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Databricks Databricks-Certified-Data-Engineer-Associate Exam Overview:

Certification Vendor:	Databricks
Exam Name:	Databricks Certified Data Engineer Associate Exam
Exam Number:	Databricks-Certified-Data-Engineer-Associate
Exam Duration:	90 minutes
Related Certifications:	Databricks Certified Data Engineer Professional Databricks Certified Data Analyst Associate
Exam Price:	200 USD
Certificate Validity Period:	2 years
Exam Format:	Multiple choice, Proctored
Available Languages:	Portuguese (Brazil), Korean, Japanese, English
Passing Score:	Not publicly disclosed, approximately 70%
Real Exam Qty:	45
Recommended Training:	Databricks Academy - Data Engineer Associate Learning Path Databricks Documentation
Exam Registration:	Official Registration
Sample Questions:	Databricks Databricks-Certified-Data-Engineer-Associate Sample Questions
Exam Way:	Online proctored or in-person at authorized test centers
Pre Condition:	No mandatory prerequisites; 6+ months hands-on experience with Databricks and data engineering tasks recommended
Official Syllabus URL:	https://www.databricks.com/learn/certification/data-engineer-associate

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Databricks-Certified-Data-Engineer-Associate Prüfung & Databricks-Certified-Data-Engineer-Associate Schulungsangebot

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Das Bestehen der Associate Exam von Databricks Certified Data Engineer zeigt, dass der Kandidat über die Fähigkeiten und das Kenntnis verfügt, um Datenpipelines und Data Warehouses mithilfe von Datenbäumen zu entwerfen, zu erstellen und zu warten. Es ist eine wichtige Zertifizierung für Dateningenieure, die ihre Karriere vorantreiben und ihre Fachkenntnisse bei der Verwendung von Datenbanks zur Lösung komplexer Daten technischer Probleme demonstrieren möchten. Angesichts des explosiven Datenwachstums in der heutigen Geschäftsumgebung ist die Nachfrage nach qualifizierten Dateningenieuren höher als je zuvor, und die Datenbank -zertifizierte Dateningenieure Associate Exam ist eine hervorragende Möglichkeit, sich in einem überfüllten Arbeitsmarkt abzuheben.

Die GAQM Databricks-Certified-Data-Engineer-Associate Prüfung ist eine Zertifizierungsprüfung für Dateningenieure, die ihre Kompetenz im Designen und Erstellen von Datenpipelines mit Databricks beweisen möchten. Diese Prüfung ist dafür konzipiert, das Wissen und die Fähigkeiten der Kandidaten in den Bereichen Datenengineering, Big Data Verarbeitung und Analytik sowie Cloud Computing mit Databricks zu testen.

Die Zertifizierungsprüfung für Datenbänke von Datenbank-Zertifizierungsdaten-Einmischung (Databricks Certified Data Engineer Associate) ist eine strenge Prüfung, bei der Kandidaten zu ihren praktischen Fähigkeiten und Kenntnissen des Datentechnik getestet werden. Die Kandidaten müssen eine Reihe von Fragen der Multiple-Choice-Fragen und praktischen Aufgaben erledigen, die die realen Daten-Engineering-Szenarien simulieren. Die Prüfung soll sicherstellen, dass die Kandidaten über die erforderlichen Fähigkeiten und Kenntnisse verfügen, um groß angelegte Datenpipelines aufzubauen und aufrechtzuerhalten.

Databricks Certified Data Engineer Associate Exam Databricks-Certified-Data-Engineer-Associate Prüfungsfragen mit Lösungen (Q17-Q22):

17. Frage
A data engineer needs to provide access to a group named manufacturing-team. The team needs privileges to create tables in the quality schema.
Which set of SQL commands will grant a group named manufacturing-team to create tables in a schema named production with the parent catalog named manufacturing with the least privileges?

A. GRANT CREATE TABLE ON SCHEMA manufacturing.quality TO manufacturing-team; GRANT CREATE SCHEMA ON SCHEMA manufacturing.quality TO manufacturing-team; GRANT USE CATALOG ON CATALOG manufacturing TO manufacturing-team;
B. GRANT USE TABLE ON SCHEMA manufacturing.quality TO manufacturing-team; GRANT USE SCHEMA ON SCHEMA manufacturing.quality TO manufacturing-team; GRANT USE CATALOG ON CATALOG manufacturing TO manufacturing-team;
C. GRANT CREATE TABLE ON SCHEMA manufacturing.quality TO manufacturing-team; GRANT USE SCHEMA ON SCHEMA manufacturing.quality TO manufacturing-team; GRANT USE CATALOG ON CATALOG manufacturing TO manufacturing-team;
D. GRANT CREATE TABLE ON SCHEMA manufacturing.quality TO manufacturing-team; GRANT CREATE SCHEMA ON SCHEMA manufacturing.quality TO manufacturing-team; GRANT CREATE CATALOG ON CATALOG manufacturing TO manufacturing-team;

Antwort: C

Begründung:
To create a table within a schema, a principal must have CREATE TABLE on the schema, USE SCHEMA on that schema, and USE CATALOG on the parent catalog. This combination ensures the group has just enough privileges to create objects in that schema without excessive permissions like CREATE SCHEMA or CREATE CATALOG.
Reference Source: Databricks Unity Catalog Privilege Model - "Privileges Required to Create a Table."

18. Frage
A data engineer is designing a medallion architecture. Raw Kafka events must be stored exactly as received for reprocessing, then cleaned and deduplicated, then aggregated for BI dashboards.
Which mapping of layers is correct?

A. Bronze = raw ingest, Silver = cleaned and conformed records, Gold = business-level aggregates
B. Bronze = external tables only, Silver = views only, Gold = temporary views only
C. Bronze = cleaned records, Silver = business aggregates, Gold = raw ingest
D. Bronze = aggregated metrics, Silver = raw ingest, Gold = cleaned records

Antwort: A

Begründung:
In the medallion architecture, bronze holds raw, append-only ingested data to enable full reprocessing; silver holds validated, deduplicated, and joined data; gold holds curated, aggregated datasets optimized for analytics and reporting.

19. Frage
A data engineer runs df.toPandas() on a wide DataFrame containing 50 million rows. The notebook cell fails with a java.lang.OutOfMemoryError on the driver. Which memory configuration is directly responsible for this failure?

A. spark.memory.fraction is too low, limiting the storage memory available for caching
B. spark.executor.memory is too low to process the DataFrame transformations
C. spark.driver.memory is too low to hold the collected result set
D. spark.sql.shuffle.partitions is set too high, causing excessive memory consumption during shuffles

Antwort: C

Begründung:
df.toPandas() collects the entire DataFrame into the driver process. A 50-million-row wide dataset exceeds available spark.driver.memory, causing the driver-side OutOfMemoryError.

20. Frage
A data engineer is onboarding a new Bronze ingestion pipeline in Databricks with Unity Catalog. The team wants Databricks to handle storage layout, apply platform optimizations over time, and simplify lifecycle management so that when a table is dropped, its underlying data is also cleaned up according to Databricks-managed retention policies.
Which table type should the data engineer create for these ingestion tables?

A. Foreign tables federated from an external catalog to delegate optimization to the source system
B. Managed tables so that Unity Catalog manages both metadata and underlying data lifecycle
C. External tables with a LOCATION pointing to an external volume for full control of file layout
D. Temporary views over files to avoid table-level governance and lifecycle coupling

Antwort: B

Begründung:
Databricks recommends managed tables when you want the platform to control both the table metadata and the underlying storage lifecycle. For Unity Catalog managed tables, Databricks manages storage layout and applies platform-managed optimization features over time. Databricks also documents that when you drop a managed table, the underlying cloud data is deleted automatically after the managed retention period, which simplifies cleanup and reduces storage administration. That behavior matches the requirement exactly. External tables, by contrast, are used when you want to retain direct control over the storage path and lifecycle. Databricks explicitly states that dropping an external table removes only the metadata and does not delete the underlying files. Foreign tables are for federation scenarios against external systems, not for Bronze ingestion storage managed by Databricks. Temporary views also do not provide the desired governed lifecycle. Because the team wants Databricks-managed storage, managed retention, and simplified lifecycle handling, managed tables are the correct choice for these Bronze ingestion tables.
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21. Frage
In which of the following scenarios should a data engineer select a Task in the Depends On field of a new Databricks Job Task?

A. When another task has the same dependency libraries as the new task
B. When another task needs to successfully complete before the new task begins
C. When another task needs to be replaced by the new task
D. When another task needs to fail before the new task begins
E. When another task needs to use as little compute resources as possible

Antwort: B

22. Frage
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