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Databricks Certified Professional Data Engineer exam covers a wide range of topics, including data engineering concepts, Databricks architecture, data ingestion and processing, data storage and management, and data security. Databricks-Certified-Professional-Data-Engineer Exam consists of 60 multiple-choice questions and participants have 90 minutes to complete it. Passing the exam requires a score of 70% or higher, and successful candidates receive a certificate that validates their expertise in building and managing data pipelines on the Databricks platform.

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Databricks-Certified-Professional-Data-Engineer certification exam is a comprehensive test that covers all aspects of data engineering with Databricks. Databricks-Certified-Professional-Data-Engineer exam is designed to test the candidate's knowledge of Databricks architecture, data engineering concepts, data processing with Databricks, and data storage with Databricks. Databricks-Certified-Professional-Data-Engineer Exam also tests the candidate's ability to design, implement, and maintain data engineering solutions using Databricks.

Databricks Certified Professional Data Engineer Exam Sample Questions (Q89-Q94):

NEW QUESTION # 89
Which of the following developer operations in CI/CD flow can be implemented in Databricks Re-pos?

A. Resolve merge conflicts
B. Trigger Databricks Repos API to pull the latest version of code into production folder
C. Delete a branch
D. Merge when code is committed
E. Pull request and review process

Answer: B

Explanation:
Explanation
See the below diagram to understand the role Databricks Repos and Git provider plays when building a CI/CD workflow.
All the steps highlighted in yellow can be done Databricks Repo, all the steps highlighted in Gray are done in a git provider like Github or Azure DevOps

NEW QUESTION # 90
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. Shallow clones can be combined with Type 1 tables to accelerate historic queries for long-term versioning.
C. Delta Lake time travel does not scale well in cost or latency to provide a long-term versioning solution.
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: C

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 # 91
In order to facilitate near real-time workloads, a data engineer is creating a helper function to leverage the schema detection and evolution functionality of Databricks Auto Loader. The desired function will automatically detect the schema of the source directly, incrementally process JSON files as they arrive in a source directory, and automatically evolve the schema of the table when new fields are detected.
The function is displayed below with a blank:

Which response correctly fills in the blank to meet the specified requirements?

A. Option A
B. Option E
C. Option B
D. Option D
E. Option C

Answer: C

Explanation:
Option B correctly fills in the blank to meet the specified requirements. Option B uses the
"cloudFiles.schemaLocation" option, which is required for the schema detection andevolution functionality of Databricks Auto Loader. Additionally, option B uses the "mergeSchema" option, which is required for the schema evolution functionality of Databricks Auto Loader. Finally, option B uses the "writeStream" method, which is required for the incremental processing of JSON files as they arrive in a source directory. The other options are incorrect because they either omit the required options, use the wrong method, or use the wrong format. References:
* Configure schema inference and evolution in Auto Loader:
https://docs.databricks.com/en/ingestion/auto-loader/schema.html
* Write streaming data:
https://docs.databricks.com/spark/latest/structured-streaming/writing-streaming-data.html

NEW QUESTION # 92
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. Schema inference and evolution on .Databricks ensure that inferred types will always accurately match the data types used by downstream systems.
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 Databricks will infer schema using types that allow all observed data to be processed, setting types manually provides greater assurance of data quality enforcement.
E. Because Delta Lake uses Parquet for data storage, data types can be easily evolved by just modifying file footer information in place.

Answer: D

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 # 93
Define an external SQL table by connecting to a local instance of an SQLite database using JDBC

A. 1.CREATE TABLE users_jdbc
2.USING org.apache.spark.sql.jdbc.sqlite
3.OPTIONS (
4. url = "jdbc:/sqmple_db",
5. dbtable = "users"
6.)
B. 1.CREATE TABLE users_jdbc
2.USING SQLITE
3.OPTIONS (
4. url = "jdbc:/sqmple_db",
5. dbtable = "users"
6.)
C. 1.CREATE TABLE users_jdbc
2.USING SQL
3.URL = {server:"jdbc:/sqmple_db",dbtable: "users"}
D. 1.CREATE TABLE users_jdbc
2.USING SQL
3.OPTIONS (
4. url = "jdbc:sqlite:/sqmple_db",
5. dbtable = "users"
6.)
E. 1.CREATE TABLE users_jdbc
2.USING org.apache.spark.sql.jdbc
3.OPTIONS (
4. url = "jdbc:sqlite:/sqmple_db",
5. dbtable = "users"
6.)

Answer: A

Explanation:
Explanation
The answer is,
1.CREATE TABLE users_jdbc
2.USING org.apache.spark.sql.jdbc
3.OPTIONS (
4. url = "jdbc:sqlite:/sqmple_db",
5. dbtable = "users"
6.)
Databricks runtime currently supports connecting to a few flavors of SQL Database including SQL Server, My SQL, SQL Lite and Snowflake using JDBC.
1.CREATE TABLE <jdbcTable>
2.USING org.apache.spark.sql.jdbc or JDBC
3.OPTIONS (
4. url = "jdbc:<databaseServerType>://<jdbcHostname>:<jdbcPort>",
5. dbtable " = <jdbcDatabase>.atable",
6. user = "<jdbcUsername>",
7. password = "<jdbcPassword>"
8.)
For more detailed documentation
SQL databases using JDBC - Azure Databricks | Microsoft Docs

NEW QUESTION # 94
......

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