Blog

Jack Robinson Jack Robinson

0 Course Enrolled • 0 Course Completed

Biography

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

BONUS!!! Laden Sie die vollständige Version der ZertPruefung Databricks-Certified-Professional-Data-Engineer Prüfungsfragen kostenlos herunter: https://drive.google.com/open?id=14B_XqLCzhfZ-Ai8HSkOSv-m2nOHIatgB

Hohe Effizienz ist genau das, was unsere Gesellschaft von uns fordern. Die in der IT-Branche arbeitende Leute haben bestimmt das erfahren. Möchten Sie so schnell wie möglich die Zertifikat der Databricks Databricks-Certified-Professional-Data-Engineer erwerben? Insofern Sie uns finden, finden Sie doch die Methode, mit der Sie effektiv die Databricks Databricks-Certified-Professional-Data-Engineer Prüfung bestehen können. Die Technik-Gruppe von uns ZertPruefung haben seit einigen Jahren große Menge von Prüfungsunterlagen der Databricks Databricks-Certified-Professional-Data-Engineer Prüfung systematisch gesammelt und analysiert. Außerdem haben Sie insgesamt 3 Versionen hergestellt. Damit können Sie sich irgendwo und irgendwie auf Databricks Databricks-Certified-Professional-Data-Engineer mit hoher Effizienz vorbereiten.

Die Zertifizierungsprüfung für Datenbanken zertifizierte professionelle Dateningenieur soll das Wissen und die Fähigkeiten von Dateningenieuren testen, die mit Datenbanken arbeiten. Databricks ist eine Cloud-basierte Plattform, die eine einheitliche Analyse-Engine für Big-Data-Verarbeitung und maschinelles Lernen bietet. Es wird von Dateningenieuren verwendet, um Datenpipelines zu verwalten, Erkenntnisse aus Daten zu extrahieren und Modelle für maschinelles Lernen zu erstellen. Die Zertifizierungsprüfung ist eine umfassende Bewertung der Fähigkeit des Kandidaten, Datenbanken effektiv für Daten technische Aufgaben zu verwenden.

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 Buch <<

Databricks-Certified-Professional-Data-Engineer Online Tests & Databricks-Certified-Professional-Data-Engineer Online Prüfung

ZertPruefung ist ein Vorläufer in der IT-Branche bei der Bereitstellung von Databricks Databricks-Certified-Professional-Data-Engineer IT-Zertifizierungsmaterialien, die Produkte von guter Qualität bieten. Die Prüfungsfragen und Antworten zur Databricks Databricks-Certified-Professional-Data-Engineer Zertifizierungsprüfung von ZertPruefung führen Sie zum Erfolg. Sie werden exzellente Leistungen erzielen und Ihren Traum verwirklichen.

Databricks Certified Professional Data Engineer Exam Databricks-Certified-Professional-Data-Engineer Prüfungsfragen mit Lösungen (Q96-Q101):

96. Frage
A new data engineer notices that a critical field was omitted from an application that writes its Kafka source to Delta Lake. This happened even though the critical field was in the Kafka source. That field was further missing from data written to dependent, long-term storage. The retention threshold on the Kafka service is seven days. The pipeline has been in production for three months.
Which describes how Delta Lake can help to avoid data loss of this nature in the future?

A. Delta Lake automatically checks that all fields present in the source data are included in the ingestion layer.
B. Data can never be permanently dropped or deleted from Delta Lake, so data loss is not possible under any circumstance.
C. Ingestine all raw data and metadata from Kafka to a bronze Delta table creates a permanent, replayable history of the data state.
D. The Delta log and Structured Streaming checkpoints record the full history of the Kafka producer.
E. Delta Lake schema evolution can retroactively calculate the correct value for newly added fields, as long as the data was in the original source.

Antwort: C

Begründung:
Explanation
This is the correct answer because it describes how Delta Lake can help to avoid data loss of this nature in the future. By ingesting all raw data and metadata from Kafka to a bronze Delta table, Delta Lake creates a permanent, replayable history of the data state that can be used for recovery or reprocessing in case of errors or omissions in downstream applications or pipelines. Delta Lake also supports schema evolution, which allows adding new columns to existing tables without affecting existing queries or pipelines. Therefore, if a critical field was omitted from an application that writes its Kafka source to Delta Lake, it can be easily added later and the data can be reprocessed from the bronze table without losing any information. Verified References:
[Databricks Certified Data Engineer Professional], under "Delta Lake" section; Databricks Documentation, under "Delta Lake core features" section.

97. Frage
Which REST API call can be used to review the notebooks configured to run as tasks in a multi-task job?

A. /jobs/runs/list
B. /jobs/list
C. /jobs/runs/get-output
D. /jobs/runs/get
E. /jobs/get

Antwort: E

Begründung:
This is the correct answer because it is the REST API call that can be used to review the notebooks configured to run as tasks in a multi-task job. The REST API is an interface that allows programmatically interacting with Databricks resources, such as clusters, jobs, notebooks, or tables. The REST API uses HTTP methods, such as GET, POST, PUT, or DELETE, to perform operations on these resources. The /jobs/get endpoint is a GET method that returns information about a job given its job ID. The information includes the job settings, such as the name, schedule, timeout, retries, email notifications, and tasks. The tasks are the units of work that a job executes. A task can be a notebook task, which runs a notebook with specified parameters; a jar task, which runs a JAR uploaded to DBFS with specified main class and arguments; or a python task, which runs a Python file uploaded to DBFS with specified parameters. A multi-task job is a job that has more than one task configured to run in a specific order or in parallel. By using the /jobs/get endpoint, one can review the notebooks configured to run as tasks in a multi-task job. Verified References: [Databricks Certified Data Engineer Professional], under "Databricks Jobs" section; Databricks Documentation, under "Get" section; Databricks Documentation, under "JobSettings" section.

98. Frage
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 change data feed only tracks inserts and updates not deleted records.
C. No: files containing deleted records may still be accessible with time travel until a BACUM command is used to remove invalidated data files.
D. No: the Delta Lake DELETE command only provides ACID guarantees when combined with the MERGE INTO command

Antwort: C

Begründung:
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 certain retention period. The VACUUM command will remove the files from the storage layer, and after this, the records will no longer be accessible.

99. Frage
You are trying to calculate total sales made by all the employees by parsing a complex struct data type that stores employee and sales data, how would you approach this in SQL Table definition, batchId INT, performance ARRAY<STRUCT<employeeId: BIGINT, sales: INT>>, in-sertDate TIMESTAMP Sample data of performance column
1.[
2.{ "employeeId":1234
3."sales" : 10000},
4.
5.{ "employeeId":3232
6."sales" : 30000}
7.]
Calculate total sales made by all the employees?
Sample data with create table syntax for the data:
1.create or replace table sales as
2.select 1 as batchId ,
3.from_json('[{ "employeeId":1234,"sales" : 10000 },{ "employeeId":3232,"sales" : 30000 }]',
4. 'ARRAY<STRUCT<employeeId: BIGINT, sales: INT>>') as performance,
5. current_timestamp() as insertDate
6.union all
7.select 2 as batchId ,
8. from_json('[{ "employeeId":1235,"sales" : 10500 },{ "employeeId":3233,"sales" : 32000 }]',
9. 'ARRAY<STRUCT<employeeId: BIGINT, sales: INT>>') as performance,
10. current_timestamp() as insertDate

A. 1.select reduce(flatten(collect_list(performance:sales)), 0, (x, y) -> x + y)
2.as total_sales from sales
B. 1.WITH CTE as (SELECT FLATTEN (performance) FROM table_name)
2.SELECT SUM (sales) FROM CTE
C. 1.select aggregate(flatten(collect_list(performance.sales)), 0, (x, y) -> x + y)
2.as total_sales from sales
D. 1.WITH CTE as (SELECT EXPLODE (performance) FROM table_name)
2.SELECT SUM (performance.sales) FROM CTE
E. SELECT SUM(SLICE (performance, sales)) FROM employee

Antwort: C

Begründung:
Explanation
The answer is
1.select aggregate(flatten(collect_list(performance.sales)), 0, (x, y) -> x + y)
2.as total_sales from sales
Nested Struct can be queried using the . notation performance.sales will give you access to all the sales values in the performance column.
Note: option D is wrong because it uses performance:sales not performance.sales. ":" this is only used when referring to JSON data but here we are dealing with a struct data type. for the exam please make sure to understand if you are dealing with JSON data or Struct data.

Other solutions:
we can also use reduce instead of aggregate
select reduce(flatten(collect_list(performance.sales)), 0, (x, y) -> x + y) as total_sales from sales we can also use explode and sum instead of using any higher-order funtions.
1.with cte as (
2. select
3. explode(flatten(collect_list(performance.sales))) sales from sales
4.)
5.select
6. sum(sales) from cte
Sample data with create table syntax for the data:
1.create or replace table sales as
2.select 1 as batchId ,
3.from_json('[{ "employeeId":1234,"sales" : 10000 },{ "employeeId":3232,"sales" : 30000 }]',
4. 'ARRAY<STRUCT<employeeId: BIGINT, sales: INT>>') as performance,
5. current_timestamp() as insertDate
6.union all
7.select 2 as batchId ,
8. from_json('[{ "employeeId":1235,"sales" : 10500 },{ "employeeId":3233,"sales" : 32000 }]',
9. 'ARRAY<STRUCT<employeeId: BIGINT, sales: INT>>') as performance,
10. current_timestamp() as insertDate

100. Frage
A Delta table of weather records is partitioned by date and has the below schema:
date DATE, device_id INT, temp FLOAT, latitude FLOAT, longitude FLOAT
To find all the records from within the Arctic Circle, you execute a query with the below filter:
latitude > 66.3
Which statement describes how the Delta engine identifies which files to load?

A. The Parquet file footers are scanned for min and max statistics for the latitude column
B. The Delta log is scanned for min and max statistics for the latitude column
C. All records are cached to attached storage and then the filter is applied
D. All records are cached to an operational database and then the filter is applied
E. The Hive metastore is scanned for min and max statistics for the latitude column

Antwort: B

Begründung:
Explanation
This is the correct answer because Delta Lake uses a transaction log to store metadata about each table, including min and max statistics for each column in each data file. The Delta engine can use this information to quickly identify which files to load based on a filter condition, without scanning the entire table or the file footers. This is called data skipping and it can improve query performance significantly. Verified References:
[Databricks Certified Data Engineer Professional], under "Delta Lake" section; [Databricks Documentation], under "Optimizations - Data Skipping" section.

101. Frage
......

Die Databricks Databricks-Certified-Professional-Data-Engineer Dumps von ZertPruefung sind die Unterlagen, die von vielen Kadidaten geprüft sind. Und es kann die sehr hohe Durchlaufrate garantieren. Wenn Sie nach der Nutzung der Dumps bei der Databricks Databricks-Certified-Professional-Data-Engineer Zertifizierung durchgefallen sind, geben wir ZertPruefung Ihnen voll Geld zurück. Oder können Sie auch die kostlosen aktualisierten Dumps bekommen. Mit der Garantie sorgen Sie sich bitte nicht.

Databricks-Certified-Professional-Data-Engineer Online Tests: https://www.zertpruefung.ch/Databricks-Certified-Professional-Data-Engineer_exam.html

P.S. Kostenlose 2025 Databricks Databricks-Certified-Professional-Data-Engineer Prüfungsfragen sind auf Google Drive freigegeben von ZertPruefung verfügbar: https://drive.google.com/open?id=14B_XqLCzhfZ-Ai8HSkOSv-m2nOHIatgB

Cookie	Duration	Description
cookielawinfo-checkbox-analytics	11 months	This cookie is set by GDPR Cookie Consent plugin. The cookie is used to store the user consent for the cookies in the category "Analytics".
cookielawinfo-checkbox-functional	11 months	The cookie is set by GDPR cookie consent to record the user consent for the cookies in the category "Functional".
cookielawinfo-checkbox-necessary	11 months	This cookie is set by GDPR Cookie Consent plugin. The cookies is used to store the user consent for the cookies in the category "Necessary".
cookielawinfo-checkbox-others	11 months	This cookie is set by GDPR Cookie Consent plugin. The cookie is used to store the user consent for the cookies in the category "Other.
cookielawinfo-checkbox-performance	11 months	This cookie is set by GDPR Cookie Consent plugin. The cookie is used to store the user consent for the cookies in the category "Performance".
viewed_cookie_policy	11 months	The cookie is set by the GDPR Cookie Consent plugin and is used to store whether or not user has consented to the use of cookies. It does not store any personal data.

Jack Robinson Jack Robinson

Biography

COOKIE NOTICE