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Databricks Certified Professional Data Engineer is a certification exam that tests the skills and knowledge required to design and implement data solutions using Databricks. Databricks is a cloud-based data platform that helps organizations manage and process large amounts of data. Databricks Certified Professional Data Engineer Exam certification exam is designed for data engineers who are responsible for creating and maintaining data pipelines, managing data storage, and implementing data solutions.

Databricks Certified Professional Data Engineer Exam is an online exam that can be taken from anywhere in the world. Databricks-Certified-Professional-Data-Engineer Exam is timed and consists of multiple-choice questions and hands-on tasks that require the candidate to demonstrate their ability to perform specific tasks using Databricks. Databricks-Certified-Professional-Data-Engineer exam is designed to be challenging and requires a thorough understanding of Databricks data engineering concepts and techniques.

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The Databricks Databricks-Certified-Professional-Data-Engineer Exam consists of multiple-choice questions and hands-on exercises designed to test the candidate's knowledge and skills in working with Databricks. Candidates who pass the exam will be awarded the Databricks Certified Professional Data Engineer certification, which is recognized by employers worldwide as a validation of the candidate's expertise and proficiency in building and maintaining data pipelines using Databricks. Overall, the Databricks Certified Professional Data Engineer certification exam is a valuable credential for anyone looking to advance their career in big data engineering and analytics.

Databricks Certified Professional Data Engineer Exam Sample Questions (Q104-Q109):

NEW QUESTION # 104
A dataset has been defined using Delta Live Tables and includes an expectations clause:
1. CONSTRAINT valid_timestamp EXPECT (timestamp > '2020-01-01')
What is the expected behaviour when a batch of data containing data that violates these constraints is
processed?

A. Records that violate the expectation are added to the target dataset and recorded as invalid in the event log
B. Records that violate the expectation are dropped from the target dataset and recorded as invalid in the event log
C. Records that violate the expectation are added to the target dataset and flagged as in-valid in a field added to the target dataset
D. Records that violate the expectation are dropped from the target dataset and loaded into a quarantine table
E. Records that violate the expectation cause the job to fail

Answer: A

NEW QUESTION # 105
A junior data engineer has been asked to develop a streaming data pipeline with a grouped aggregation using DataFrame df. The pipeline needs to calculate the average humidity and average temperature for each non-overlapping five-minute interval. Events are recorded once per minute per device.
Streaming DataFrame df has the following schema:
"device_id INT, event_time TIMESTAMP, temp FLOAT, humidity FLOAT"
Code block:
Choose the response that correctly fills in the blank within the code block to complete this task.

A. window("event_time", "5 minutes").alias("time")
B. window("event_time", "10 minutes").alias("time")
C. "event_time"
D. lag("event_time", "10 minutes").alias("time")
E. to_interval("event_time", "5 minutes").alias("time")

Answer: A

Explanation:
This is the correct answer because the window function is used to group streaming data by time intervals. The window function takes two arguments: a time column and a window duration. The window duration specifies how long each window is, and must be a multiple of 1 second. In this case, the window duration is "5 minutes", which means each window will cover a non-overlapping five-minute interval. The window function also returns a struct column with two fields: start and end, which represent the start and end time of each window. The alias function is used to rename the struct column as "time". Verified References: [Databricks Certified Data Engineer Professional], under "Structured Streaming" section; Databricks Documentation, under "WINDOW" section.
https://www.databricks.com/blog/2017/05/08/event-time-aggregation-watermarking-apache-sparks-structured-str

NEW QUESTION # 106
Data science team members are using a single cluster to perform data analysis, although cluster size was chosen to handle multiple users and auto-scaling was enabled, the team realized queries are still running slow, what would be the suggested fix for this?

A. Setup multiple clusters so each team member has their own cluster
B. Disable the auto-scaling feature
C. Increase the size of the driver node
D. Use High concurrency mode instead of the standard mode

Answer: D

Explanation:
Explanation
The answer is Use High concurrency mode instead of the standard mode,
https://docs.databricks.com/clusters/cluster-config-best-practices.html#cluster-mode High Concurrency clusters are ideal for groups of users who need to share resources or run ad-hoc jobs.
Databricks recommends enabling autoscaling for High Concurrency clusters.

NEW QUESTION # 107
Which of the following is not a privilege in the Unity catalog?

A. CREATE TABLE
B. EXECUTE
C. SELECT
D. DELETE
E. MODIFY

Answer: D

Explanation:
Explanation
The Answer is DELETE and UPDATE permissions do not exit, you have to use MODIFY which provides both Update and Delete permissions.
Please note: TABLE ACL privilege types are different from Unity Catalog privilege types, please read the question carefully.
Here is the list of all privileges in Unity Catalog:

Unity Catalog Privileges
https://learn.microsoft.com/en-us/azure/databricks/spark/latest/spark-sql/language-manual/sql-ref-privileges#priv Table ACL privileges
https://learn.microsoft.com/en-us/azure/databricks/security/access-control/table-acls/object-privileges#privileges

NEW QUESTION # 108
A data architect has heard about lake's built-in versioning and time travel capabilities. For auditing purposes they have a requirement to maintain a full of all valid street addresses as they appear in the customers table.
The architect is interested in implementing a Type 1 table, overwriting existing records with new values and relying on Delta Lake time travel to support long-term auditing. A data engineer on the project feels that a Type 2 table will provide better performance and scalability.
Which piece of information is critical to this decision?

A. Data corruption can occur if a query fails in a partially completed state because Type 2 tables requires Setting multiple fields in a single update.
B. Delta Lake time travel does not scale well in cost or latency to provide a long-term versioning solution.
C. Shallow clones can be combined with Type 1 tables to accelerate historic queries for long-term versioning.
D. Delta Lake time travel cannot be used to query previous versions of these tables because Type 1 changes modify data files in place.

Answer: B

Explanation:
Delta Lake's time travel feature allows users to access previous versions of a table, providing a powerful tool for auditing and versioning. However, using time travel as a long-term versioning solution for auditing purposes can be less optimal in terms of cost and performance, especially as the volume of data and the number of versions grow. For maintaining a full history of valid street addresses as they appear in a customers table, using a Type 2 table (where each update creates a new record with versioning) might provide better scalability and performance by avoiding the overhead associated with accessing older versions of a large table. While Type 1 tables, where existing records are overwritten with new values, seem simpler and can leverage time travel for auditing, the critical piece of information is that time travel might not scale well in cost or latency for long-term versioning needs, making a Type 2 approach more viable for performance and scalability.References:
* Databricks Documentation on Delta Lake's Time Travel: Delta Lake Time Travel
* Databricks Blog on Managing Slowly Changing Dimensions in Delta Lake: Managing SCDs in Delta Lake

NEW QUESTION # 109
......

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Valid Databricks-Certified-Professional-Data-Engineer Test Guide - Databricks-Certified-Professional-Data-Engineer Reliable Exam Test

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Databricks Certified Professional Data Engineer Exam Sample Questions (Q104-Q109):

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