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Databricks Certified Professional Data Engineer is a certification exam that measures individuals' knowledge and skills in using Databricks to manipulate big data. Databricks is a cloud-based data processing platform that allows data engineers to build, deploy, and manage big data processing pipelines. Databricks Certified Professional Data Engineer Exam certification exam is designed to validate the expertise of data engineers who work with Databricks.

The Databricks Certified Professional Data Engineer Exam certification exam covers a range of topics, including data ingestion, transformation, and storage, ETL processes, data modeling, and machine learning. Candidates are tested on their ability to use Databricks tools and technologies to solve real-world data engineering problems. Databricks-Certified-Professional-Data-Engineer exam also evaluates the candidate's understanding of best practices for data engineering, including security, scalability, and cost optimization. By passing the Databricks Certified Professional Data Engineer exam, candidates can demonstrate their proficiency in Databricks data engineering technologies and enhance their job prospects in the field.

Databricks Certified Professional Data Engineer exam covers a wide range of topics, such as data ingestion, transformation, storage, and processing. Databricks-Certified-Professional-Data-Engineer Exam Tests the candidates' ability to use Databricks tools and technologies to solve real-world problems and challenges. Candidates who pass Databricks-Certified-Professional-Data-Engineer exam demonstrate their proficiency in designing, building, and managing data pipelines with Databricks, which is a leading cloud-based platform for big data processing and analytics.

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Databricks Certified Professional Data Engineer Exam Sample Questions (Q115-Q120):

NEW QUESTION # 115
A Spark job is taking longer than expected. Using the Spark UI, a data engineer notes that the Min, Median, and Max Durations for tasks in a particular stage show the minimum and median time to complete a task as roughly the same, but the max duration for a task to be roughly 100 times as long as the minimum.
Which situation is causing increased duration of the overall job?

A. Spill resulting from attached volume storage being too small.
B. Task queueing resulting from improper thread pool assignment.
C. Skew caused by more data being assigned to a subset of spark-partitions.
D. Network latency due to some cluster nodes being in different regions from the source data
E. Credential validation errors while pulling data from an external system.

Answer: C

Explanation:
This is the correct answer because skew is a common situation that causes increased duration of the overall job. Skew occurs when some partitions have more data than others, resulting in uneven distribution of work among tasks and executors. Skew can be caused by various factors, such as skewed data distribution, improper partitioning strategy, or join operations with skewed keys. Skew can lead to performance issues such as long-running tasks, wasted resources, or even task failures due to memory or disk spills. Verified References:
[Databricks Certified Data Engineer Professional], under "Performance Tuning" section; Databricks Documentation, under "Skew" section.

NEW QUESTION # 116
A small company based in the United States has recently contracted a consulting firm in India to implement several new data engineering pipelines to power artificial intelligence applications. All the company's data is stored in regional cloud storage in the United States.
The workspace administrator at the company is uncertain about where the Databricks workspace used by the contractors should be deployed.
Assuming that all data governance considerations are accounted for, which statement accurately informs this decision?

A. Databricks leverages user workstations as the driver during interactive development; as such, users should always use a workspace deployed in a region they are physically near.
B. Databricks runs HDFS on cloud volume storage; as such, cloud virtual machines must be deployed in the region where the data is stored.
C. Cross-region reads and writes can incur significant costs and latency; whenever possible, compute should be deployed in the same region the data is stored.
D. Databricks workspaces do not rely on any regional infrastructure; as such, the decision should be made based upon what is most convenient for the workspace administrator.
E. Databricks notebooks send all executable code from the user's browser to virtual machines over the open internet; whenever possible, choosing a workspace region near the end users is the most secure.

Answer: C

Explanation:
This is the correct answer because it accurately informs this decision. The decision is about where the Databricks workspace used by the contractors should be deployed. The contractors are based in India, while all the company's data is stored in regional cloud storage in the United States. When choosing a region for deploying a Databricks workspace, one of the important factors to consider is the proximity to the data sources and sinks. Cross-region reads and writes can incur significant costs and latency due to network bandwidth and data transfer fees. Therefore, whenever possible, compute should be deployed in the same region the data is stored to optimize performance and reduce costs. Verified Reference: [Databricks Certified Data Engineer Professional], under "Databricks Workspace" section; Databricks Documentation, under "Choose a region" section.

NEW QUESTION # 117
The data engineering team maintains a table of aggregate statistics through batch nightly updates. This includes total sales for the previous day alongside totals and averages for a variety of time periods including the 7 previous days, year-to-date, and quarter-to-date. This table is namedstore_saies_summaryand the schema is as follows:

The tabledaily_store_salescontains all the information needed to updatestore_sales_summary. The schema for this table is:
store_id INT, sales_date DATE, total_sales FLOAT
Ifdaily_store_salesis implemented as a Type 1 table and thetotal_salescolumn might be adjusted after manual data auditing, which approach is the safest to generate accurate reports in thestore_sales_summarytable?

A. Use Structured Streaming to subscribe to the change data feed for daily_store_sales and apply changes to the aggregates in the store_sales_summary table with each update.
B. Implement the appropriate aggregate logic as a Structured Streaming read against the daily_store_sales table and use upsert logic to update results in the store_sales_summary table.
C. Implement the appropriate aggregate logic as a batch read against the daily_store_sales table and append new rows nightly to the store_sales_summary table.
D. Implement the appropriate aggregate logic as a batch read against the daily_store_sales table and overwrite the store_sales_summary table with each Update.
E. Implement the appropriate aggregate logic as a batch read against the daily_store_sales table and use upsert logic to update results in the store_sales_summary table.

Answer: A

Explanation:
The daily_store_sales table contains all the information needed to update store_sales_summary. The schema of the table is:
store_id INT, sales_date DATE, total_sales FLOAT
The daily_store_sales table is implemented as a Type 1 table, which means that old values are overwritten by new values and no history is maintained. The total_sales column might be adjusted after manual data auditing, which means that the data in the table may change over time.
The safest approach to generate accurate reports in the store_sales_summary table is to use Structured Streaming to subscribe to the change data feed for daily_store_sales and apply changes to the aggregates in the store_sales_summary table with each update. Structured Streaming is a scalable and fault-tolerant stream processing engine built on Spark SQL. Structured Streaming allows processing data streams as if they were tables or DataFrames, using familiar operations such as select, filter, groupBy, or join. Structured Streaming also supports output modes that specify how to write the results of a streaming query to a sink, such as append, update, or complete. Structured Streaming can handle both streaming and batch data sources in a unified manner.
The change data feed is a feature of Delta Lake that provides structured streaming sources that can subscribe to changes made to a Delta Lake table. The change data feed captures both data changes and schema changes as ordered events that can be processed by downstream applications or services. The change data feed can be configured with different options, such as starting from a specific version or timestamp, filtering by operation type or partition values, or excluding no-op changes.
By using Structured Streaming to subscribe to the change data feed for daily_store_sales, one can capture and process any changes made to the total_sales column due to manual data auditing. By applying these changes to the aggregates in the store_sales_summary table with each update, one can ensure that the reports are always consistent and accurate with the latest data. Verified References: [Databricks Certified Data Engineer Professional], under "Spark Core" section; Databricks Documentation, under "Structured Streaming" section; Databricks Documentation, under "Delta Change Data Feed" section.

NEW QUESTION # 118
When you drop a managed table using SQL syntax DROP TABLE table_name how does it impact metadata, history, and data stored in the table?

A. Drops table from meta store, meta data and history but keeps the data in storage
B. Drops table from meta store and data from storage but keeps metadata and history in storage
C. Drops table and history but keeps meta data and data in storage
D. Drops table from meta store, drops metadata, history, and data in storage.
E. Drops table but keeps meta data, history and data in storage

Answer: D

Explanation:
Explanation
For a managed table, a drop command will drop everything from metastore and storage.
See the below image to understand the differences between dropping an external table.
Diagram Description automatically generated

NEW QUESTION # 119
A team member is leaving the team and he/she is currently the owner of the few tables, instead of transfering the ownership to a user you have decided to transfer the ownership to a group so in the future anyone in the group can manage the permissions rather than a single individual, which of the following commands help you accomplish this?

A. ALTER OWNER ON table_name to 'group'
B. GRANT OWNER table_name to 'group'*
C. ALTER TABLE table_name OWNER to 'group'
D. GRANT OWNER On table_name to 'group'
E. TRANSFER OWNER table_name to 'group'

Answer: C

Explanation:
Explanation
The answer is ALTER TABLE table_name OWNER to 'group'
Assign owner to object

NEW QUESTION # 120
......

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Quiz Databricks-Certified-Professional-Data-Engineer - Efficient Databricks Certified Professional Data Engineer Exam Exam Overview

How Can I Prepare Databricks-Certified-Professional-Data-Engineer Exam Questions In One Week? [2025]

Databricks Certified Professional Data Engineer Exam Sample Questions (Q115-Q120):

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