage
......

Databricks-Certified-Professional-Data-Engineer Zertifikatsfragen: https://de.fast2test.com/Databricks-Certified-Professional-Data-Engineer-premium-file.html