Databricks selftestengine new Release Databricks-certified-professional-data-engineer Databricks Certification Questions by Goldie q200 vce pdf

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

Exam Name:	Databricks Certified Data Engineer Professional Exam
Exam Code:	Databricks-Certified-Professional-Data-Engineer Dumps
Vendor:	Databricks	Certification:	Databricks Certification
Questions:	195 Q&A's	Shared By:	goldie

Question 36

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?

Options:

Set the trigger interval to 500 milliseconds; setting a small but non-zero trigger interval ensures that the source is not queried too frequently.

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.

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.

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.

Discussion

Question 37

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?

Options:

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

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

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

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

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

Discussion

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Question 38

In order to facilitate near real-time workloads, a data engineer is creating a helper function to leverage the schema detection and evolution functionality of Databricks Auto Loader. The desired function will automatically detect the schema of the source directly, incrementally process JSON files as they arrive in a source directory, and automatically evolve the schema of the table when new fields are detected.

The function is displayed below with a blank:

Questions 38

Which response correctly fills in the blank to meet the specified requirements?

Questions 38

Options:

Option A

Option B

Option C

Option D

Option E

Discussion

Question 39

A data architect has designed a system in which two Structured Streaming jobs will concurrently write to a single bronze Delta table. Each job is subscribing to a different topic from an Apache Kafka source, but they will write data with the same schema. To keep the directory structure simple, a data engineer has decided to nest a checkpoint directory to be shared by both streams.

The proposed directory structure is displayed below:

Questions 39