Databricks udemy selected Databricks-certified-professional-data-engineer Databricks Certification Questions Answers by Mae q67 vce pdf

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Exam Name:	Databricks Certified Data Engineer Professional Exam
Exam Code:	Databricks-Certified-Professional-Data-Engineer Dumps
Vendor:	Databricks	Certification:	Databricks Certification
Questions:	202 Q&A's	Shared By:	mae

Question 20

A nightly job ingests data into a Delta Lake table using the following code:

Questions 20

The next step in the pipeline requires a function that returns an object that can be used to manipulate new records that have not yet been processed to the next table in the pipeline.

Which code snippet completes this function definition?

def new_records():

Options:

return spark.readStream.table( " bronze " )

return spark.readStream.load( " bronze " )

Option C 20

return spark.read.option( " readChangeFeed " , " true " ).table ( " bronze " )

Option E 20

Discussion

Question 21

A data engineer created a daily batch ingestion pipeline using a cluster with the latest DBR version to store banking transaction data, and persisted it in a MANAGED DELTA table called prod.gold.all_banking_transactions_daily. The data engineer is constantly receiving complaints from business users who query this table ad hoc through a SQL Serverless Warehouse about poor query performance. Upon analysis, the data engineer identified that these users frequently use high-cardinality columns as filters. The engineer now seeks to implement a data layout optimization technique that is incremental, easy to maintain, and can evolve over time.

Which command should the data engineer implement?

Options:

Alter the table to use Hive-Style Partitions + Z-ORDER and implement a periodic OPTIMIZE command.

Alter the table to use Liquid Clustering and implement a periodic OPTIMIZE command.

Alter the table to use Hive-Style Partitions and implement a periodic OPTIMIZE command.

Alter the table to use Z-ORDER and implement a periodic OPTIMIZE command.

Discussion

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Question 22

To identify the top users consuming compute resources, a data engineering team needs to monitor usage within their Databricks workspace for better resource utilization and cost control. The team decided to use Databricks system tables, available under the System catalog in Unity Catalog, to gain detailed visibility into workspace activity.

Which SQL query should the team run from the System catalog to achieve this?

Options:

SELECT sku_name,

identity_metadata.created_by AS user_email,

COUNT(usage_quantity) AS total_dbus

FROM system.billing.usage

GROUP BY user_email, sku_name

ORDER BY total_dbus DESC

LIMIT 10

SELECT identity_metadata.run_as AS user_email,

SUM(usage_quantity) AS total_dbus

FROM system.billing.usage

GROUP BY user_email

ORDER BY total_dbus DESC

LIMIT 10

SELECT sku_name,

identity_metadata.created_by AS user_email,

SUM(usage_quantity * usage_unit) AS total_dbus

FROM system.billing.usage

GROUP BY user_email, sku_name

ORDER BY total_dbus DESC

LIMIT 10

SELECT sku_name,

usage_metadata.run_name AS user_email,

SUM(usage_quantity) AS total_dbus

FROM system.billing.usage

GROUP BY user_email, sku_name

ORDER BY total_dbus DESC

LIMIT 10

Discussion

Question 23

A data engineer is tasked with building a nightly batch ETL pipeline that processes very large volumes of raw JSON logs from a data lake into Delta tables for reporting. The data arrives in bulk once per day, and the pipeline takes several hours to complete. Cost efficiency is important, but performance and reliability of completing the pipeline are the highest priorities.

Which type of Databricks cluster should the data engineer configure?

Options:

A lightweight single-node cluster with low worker node count to reduce costs.

A high-concurrency cluster designed for interactive SQL workloads.

An all-purpose cluster always kept running to ensure low-latency job startup times.

A job cluster configured to autoscale across multiple workers during the pipeline run.