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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
Passing Score:	70%
Exam Price:	USD 200
Exam Format:	Scenario-based questions, Multiple choice, Multiple select
Related Certifications:	Databricks Certified Data Engineer Associate
Real Exam Qty:	60-70
Available Languages:	English
Exam Duration:	120 minutes
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 Fragenkatalog <<

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Die Databricks Certified Professional Data Engineer Prüfung ist eine herstellerneutrale Zertifizierung, was bedeutet, dass sie nicht spezifisch für eine bestimmte Technologie oder Produkt ist. Das macht sie zu einer ausgezeichneten Wahl für Daten-Ingenieure, die mit verschiedenen Big-Data-Technologien arbeiten und ihr Wissen über Databricks demonstrieren möchten. Die Zertifizierungsprüfung wird weltweit anerkannt und ist bei Organisationen, die Databricks für ihre Big-Data-Verarbeitungsbedürfnisse nutzen, sehr geschätzt.

Die Prüfung besteht aus Multiple-Choice-Fragen und praktischen Aufgaben, die das praktische Wissen des Kandidaten in Databricks testen. Die Prüfung umfasst eine Vielzahl von Themen wie Datenengineering, Datenverarbeitung, ETL, Datenmodellierung, Data Warehousing, Datenverwaltung und Datensicherheit. Die Prüfung ist darauf ausgelegt, die Fähigkeit des Kandidaten zu bewerten, skalierbare Datenpipelines unter Verwendung von Databricks zu entwerfen und zu implementieren.

Databricks Certified Professional Data Engineer Exam Databricks-Certified-Professional-Data-Engineer Prüfungsfragen mit Lösungen (Q81-Q86):

81. Frage
A junior data engineer seeks to leverage Delta Lake's Change Data Feed functionality to create a Type 1 table representing all of the values that have ever been valid for all rows in abronzetable created with the propertydelta.enableChangeDataFeed = true. They plan to execute the following code as a daily job:

Which statement describes the execution and results of running the above query multiple times?

A. Each time the job is executed, only those records that have been inserted or updated since the last execution will be appended to the target table giving the desired result.
B. Each time the job is executed, newly updated records will be merged into the target table, overwriting previous values with the same primary keys.
C. Each time the job is executed, the target table will be overwritten using the entire history of inserted or updated records, giving the desired result.
D. Each time the job is executed, the entire available history of inserted or updated records will be appended to the target table, resulting in many duplicate entries.
E. Each time the job is executed, the differences between the original and current versions are calculated; this may result in duplicate entries for some records.

Antwort: C

Begründung:
Explanation
This is the correct answer because it describes the execution and results of running the above query multiple times. The query uses the readChanges function to read all change events from a bronze table that has enabled change data feed. The readChanges function takes two arguments: version and options. The version argument specifies which version of the table to read changes from, and can be either a specific version number or -1 to indicate all versions. The options argument specifies additional options for reading changes, such as whether to include deletes or not. In this case, the query reads all changes from all versions of the bronze table and filters out delete events by setting includeDeletes to false. Then, it uses write.format("delta").mode("overwrite") to overwrite a target table using the entire history of inserted or updated records, giving the desired result of a Type 1 table representing all values that have ever been valid for all rows in the bronze table. Verified References: [Databricks Certified Data Engineer Professional], under
"Delta Lake" section; Databricks Documentation, under "Read changes in batch queries" section.

82. Frage
A data engineer has ingested data from an external source into a PySpark DataFrame raw_df. They need to
briefly make this data available in SQL for a data analyst to perform a quality assurance check on the data.
Which of the following commands should the data engineer run to make this data available in SQL for only
the remainder of the Spark session?

A. raw_df.createOrReplaceTempView("raw_df")
B. raw_df.saveAsTable("raw_df")
C. raw_df.write.save("raw_df")
D. raw_df.createTable("raw_df")
E. There is no way to share data between PySpark and SQL

Antwort: A

83. Frage
A junior data engineer has been asked to develop a streaming data pipeline with a grouped aggregation using DataFrame df. The pipeline needs to calculate the average humidity and average temperature for each non- overlapping five-minute interval. Incremental state information should be maintained for 10 minutes for late- arriving data.
Streaming DataFrame df has the following schema:
"device_id INT, event_time TIMESTAMP, temp FLOAT, humidity FLOAT"
Code block:

Choose the response that correctly fills in the blank within the code block to complete this task.

A. await("event_time + '10 minutes'")
B. withWatermark("event_time", "10 minutes")
C. delayWrite("event_time", "10 minutes")
D. awaitArrival("event_time", "10 minutes")
E. slidingWindow("event_time", "10 minutes")

Antwort: B

Begründung:
The correct answer is A. withWatermark("event_time", "10 minutes"). This is because the question asks for incremental state information to be maintained for 10 minutes for late-arriving data. The withWatermark method is used to define the watermark for late data. The watermark is a timestamp column and a threshold that tells the system how long to wait for late data. In this case, the watermark is set to 10 minutes. The other options are incorrect because they are not valid methods or syntax for watermarking in Structured Streaming. References:
* Watermarking: https://docs.databricks.com/spark/latest/structured-streaming/watermarks.html
* Windowed aggregations: https://docs.databricks.com/spark/latest/structured-streaming/window- operations.html

84. Frage
A data architect has determined that a table of the following format is necessary:
Which of the following code blocks uses SQL DDL commands to create an empty Delta table in the above
format regardless of whether a table already exists with this name?

A. 1. CREATE OR REPLACE TABLE table_name
2. WITH COLUMNS ( id STRING, birthDate DATE, avgRating FLOAT ) USING DELTA
B. 1. CREATE OR REPLACE TABLE table_name ( id STRING, birthDate DATE, avgRating FLOAT )
C. 1. CREATE TABLE IF NOT EXISTS table_name ( id STRING, birthDate DATE, avgRating FLOAT )
D. 1. CREATE TABLE table_name AS
2. SELECT id STRING, birthDate DATE, avgRating FLOAT
E. 1. CREATE OR REPLACE TABLE table_name AS
2. SELECT id STRING, birthDate DATE, avgRating FLOAT USING DELTA

Antwort: B

85. Frage
The marketing team is looking to share data in an aggregate table with the sales organization, but the field names used by the teams do not match, and a number of marketing specific fields have not been approval for the sales org.
Which of the following solutions addresses the situation while emphasizing simplicity?

A. Add a parallel table write to the current production pipeline, updating a new sales table that varies as required from marketing table.
B. Create a view on the marketing table selecting only these fields approved for the sales team alias the names of any fields that should be standardized to the sales naming conventions.
C. Create a new table with the required schema and use Delta Lake's DEEP CLONE functionality to sync up changes committed to one table to the corresponding table.
D. Use a CTAS statement to create a derivative table from the marketing table configure a production jon to propagation changes.

Antwort: B

Begründung:
Creating a view is a straightforward solution that can address the need for field name standardization and selective field sharing between departments. A view allows for presenting a transformed version of the underlying data without duplicating it. In this scenario, the view would only include the approved fields for the sales team and rename any fields as per their naming conventions.
Reference:
Databricks documentation on using SQL views in Delta Lake: https://docs.databricks.com/delta/quick-start.html#sql-views

86. Frage
......

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