DATABRICKS-CERTIFIED-PROFESSIONAL-DATA-ENGINEER LATEST EXAM PDF | DATABRICKS-CERTIFIED-PROFESSIONAL-DATA-ENGINEER LATEST TEST CRAM

Databricks-Certified-Professional-Data-Engineer Latest Exam Pdf | Databricks-Certified-Professional-Data-Engineer Latest Test Cram

Databricks-Certified-Professional-Data-Engineer Latest Exam Pdf | Databricks-Certified-Professional-Data-Engineer Latest Test Cram

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Databricks Certified Professional Data Engineer exam is a rigorous certification exam that requires extensive knowledge and experience in data engineering. Candidates must have a deep understanding of data engineering concepts, such as data modeling, data warehousing, ETL, data governance, and data security. Additionally, they must have experience working with Databricks tools and technologies, such as Apache Spark, Delta Lake, and MLflow. Passing Databricks-Certified-Professional-Data-Engineer Exam demonstrates that the candidate has the skills and knowledge needed to build and optimize data pipelines on the Databricks platform.

Databricks-Certified-Professional-Data-Engineer certification exam is a valuable credential for data engineers who work with Databricks. Databricks Certified Professional Data Engineer Exam certification demonstrates the candidate's expertise in Databricks technology and data engineering concepts. Databricks Certified Professional Data Engineer Exam certification also demonstrates the candidate's commitment to professional development and continuous learning.

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Databricks-Certified-Professional-Data-Engineer exam is a comprehensive assessment that evaluates a candidate's ability to design, implement, and manage data pipelines, as well as leverage advanced analytics and machine learning techniques on the Databricks platform. Databricks-Certified-Professional-Data-Engineer Exam consists of multiple-choice questions and requires candidates to complete a hands-on project that demonstrates their ability to build a data solution on the Databricks platform.

Databricks Certified Professional Data Engineer Exam Sample Questions (Q119-Q124):

NEW QUESTION # 119
A data engineer wants to reflector the following DLT code, which includes multiple definition with very similar code:

In an attempt to programmatically create these tables using a parameterized table definition, the data engineer writes the following code.

The pipeline runs an update with this refactored code, but generates a different DAG showing incorrect configuration values for tables.
How can the data engineer fix this?

  • A. Wrap the loop inside another table definition, using generalized names and properties to replace with those from the inner table
  • B. Convert the list of configuration values to a dictionary of table settings, using table names as keys.
  • C. Convert the list of configuration values to a dictionary of table settings, using different input the for loop.
  • D. Load the configuration values for these tables from a separate file, located at a path provided by a pipeline parameter.

Answer: B

Explanation:
The issue with the refactored code is that it tries to use string interpolation to dynamically create table names within the dlc.table decorator, which will not correctly interpret the table names. Instead, by using a dictionary with table names as keys and their configurations as values, the data engineer can iterate over the dictionary items and use the keys (table names) to properly configure the table settings. This way, the decorator can correctly recognize each table name, and the corresponding configuration settings can be applied appropriately.


NEW QUESTION # 120
You are currently looking at a table that contains data from an e-commerce platform, each row contains a list of items(Item number) that were present in the cart, when the customer makes a change to the cart the entire information is saved as a separate list and appended to an existing list for the duration of the customer session, to identify all the items customer bought you have to make a unique list of items, you were asked to create a unique item's list that was added to the cart by the user, fill in the blanks of below query by choosing the appropriate higher-order function?
Note: See below sample data and expected output.
Schema: cartId INT, items Array<INT>

Fill in the blanks:
Fill in the blanks:
SELECT cartId, _(_(items)) FROM carts

  • A. ARRAY_DISTINCT, FLATTEN
  • B. ARRAY_DISTINCT, ARRAY_UNION
  • C. FLATTEN, ARRAY_DISTINCT
  • D. ARRAY_UNION, ARRAY_DISCINT
  • E. ARRAY_DISTINCT, ARRAY_FLATTEN

Answer: A

Explanation:
Explanation
FLATTEN -> Transforms an array of arrays into a single array.
ARRAY_DISTINCT -> The function returns an array of the same type as the input argument where all duplicate values have been removed.
Table Description automatically generated


NEW QUESTION # 121
Which of the following programming languages can be used to build a Databricks SQL dashboard?

  • A. All of the above
  • B. SQL
  • C. Scala
  • D. Python
  • E. R

Answer: B


NEW QUESTION # 122
The sales team has asked the Data engineering team to develop a dashboard that shows sales per-formance for all stores, but the sales team would like to use the dashboard but would like to select individual store location, which of the following approaches Data Engineering team can use to build this functionality into the dashboard.

  • A. Use SQL UDF function to filter the data based on the location
  • B. Use Databricks REST API to create a dashboard for each location
  • C. Use Dynamic views to filter the data based on the location
  • D. Currently dashboards do not support parameters
  • E. Use query Parameters which then allow user to choose any location

Answer: E

Explanation:
Explanation
The answer is
Databricks supports many types of parameters in the dashboard, a drop-down list can be created based on a query that has a unique list of store locations.
Here is a simple query that takes a parameter for
SELECT * FROM sales WHERE field IN ( {{ Multi Select Parameter }} )
Or
SELECT * FROM sales WHERE field = {{ Single Select Parameter }}
Query parameter types
*Text
*Number
*Dropdown List
*Query Based Dropdown List
*Date and Time


NEW QUESTION # 123
Which is a key benefit of an end-to-end test?

  • A. It closely simulates real world usage of your application.
  • B. It makes it easier to automate your test suite
  • C. It pinpoint errors in the building blocks of your application.
  • D. It provides testing coverage for all code paths and branches.

Answer: A

Explanation:
End-to-end testing is a methodology used to test whether the flow of an application, from start to finish, behaves as expected. The key benefit of an end-to-end test is that it closely simulates real-world, user behavior, ensuring that the system as a whole operates correctly.
Reference:
Software Testing: End-to-End Testing


NEW QUESTION # 124
......

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