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Sharpen Your Knowledge with Databricks (Databricks Certified Associate Developer for Apache Spark 3.0) Certification Sample Questions
CertsTime has provided you with a sample question set to elevate your knowledge about the Databricks Certified Associate Developer for Apache Spark 3.0 exam. With these updated sample questions, you can become quite familiar with the difficulty level and format of the real Databricks Certified Associate Developer for Apache Spark 3.0 certification test. Try our sample Databricks Certified Associate Developer for Apache Spark 3.0 certification practice exam to get a feel for the real exam environment. Our sample practice exam gives you a sense of reality and an idea of the questions on the actual Databricks Apache Spark Associate Developer certification exam.
Our sample questions are similar to the Real Databricks Certified Associate Developer for Apache Spark 3.0 exam questions. The premium Databricks Certified Associate Developer for Apache Spark 3.0 certification practice exam gives you a golden opportunity to evaluate and strengthen your preparation with real-time scenario-based questions. Plus, by practicing real-time scenario-based questions, you will run into a variety of challenges that will push you to enhance your knowledge and skills.
Databricks Certified Associate Developer for Apache Spark 3.0 Sample Questions:
Which of the following code blocks returns the number of unique values in column storeId of DataFrame transactionsDf?
The code block shown below should return an exact copy of DataFrame transactionsDf that does not include rows in which values in column storeId have the value 25. Choose the answer that
correctly fills the blanks in the code block to accomplish this.
The code block shown below should return a two-column DataFrame with columns transactionId and supplier, with combined information from DataFrames itemsDf and transactionsDf. The code
block should merge rows in which column productId of DataFrame transactionsDf matches the value of column itemId in DataFrame itemsDf, but only where column storeId of DataFrame
transactionsDf does not match column itemId of DataFrame itemsDf. Choose the answer that correctly fills the blanks in the code block to accomplish this.
Code block:
transactionsDf.__1__(itemsDf, __2__).__3__(__4__)
Which of the following code blocks displays various aggregated statistics of all columns in DataFrame transactionsDf, including the standard deviation and minimum of values in each column?
The code block displayed below contains an error. The code block should merge the rows of DataFrames transactionsDfMonday and transactionsDfTuesday into a new DataFrame, matching
column names and inserting null values where column names do not appear in both DataFrames. Find the error.
Sample of DataFrame transactionsDfMonday:
1. +-------------+---------+-----+-------+---------+----+
2. |transactionId|predError|value|storeId|productId| f|
3. +-------------+---------+-----+-------+---------+----+
4. | 5| null| null| null| 2|null|
5. | 6| 3| 2| 25| 2|null|
6. +-------------+---------+-----+-------+---------+----+
Sample of DataFrame transactionsDfTuesday:
1. +-------+-------------+---------+-----+
2. |storeId|transactionId|productId|value|
3. +-------+-------------+---------+-----+
4. | 25| 1| 1| 4|
5. | 2| 2| 2| 7|
6. | 3| 4| 2| null|
7. | null| 5| 2| null|
8. +-------+-------------+---------+-----+
Code block:
sc.union([transactionsDfMonday, transactionsDfTuesday])
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