Practice Databricks-Certified-Professional-Data-Engineer Exams | Databricks-Certified-Professional-Data-Engineer Latest Exam Guide

Another thing you will get from using the Databricks-Certified-Professional-Data-Engineer Exam study material is free to support. If you encounter any problem while using the Databricks-Certified-Professional-Data-Engineer material, you have nothing to worry about. The solution is closer to you than you can imagine, just contact the support team and continue enjoying your study with the Databricks Certified Professional Data Engineer Exam preparation material.

Our Databricks-Certified-Professional-Data-Engineer learning quiz has accompanied many people on their way to success and they will help you for sure. And you will learn about some of the advantages of our Databricks-Certified-Professional-Data-Engineer training prep if you just free download the demos to have a check. You will understand that this is really a successful Databricks-Certified-Professional-Data-Engineer Exam Questions that allows you to do more with less. With our Databricks-Certified-Professional-Data-Engineer study materials for 20 to 30 hours, we can claim that you will pass the exam and get what you want.

>> Practice Databricks-Certified-Professional-Data-Engineer Exams <<

2025 Databricks Efficient Practice Databricks-Certified-Professional-Data-Engineer Exams

If you want to pass your exam and get your certification, we can make sure that our Databricks Certification guide questions will be your ideal choice. Our company will provide you with professional team, high quality service and reasonable price. In order to help customers solve problems, our company always insist on putting them first and providing valued service. We deeply believe that our Databricks-Certified-Professional-Data-Engineer question torrent will help you pass the exam and get your certification successfully in a short time. Maybe you cannot wait to understand our Databricks-Certified-Professional-Data-Engineer Guide questions; we can promise that our products have a higher quality when compared with other study materials. At the moment I am willing to show our Databricks-Certified-Professional-Data-Engineer guide torrents to you, and I can make a bet that you will be fond of our products if you understand it.

Databricks Certified Professional Data Engineer Certification Exam is created to challenge data engineers with the significant knowledge of Databricks’ data engineering principles and techniques. To become Databricks certified, a candidate must pass the online certification exam designed for data engineers. Databricks-Certified-Professional-Data-Engineer Exam is scenario-based, comprises of 80 multiple-choice questions, and has a time limit of 120 minutes. The Certification exam tests the candidate's knowledge in topics such as data ingestion, data processing, data engineering, ETL, and data warehousing.

Databricks Certified Professional Data Engineer Exam Sample Questions (Q54-Q59):

NEW QUESTION # 54
What is the best way to query external csv files located on DBFS Storage to inspect the data using SQL?

A. You can not query external files directly, us COPY INTO to load the data into a table first
B. SELECT * FROM 'dbfs:/location/csv_files/' FORMAT = 'CSV'
C. SELECT CSV. * from 'dbfs:/location/csv_files/'
D. SELECT * FROM 'dbfs:/location/csv_files/' USING CSV
E. SELECT * FROM CSV. 'dbfs:/location/csv_files/'

Answer: E

Explanation:
Explanation
Answer is, SELECT * FROM CSV. 'dbfs:/location/csv_files/'
you can query external files stored on the storage using below syntax
SELECT * FROM format.`/Location`
format - CSV, JSON, PARQUET, TEXT

NEW QUESTION # 55
A Delta Lake table representing metadata about content posts from users has the following schema:
user_id LONG, post_text STRING, post_id STRING, longitude FLOAT, latitude FLOAT, post_time TIMESTAMP, date DATE This table is partitioned by the date column. A query is run with the following filter:
longitude < 20 & longitude > -20
Which statement describes how data will be filtered?

A. Statistics in the Delta Log will be used to identify data files that might include records in the filtered range.
B. No file skipping will occur because the optimizer does not know the relationship between the partition column and the longitude.
C. The Delta Engine will use row-level statistics in the transaction log to identify the flies that meet the filter criteria.
D. The Delta Engine will scan the parquet file footers to identify each row that meets the filter criteria.
E. Statistics in the Delta Log will be used to identify partitions that might Include files in the filtered range.

Answer: A

Explanation:
Explanation
This is the correct answer because it describes how data will be filtered when a query is run with the following filter: longitude < 20 & longitude > -20. The query is run on a Delta Lake table that has the following schema:
user_id LONG, post_text STRING, post_id STRING, longitude FLOAT, latitude FLOAT, post_time TIMESTAMP, date DATE. This table is partitioned by the date column. When a query is run on a partitioned Delta Lake table, Delta Lake uses statistics in the Delta Log to identify data files that might include records in the filtered range. The statistics include information such as min and max values for each column in each data file. By using these statistics, Delta Lake can skip reading data files that do not match the filter condition, which can improve query performance and reduce I/O costs. Verified References: [Databricks Certified Data Engineer Professional], under "Delta Lake" section; Databricks Documentation, under "Data skipping" section.

NEW QUESTION # 56
The DevOps team has configured a production workload as a collection of notebooks scheduled to run daily using the Jobs UI. A new data engineering hire is onboarding to the team and has requested access to one of these notebooks to review the production logic.
What are the maximum notebook permissions that can be granted to the user without allowing accidental changes to production code or data?

A. Can Read
B. Can Manage
C. Can Edit
D. No permissions
E. Can Run

Answer: A

Explanation:
This is the correct answer because it is the maximum notebook permissions that can be granted to the user without allowing accidental changes to production code or data. Notebook permissions are used to control access to notebooks in Databricks workspaces. There are four types of notebook permissions: Can Manage, Can Edit, Can Run, and Can Read. Can Manage allows full control over the notebook, including editing, running, deleting, exporting, and changing permissions. Can Edit allows modifying and running the notebook, but not changing permissions or deleting it. Can Run allows executing commands in an existing cluster attached to the notebook, but not modifying or exporting it. Can Read allows viewing the notebook content, but not running or modifying it. In this case, granting Can Read permission to the user will allow them to review the production logic in the notebook without allowing them to make any changes to it or run any commands that may affect production data. Verified References: [Databricks Certified Data Engineer Professional], under "Databricks Workspace" section; Databricks Documentation, under "Notebook permissions" section.

NEW QUESTION # 57
A user wants to use DLT expectations to validate that a derived table report contains all records from the source, included in the table validation_copy.
The user attempts and fails to accomplish this by adding an expectation to the report table definition.

Which approach would allow using DLT expectations to validate all expected records are present in this table?

A. Define a view that performs a left outer join on validation_copy and report, and reference this view in DLT expectations for the report table
B. Define a temporary table that perform a left outer join on validation_copy and report, and define an expectation that no report key values are null
C. Define a function that performs a left outer join on validation_copy and report and report, and check against the result in a DLT expectation for the report table
D. Define a SQL UDF that performs a left outer join on two tables, and check if this returns null values for report key values in a DLT expectation for the report table.

Answer: A

Explanation:
To validate that all records from the source are included in the derived table, creating a view that performs a left outer join between thevalidation_copytable and thereporttable is effective. The view can highlight any discrepancies, such as null values in the report table's key columns, indicating missing records. This view can then be referenced in DLT (Delta Live Tables) expectations for thereporttable to ensure data integrity. This approach allows for a comprehensive comparison between the source and the derived table.
References:
* Databricks Documentation on Delta Live Tables and Expectations: Delta Live Tables Expectations

NEW QUESTION # 58
A junior data engineer is working to implement logic for a Lakehouse table named silver_device_recordings. The source data contains 100 unique fields in a highly nested JSON structure.
The silver_device_recordings table will be used downstream to power several production monitoring dashboards and a production model. At present, 45 of the 100 fields are being used in at least one of these applications.
The data engineer is trying to determine the best approach for dealing with schema declaration given the highly-nested structure of the data and the numerous fields.
Which of the following accurately presents information about Delta Lake and Databricks that may impact their decision-making process?

A. Human labor in writing code is the largest cost associated with data engineering workloads; as such, automating table declaration logic should be a priority in all migration workloads.
B. Because Databricks will infer schema using types that allow all observed data to be processed, setting types manually provides greater assurance of data quality enforcement.
C. The Tungsten encoding used by Databricks is optimized for storing string data; newly-added native support for querying JSON strings means that string types are always most efficient.
D. Because Delta Lake uses Parquet for data storage, data types can be easily evolved by just modifying file footer information in place.
E. Schema inference and evolution on .Databricks ensure that inferred types will always accurately match the data types used by downstream systems.

Answer: B

Explanation:
This is the correct answer because it accurately presents information about Delta Lake and Databricks that may impact the decision-making process of a junior data engineer who is trying to determine the best approach for dealing with schema declaration given the highly-nested structure of the data and the numerous fields. Delta Lake and Databricks support schema inference and evolution, which means that they can automatically infer the schema of a table from the source data and allow adding new columns or changing column types without affecting existing queries or pipelines. However, schema inference and evolution may not always be desirable or reliable, especially when dealing with complex or nested data structures or when enforcing data quality and consistency across different systems. Therefore, setting types manually can provide greater assurance of data quality enforcement and avoid potential errors or conflicts due to incompatible or unexpected data types. Verified Reference: [Databricks Certified Data Engineer Professional], under "Delta Lake" section; Databricks Documentation, under "Schema inference and partition of streaming DataFrames/Datasets" section.

NEW QUESTION # 59
......

The Databricks Certified Professional Data Engineer Exam exam questions are very similar to actual Databricks Certified Professional Data Engineer Exam Databricks-Certified-Professional-Data-Engineer Exam Questions. So it creates a real Databricks-Certified-Professional-Data-Engineer exam scenario for trustworthy users. As it is a Browser-Based Databricks Certified Professional Data Engineer Exam Databricks-Certified-Professional-Data-Engineer practice exam so there is no need for any installation. The Web-Based Databricks Certified Professional Data Engineer Exam practice exam is supported by all major browsers like Chrome, IE, Firefox, Opera, and Safari. Furthermore, no special plugins are required to start your journey toward a bright career.

Databricks-Certified-Professional-Data-Engineer Latest Exam Guide: https://www.examcollectionpass.com/Databricks/Databricks-Certified-Professional-Data-Engineer-practice-exam-dumps.html