Free CERTIFIED-ASSOCIATE-DEVELOPER-FOR-APACHE-SPARK – Databricks CERTIFIED-ASSOCIATE-DEVELOPER-FOR-APACHE-SPARK Practice Test Question

Name: Databricks CERTIFIED-ASSOCIATE-DEVELOPER-FOR-APACHE-SPARK Free Actual Questions Answers
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Showing 1–3 of 23 questions

Question 1 (Topic 1)

Which of the following DataFrame operations is classified as an action?

Select an option, then click Submit answer.

○ DataFrame.drop()
○ DataFrame.coalesce()
○ DataFrame.take()
○ DataFrame.join()
○ DataFrame.filter()

Question 2 (Topic 1)

Which of the following operations can be used to create a DataFrame with a subset of columns from DataFrame storesDF that are specified by name?

Select an option, then click Submit answer.

○ storesDF.subset()
○ storesDF.select()
○ storesDF.selectColumn()
○ storesDF.filter()
○ storesDF.drop()

Reference / correct answer:

storesDF.select()

Most accepted answer: B. storesDF.select()

Community votes: B=2

Selected Answer: B The operation that can be used to create a DataFrame with a subset of columns from DataFrame storesDF that are specified by name is storesDF.select(). The select() operation allows you to specify the columns you want to keep in the resulting DataFrame by passing in the column names as arguments. For example, to create a new DataFrame that contains only the columns store_id and store_name from the storesDF DataFrame, you can use the following code: newDF = storesDF.select("store_id", "store_name") upvoted 6 times

E.storesDF.drop() is also correct. It is just opposite of select. If you have a large number of columns you need to select but a few to drop to meet your requirements, then drop is easier than select. upvoted 1 times

Selected Answer: B The select() operation in Spark DataFrame allows you to specify the columns you want to include in the resulting DataFrame. You can provide column names as arguments to the select() operation to create a new DataFrame with only the specified columns. upvoted 2 times

Question 3 (Topic 1)

The code block shown below contains an error. The code block is intended to return a new DataFrame with the mean of column sqft from DataFrame storesDF in column sqftMean. Identify the error. Code block: storesDF.agg(mean("sqft").alias("sqftMean"))

Select an option, then click Submit answer.

○ The argument to the mean() operation should be a Column abject rather than a string column name.
○ The argument to the mean() operation should not be quoted.
○ The mean() operation is not a standalone function – it’s a method of the Column object.
○ The agg() operation is not appropriate here – the withColumn() operation should be used instead.
○ The only way to compute a mean of a column is with the mean() method from a DataFrame.

Reference / correct answer:

The argument to the mean() operation should be a Column abject rather than a string column name.

Most accepted answer: A. The argument to the mean() operation should be a Column abject rather than a string column name.

Community votes: A=5, D=1, E=1

Selected Answer: E The code block shown is correct and should return a new DataFrame with the mean of column sqft from DataFrame storesDF in column sqftMean. Therefore, the answer is E - none of the options identify a valid error in the code block. Here's an explanation for each option: A. The argument to the mean() operation can be either a Column object or a string column name, so there is no error in using a string column name in this case. E. This option is incorrect because the code block shown is a valid way to compute the mean of a column using PySpark. Another way to compute the mean of a column is with the mean() method from a DataFrame, but that doesn't mean the code block shown is invalid. upvoted 7 times newusername 2 years, 8 months ago wrong! A upvoted 3 times ...

wrong! A upvoted 3 times

Selected Answer: A The function mean() is part of pyspark.sql.functions, and it expects a Column object, not a string. upvoted 1 times

The mean() function expects a Column object as an argument, which can be created using col("sqft"). Simply passing the column name as a string will result in an error. upvoted 2 times

The correct answer is A. The argument to the mean() operation should be a Column object rather than a string column name. In Spark DataFrames, the mean() function takes a Column object as its argument, not a string column name. To create a Column object from a string column name, you can use the col() function. upvoted 1 times

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