Databricks-Certified-Associate-Developer-for-Apache-Spark-3.0 Exam Questions - Navigate Your Path to Success

Correct : D

itemsDf.withColumnRenamed('attributes', 'feature0').withColumnRenamed('supplier', 'feature1')

Correct! Spark's DataFrame.withColumnRenamed syntax makes it relatively easy to change the name of a column.

itemsDf.withColumnRenamed(attributes, feature0).withColumnRenamed(supplier, feature1)

Incorrect. In this code block, the Python interpreter will try to use attributes and the other column names as variables. Needless to say, they are undefined, and as a result the block will not run.

itemsDf.withColumnRenamed(col('attributes'), col('feature0'), col('supplier'), col('feature1'))

Wrong. The DataFrame.withColumnRenamed() operator takes exactly two string arguments. So, in this answer both using col() and using four arguments is wrong.

itemsDf.withColumnRenamed('attributes', 'feature0')

itemsDf.withColumnRenamed('supplier', 'feature1')

No. In this answer, the returned DataFrame will only have column supplier be renamed, since the result of the first line is not written back to itemsDf.

itemsDf.withColumn('attributes', 'feature0').withColumn('supplier', 'feature1')

Incorrect. While withColumn works for adding and naming new columns, you cannot use it to rename existing columns.

More info: pyspark.sql.DataFrame.withColumnRenamed --- PySpark 3.1.2 documentation

Static notebook | Dynamic notebook: See test 3, Question: 29 (Databricks import instructions)

Correct : B

Correct code block: transactionsDf.drop('productId', 'f')

This Question: requires a lot of thinking to get right. For solving it, you may take advantage of the digital notepad that is provided to you during the test. You have probably seen that the code

block

includes multiple errors. In the test, you are usually confronted with a code block that only contains a single error. However, since you are practicing here, this challenging multi-error QUESTION

NO: will

make it easier for you to deal with single-error questions in the real exam.

The select operator should be replaced by a drop operator, the column names should be listed directly as arguments to the operator and not as a list, and all column names should be expressed as

strings without being wrapped in a col() operator.

Correct! Here, you need to figure out the many, many things that are wrong with the initial code block. While the Question: can be solved by using a select statement, a drop statement, given

the

answer options, is the correct one. Then, you can read in the documentation that drop does not take a list as an argument, but just the column names that should be dropped. Finally, the column

names should be expressed as strings and not as Python variable names as in the original code block.

The column names should be listed directly as arguments to the operator and not as a list.

Incorrect. While this is a good first step and part of the correct solution (see above), this modification is insufficient to solve the question.

The column names should be listed directly as arguments to the operator and not as a list and following the pattern of how column names are expressed in the code block, columns productId and f

should be replaced by transactionId, predError, value and storeId.

Wrong. If you use the same pattern as in the original code block (col(productId), col(f)), you are still making a mistake. col(productId) will trigger Python to search for the content of a variable named

productId instead of telling Spark to use the column productId - for that, you need to express it as a string.

The select operator should be replaced by a drop operator, the column names should be listed directly as arguments to the operator and not as a list, and all col() operators should be removed.

No. This still leaves you with Python trying to interpret the column names as Python variables (see above).

The select operator should be replaced by a drop operator.

Wrong, this is not enough to solve the question. If you do this, you will still face problems since you are passing a Python list to drop and the column names are still interpreted as Python variables

(see above).

More info: pyspark.sql.DataFrame.drop --- PySpark 3.1.2 documentation

Static notebook | Dynamic notebook: See test 3, Question: 30 (Databricks import instructions)