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Databricks DATABRICKS-CERTIFIED-PROFESSIONAL-DATA-ENGINEER Exam Dumps

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Databricks DATABRICKS-CERTIFIED-PROFESSIONAL-DATA-ENGINEER Exam Dumps

Vendor: Databricks
Exam Code: DATABRICKS-CERTIFIED-PROFESSIONAL-DATA-ENGINEER
Exam Name: Databricks Certified Professional Data Engineer Exam
Certification: Databricks Certification
Total Questions: 120 Q&A
Updated on: Dec 16, 2024
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Same Questions from the Latest Real Exam

DATABRICKS-CERTIFIED-PROFESSIONAL-DATA-ENGINEER Online Practice Questions and Answers

Questions 1

A user new to Databricks is trying to troubleshoot long execution times for some pipeline logic they are working on. Presently, the user is executing code cell-by-cell, using display() calls to confirm code is producing the logically correct results as new transformations are added to an operation. To get a measure of average time to execute, the user is running each cell multiple times interactively.

Which of the following adjustments will get a more accurate measure of how code is likely to perform in production?

A. Scala is the only language that can be accurately tested using interactive notebooks; because the best performance is achieved by using Scala code compiled to JARs. all PySpark and Spark SQL logic should be refactored.

B. The only way to meaningfully troubleshoot code execution times in development notebooks Is to use production-sized data and production-sized clusters with Run All execution.

C. Production code development should only be done using an IDE; executing code against a local build of open source Spark and Delta Lake will provide the most accurate benchmarks for how code will perform in production.

D. Calling display () forces a job to trigger, while many transformations will only add to the logical query plan; because of caching, repeated execution of the same logic does not provide meaningful results.

E. The Jobs Ul should be leveraged to occasionally run the notebook as a job and track execution time during incremental code development because Photon can only be enabled on clusters launched for scheduled jobs.

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Questions 2

A junior data engineer has configured a workload that posts the following JSON to the Databricks REST API endpoint 2.0/jobs/create.

Assuming that all configurations and referenced resources are available, which statement describes the result of executing this workload three times?

A. Three new jobs named "Ingest new data" will be defined in the workspace, and they will each run once daily.

B. The logic defined in the referenced notebook will be executed three times on new clusters with the configurations of the provided cluster ID.

C. Three new jobs named "Ingest new data" will be defined in the workspace, but no jobs will be executed.

D. One new job named "Ingest new data" will be defined in the workspace, but it will not be executed.

E. The logic defined in the referenced notebook will be executed three times on the referenced existing all purpose cluster.

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Questions 3

A data engineer is performing a join operating to combine values from a static userlookup table with a streaming DataFrame streamingDF. Which code block attempts to perform an invalid stream-static join?

A. userLookup.join(streamingDF, ["userid"], how="inner")

B. streamingDF.join(userLookup, ["user_id"], how="outer")

C. streamingDF.join(userLookup, ["user_id"], how="left")

D. streamingDF.join(userLookup, ["userid"], how="inner")

E. userLookup.join(streamingDF, ["user_id"], how="right")

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