Databricks-Certified-Professional-Data-Engineer Prüfungsfragen, Databricks-Certified-Professional-Data-Engineer Fragen und Antworten, Databricks Certified Professional Data Engineer Exam

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

Certification Vendor:	Databricks
Exam Name:	Databricks Certified Professional Data Engineer Exam
Exam Number:	Databricks-Certified-Professional-Data-Engineer
Exam Format:	Multiple select, Scenario-based questions, Multiple choice
Related Certifications:	Databricks Certified Data Engineer Associate
Real Exam Qty:	60-70
Exam Duration:	120 minutes
Passing Score:	70%
Available Languages:	English
Exam Price:	USD 200
Certificate Validity Period:	2 years
Recommended Training:	Databricks Academy
Exam Registration:	Databricks Certification Portal
Sample Questions:	Databricks Databricks-Certified-Professional-Data-Engineer Sample Questions
Exam Way:	Online proctored exam
Pre Condition:	Recommended: Databricks Certified Data Engineer Associate or equivalent experience with Spark and Databricks platform.
Official Syllabus URL:	https://www.databricks.com/learn/certification

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

Databricks-Certified-Professional-Data-Engineer Prüfungsinformationen & Databricks-Certified-Professional-Data-Engineer Fragenkatalog

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Die Prüfung ist ideal für Daten-Ingenieure, die ihre Expertise im Entwurf, Aufbau und der Wartung von Big-Data-Verarbeitungspipelines mit Databricks demonstrieren möchten. Die Zertifizierungsprüfung ist auf die spezifischen Rollen und Verantwortlichkeiten eines Daten-Ingenieurs zugeschnitten und umfasst eine Reihe von Themen, einschließlich Daten-Erfassung, Daten-Transformationen, Daten-Speicherung und Daten-Analyse. Durch das Bestehen der Prüfung können Kandidaten ihre Kompetenz im Umgang mit Databricks bei der Lösung komplexer Big-Data-Herausforderungen demonstrieren.

Die Zertifizierungsprüfung für Datenbank-zertifizierte professionelle DATA-Einmischungsprüfung ist ein umfassender Test, der alle Aspekte des Datenentwicklung mit Datenbanken abdeckt. Die Prüfung wurde entwickelt, um das Wissen des Kandidaten über Datenbankarchitektur, Datenentwicklungskonzepte, Datenverarbeitung mit Datenbanken und Datenspeicher mit Datenbanken zu testen. Die Prüfung testet auch die Fähigkeit des Kandidaten, Datenentwicklungslösungen mithilfe von Datenbäumen zu entwerfen, zu implementieren und zu verwalten.

Databricks Certified Professional Data Engineer Exam Databricks-Certified-Professional-Data-Engineer Prüfungsfragen mit Lösungen (Q69-Q74):

69. Frage
A data engineer, User A, has promoted a new pipeline to production by using the REST API to programmatically create several jobs. A DevOps engineer, User B, has configured an external orchestration tool to trigger job runs through the REST API. Both users authorized the REST API calls using their personal access tokens.
Which statement describes the contents of the workspace audit logs concerning these events?

A. Because these events are managed separately, User A will have their identity associated with the job creation events and User B will have their identity associated with the job run events.
B. Because User B last configured the jobs, their identity will be associated with both the job creation events and the job run events.
C. Because the REST API was used for job creation and triggering runs, a Service Principal will be automatically used to identity these events.
D. Because the REST API was used for job creation and triggering runs, user identity will not be captured in the audit logs.
E. Because User A created the jobs, their identity will be associated with both the job creation events and the job run events.

Antwort: A

Begründung:
Explanation
The events are that a data engineer, User A, has promoted a new pipeline to production by using the REST API to programmatically create several jobs, and a DevOps engineer, User B, has configured an external orchestration tool to trigger job runs through the REST API. Both users authorized the REST API calls using their personal access tokens. The workspace audit logs are logs that record user activities in a Databricks workspace, such as creating, updating, or deleting objects like clusters, jobs, notebooks, or tables. The workspace audit logs also capture the identity of the user who performed each activity, as well as the time and details of the activity. Because these events are managed separately, User A will have their identity associated with the job creation events and User B will have their identity associated with the job run events in the workspace audit logs. Verified References: [Databricks Certified Data Engineer Professional], under
"Databricks Workspace" section; Databricks Documentation, under "Workspace audit logs" section.

70. Frage
A production workload incrementally applies updates from an external Change Data Capture feed to a Delta Lake table as an always-on Structured Stream job. When data was initially migrated for this table, OPTIMIZE was executed and most data files were resized to 1 GB. Auto Optimize and Auto Compaction were both turned on for the streaming production job. Recent review of data files shows that most data files are under 64 MB, although each partition in the table contains at least 1 GB of data and the total table size is over 10 TB.
Which of the following likely explains these smaller file sizes?

A. Databricks has autotuned to a smaller target file size based on the amount of data in each partition
B. Databricks has autotuned to a smaller target file size to reduce duration of MERGE operations
C. Databricks has autotuned to a smaller target file size based on the overall size of data in the table
D. Z-order indices calculated on the table are preventing file compaction C Bloom filler indices calculated on the table are preventing file compaction

Antwort: B

Begründung:
This is the correct answer because Databricks has a feature called Auto Optimize, which automatically optimizes the layout of Delta Lake tables by coalescing small files into larger ones and sorting data within each file by a specified column. However, Auto Optimize also considers the trade-off between file size and merge performance, and may choose a smaller target file size to reduce the duration of merge operations, especially for streaming workloads that frequently update existing records. Therefore, it is possible that Auto Optimize has autotuned to a smaller target file size based on the characteristics of the streaming production job. Verified References: [Databricks Certified Data Engineer Professional], under "Delta Lake" section; Databricks Documentation, under "Auto Optimize" section. https://docs.databricks.com/en/delta/tune-file-size.
html#autotune-table 'Autotune file size based on workload'

71. Frage
A data engineer inherits a Delta table with historical partitions by country that are badly skewed. Queries often filter by high-cardinality customer_id and vary across dimensions over time. The engineer wants a strategy that avoids a disruptive full rewrite, reduces sensitivity to skewed partitions, and sustains strong query performance as access patterns evolve.
Which two actions should the data engineer take? (Choose 2)

A. Keep existing partitions and rely on bin-packing OPTIMIZE only; ZORDER and clustering are unnecessary for multi-dimensional filters.
B. Switch from static partitioning to liquid clustering and select initial clustering keys that reflect common filters such as customer_id.
C. Periodically run OPTIMIZE table_name.
D. Disable data skipping statistics to avoid maintenance overhead; rely on adaptive query execution instead.
E. Depend solely on optimized writes; Databricks will automatically replace partitioning with clustering over time.

Antwort: B,C

Begründung:
Liquid Clustering replaces traditional partitioning and ZORDER optimization by automatically organizing data according to clustering keys. It supports evolving clustering strategies without requiring a full table rewrite. To maintain cluster balance and improve performance, the OPTIMIZE command should be run periodically. OPTIMIZE groups data files by clustering keys and helps reduce small file overhead.
Reference Source: Databricks Delta Lake Guide - "Use Liquid Clustering for Tables" and "OPTIMIZE Command for File Compaction and Data Layout."
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72. Frage
Which of the following data workloads will utilize a silver table as its source?

A. A job that enriches data by parsing its timestamps into a human-readable format
B. A job that ingests raw data from a streaming source into the Lakehouse
C. A job that queries aggregated data that already feeds into a dashboard
D. A job that cleans data by removing malformatted records
E. A job that aggregates cleaned data to create standard summary statistics

Antwort: E

Begründung:
Explanation
The answer is, A job that aggregates cleaned data to create standard summary statistics Silver zone maintains the grain of the original data, in this scenario a job is taking data from the silver zone as the source and aggregating and storing them in the gold zone.
Medallion Architecture - Databricks
Silver Layer:
1. Reduces data storage complexity, latency, and redundency
2. Optimizes ETL throughput and analytic query performance
3. Preserves grain of original data (without aggregation)
4. Eliminates duplicate records
5. production schema enforced
6. Data quality checks, quarantine corrupt data
Exam focus: Please review the below image and understand the role of each layer(bronze, silver, gold) in medallion architecture, you will see varying questions targeting each layer and its purpose.
Sorry I had to add the watermark some people in Udemy are copying my content.
Purpose of each layer in medallion architecture

73. Frage
A Structured Streaming job deployed to production has been resulting in higher than expected cloud storage costs. At present, during normal execution, each micro-batch of data is processed in less than 3 seconds; at least 12 times per minute, a micro-batch is processed that contains 0 records. The streaming write was configured using the default trigger settings. The production job is currently scheduled alongside many other Databricks jobs in a workspace with instance pools provisioned to reduce start-up time for jobs with batch execution. Holding all other variables constant and assuming records need to be processed in less than 10 minutes, which adjustment will meet the requirement?

A. Use the trigger once option and configure a Databricks job to execute the query every 10 minutes; this approach minimizes costs for both compute and storage.
B. Set the trigger interval to 3 seconds; the default trigger interval is consuming too many records per batch, resulting in spill to disk that can increase volume costs.
C. Set the trigger interval to 10 minutes; each batch calls APIs in the source storage account, so decreasing trigger frequency to the maximum allowable threshold should minimize this cost.
D. Set the trigger interval to 500 milliseconds; setting a small but non-zero trigger interval ensures that the source is not queried too frequently.

Antwort: A

Begründung:
* Exact extract: "If no trigger is specified, the default processing-time trigger runs micro-batches as fast as possible."
* Exact extract: "Trigger once processes all available data once and then stops." References: Structured Streaming triggers; Databricks Jobs and job clusters.

74. Frage
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