Databricks-Certified-Associate-Developer-for-Apache-Spark-3.0 Exam Dumps : Databricks Certified Associate Developer for Apache Spark 3.0 Exam

Explanation

The key here is to understand that columns that are passed to DataFrame.drop() are ignored if they do not exist in the DataFrame. So, passing column name associateId to transactionsDf.drop()

does not have any effect.

Passing a list to transactionsDf.drop() is not valid. The documentation (link below) shows the call structure as DataFrame.drop(*cols). The * means that all arguments that are passed to

DataFrame.drop() are read as columns. However, since a list of columns, for example ["predError", "productId", "associateId"] is not a column, Spark will run into an error.

More info: pyspark.sql.DataFrame.drop — PySpark 3.1.1 documentation

Static notebook | Dynamic notebook: See test 1, QUESTION NO: 50 (Databricks import instructions)

Explanation

Correct code block:

transactionsDf.select("storeId").printSchema()

The difficulty of this QUESTION NO: is that it is hard to solve with the stepwise first-to-last-gap approach that has worked well for similar questions, since the answer options are so different from

one

another. Instead, you might want to eliminate answers by looking for patterns of frequently wrong answers.

A first pattern that you may recognize by now is that column names are not expressed in quotes. For this reason, the answer that includes storeId should be eliminated.

By now, you may have understood that the DataFrame.limit() is useful for returning a specified amount of rows. It has nothing to do with specific columns. For this reason, the answer that resolves to

limit("storeId") can be eliminated.

Given that we are interested in information about the data type, you should QUESTION NO: whether the answer that resolves to limit(1).columns provides you with this information. While

DataFrame.columns is a valid call, it will only report back column names, but not column types. So, you can eliminate this option.

The two remaining options either use the printSchema() or print_schema() command. You may remember that DataFrame.printSchema() is the only valid command of the two. The select("storeId")

part just returns the storeId column of transactionsDf - this works here, since we are only interested in that column's type anyways.

More info: pyspark.sql.DataFrame.printSchema — PySpark 3.1.2 documentation

Static notebook | Dynamic notebook: See test 3, QUESTION NO: 57 (Databricks import instructions)

Explanation

Correct code block:

itemsDf.filter(col('supplier').contains('et'))

A mixup can easily happen here between isin and contains. Since we want to check whether a column "contains" the values et, this is the operator we should use here. Note that both methods are

methods of Spark's Column object. See below for documentation links.

A specific Column object can be accessed through the col() method and not the Column() method or through col[], which is an essential thing to know here. In PySpark, Column references a generic

column object. To use it for queries, you need to link the generic column object to a specific DataFrame. This can be achieved, for example, through the col() method.

More info:

- isin documentation: pyspark.sql.Column.isin — PySpark 3.1.1 documentation

- contains documentation: pyspark.sql.Column.contains — PySpark 3.1.1 documentation

Static notebook | Dynamic notebook: See test 1, QUESTION NO: 51 (Databricks import instructions)