Databricks exams4sure complete Databricks-certified-data-engineer-associate Materials by Millie-mae q41 vce pdf

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Exam Name:	Databricks Certified Data Engineer Associate Exam
Exam Code:	Databricks-Certified-Data-Engineer-Associate Dumps
Vendor:	Databricks	Certification:	Databricks Certification
Questions:	230 Q&A's	Shared By:	millie-mae

Question 56

An organization plans to share a large dataset stored in a Databricks workspace on AWS with a partner organization whose Databricks workspace is hosted on Azure. The data engineer wants to minimize data transfer costs while ensuring secure and efficient data sharing.

Which strategy will reduce data egress costs associated with cross-cloud data sharing?

Options:

Sharing data via pre-signed URLs without monitoring egress costs

Migrating the dataset to Cloudflare R2 object storage before sharing

Configure VPN connection between AWS and Azure for faster data sharing

Using Delta Sharing without any additional configurations

Discussion

Question 57

A data engineer is reviewing the documentation on audit logs in Databricks for compliance purposes and needs to understand the format in which audit logs output events.

How are events formatted in Databricks audit logs?

Options:

In Databricks, audit logs output events in a plain text format. In Databricks, audit logs output events in a JSON format.

In Databricks, audit logs output events in an XML format.

In Databricks, audit logs output events in a CSV format.

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Question 58

A data engineer is joining a very large events_df DataFrame containing billions of rows with a small country_lookup_df DataFrame containing fewer than 10,000 rows. The DataFrames are joined on the country_code column. The join is slow because of data shuffling.

How should the data engineer improve performance?

Options:

Broadcast events_df when performing the join.

Cache both DataFrames, and then run the same join.

Persist both DataFrames, and then run the same join.

Broadcast country_lookup_df when performing the join.

Discussion

Question 59

A data engineer is cleaning a Bronze table. The requirement is to eliminate rows where either the customer_email field or the customer_phone field is NULL. The cleaning must be performed in one operation using a single method call.

Which PySpark approach supports filtering multiple columns for NULL values in one call?

Options:

df.dropna(subset=[ " customer_email " , " customer_phone " ])

df.where( " customer_email IS NOT NULL " ).where( " customer_phone IS NOT NULL " )

df.na.drop(how= " all " )

df.filter(col( " customer_email " ).isNotNull() & col( " customer_phone " ).isNotNull())