Databricks Databricks-Certified-Data-Engineer-Associate exam practice questions and answers

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The GAQM Databricks-Certified-Data-Engineer-Associate (Databricks Certified Data Engineer Associate) exam is a certification program designed for individuals who wish to demonstrate their expertise in designing and implementing big data solutions using Databricks. Databricks-Certified-Data-Engineer-Associate exam is intended for data engineers, data analysts, and big data architects who want to validate their skills in building and managing large-scale data processing systems using Databricks.

Databricks Certified Data Engineer Associate certification is a valuable credential for data engineers who want to demonstrate their expertise in working with Databricks. Databricks Certified Data Engineer Associate Exam certification is recognized by leading organizations and can help data engineers to advance their careers. Databricks Certified Data Engineer Associate Exam certification is also a great way for data engineers to demonstrate their commitment to ongoing professional development and to stay up to date with the latest trends and technologies in the field.

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The GAQM Databricks-Certified-Data-Engineer-Associate (Databricks Certified Data Engineer Associate) Certification Exam is a comprehensive and rigorous examination that validates a candidate's understanding of the Databricks platform and its role in data engineering. Databricks Certified Data Engineer Associate Exam certification focuses on assessing the practical skills and knowledge required to design, build, and maintain data pipelines using Databricks.

Databricks Certified Data Engineer Associate Exam Sample Questions (Q33-Q38):

NEW QUESTION # 33
A data engineer has a Python notebook in Databricks, but they need to use SQL to accomplish a specific task within a cell. They still want all of the other cells to use Python without making any changes to those cells.
Which of the following describes how the data engineer can use SQL within a cell of their Python notebook?

Answer: C

Explanation:
In Databricks, you can use different languages within the same notebook by using magic commands. Magic commands are special commands that start with a percentage sign (%) and allow you to change the behavior of the cell. To use SQL within a cell of a Python notebook, you can add %sql to the first line of the cell. This will tell Databricks to interpret the rest of the cell as SQL code and execute it against the default database.
You can also specify a different database by using the USE statement. The result of the SQL query will be displayed as a table or a chart, depending on the output mode. You can also assign the result to a Python variable by using the -o option. For example, %sql -o df SELECT * FROM my_table will run the SQL query and store the result as a pandas DataFrame in the Python variable df. Option A is incorrect, as it is possible to use SQL in a Python notebook using magic commands. Option B is incorrect, as attaching the cell to a SQL endpoint is not necessary and will not change the language of the cell. Option C is incorrect, as simply writing SQL syntax in the cell will result in a syntax error, as the cell will still be interpreted as Python code. Option E is incorrect, as changing the default language of the notebook to SQL will affect all the cells, not just one. References: Use SQL in Notebooks - Knowledge Base - Noteable, [SQL magic commands - Databricks],
[Databricks SQL Guide - Databricks]


NEW QUESTION # 34
A data engineer is using the following code block as part of a batch ingestion pipeline to read from a composable table:

Which of the following changes needs to be made so this code block will work when the transactions table is a stream source?

Answer: E

Explanation:
1: To read from a stream source, the data engineer needs to use the spark.readStream method instead of the spark.read method. The spark.readStream method returns a DataStreamReader object that can be used to specify the details of the input source, such as the format, the schema, the path, and the options. The spark.read method is only suitable for batch processing, not streaming processing. The other changes are not necessary or correct for reading from a stream source. Reference: Structured Streaming Programming Guide, Read a stream, Databricks Data Sources


NEW QUESTION # 35
Which of the following Structured Streaming queries is performing a hop from a Silver table to a Gold table?

Answer: D


NEW QUESTION # 36
A data engineer needs to use a Delta table as part of a data pipeline, but they do not know if they have the appropriate permissions.
In which location can the data engineer review their permissions on the table?

Answer: D


NEW QUESTION # 37
A data engineer has a Python variable table_name that they would like to use in a SQL query. They want to construct a Python code block that will run the query using table_name.
They have the following incomplete code block:
____(f"SELECT customer_id, spend FROM {table_name}")
Which of the following can be used to fill in the blank to successfully complete the task?

Answer: A

Explanation:
The spark.sql method can be used to execute SQL queries programmatically and return the result as a DataFrame. The spark.sql method accepts a string argument that contains a valid SQL statement. The data engineer can use a formatted string literal (f-string) to insert the Python variable table_name into the SQL query. The other methods are either invalid or not suitable for running SQL queries. References: Running SQL Queries Programmatically, Formatted string literals, spark.sql


NEW QUESTION # 38
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