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

Explanation

spark.createDataFrame([("summer", 4.5), ("winter", 7.5)], ["season", "wind_speed_ms"])

Correct. This command uses the Spark Session's createDataFrame method to create a new DataFrame. Notice how rows, columns, and column names are passed in here: The rows are specified

as a Python list. Every entry in the list is a new row. Columns are specified as Python tuples (for example ("summer", 4.5)). Every column is one entry in the tuple.

The column names are specified as the second argument to createDataFrame(). The documentation (link below) shows that "when schema is a list of column names, the type of each column will be

inferred from data" (the first argument). Since values 4.5 and 7.5 are both float variables, Spark will correctly infer the double type for column wind_speed_ms. Given that all values in column

"season" contain only strings, Spark will cast the column appropriately as string.

Find out more about SparkSession.createDataFrame() via the link below.

spark.newDataFrame([("summer", 4.5), ("winter", 7.5)], ["season", "wind_speed_ms"])

No, the SparkSession does not have a newDataFrame method.

from pyspark.sql import types as T

spark.createDataFrame((("summer", 4.5), ("winter", 7.5)), T.StructType([T.StructField("season", T.CharType()), T.StructField("season", T.DoubleType())]))

No. pyspark.sql.types does not have a CharType type. See link below for available data types in Spark.

spark.createDataFrame({"season": ["winter","summer"], "wind_speed_ms": [4.5, 7.5]})

No, this is not correct Spark syntax. If you have considered this option to be correct, you may have some experience with Python's pandas package, in which this would be correct syntax. To create

a Spark DataFrame from a Pandas DataFrame, you can simply use spark.createDataFrame(pandasDf) where pandasDf is the Pandas DataFrame.

Find out more about Spark syntax options using the examples in the documentation for SparkSession.createDataFrame linked below.

spark.DataFrame({"season": ["winter","summer"], "wind_speed_ms": [4.5, 7.5]})

No, the Spark Session (indicated by spark in the code above) does not have a DataFrame method.

More info: pyspark.sql.SparkSession.createDataFrame — PySpark 3.1.1 documentation and Data Types - Spark 3.1.2 Documentation

Static notebook | Dynamic notebook: See test 1, QUESTION NO: 41 (Databricks import instructions)

Explanation

The key to solving this QUESTION NO: is knowing (or reading in the documentation) that, by default, cache() stores values to memory and writes any partitions for which there is insufficient memory

to disk. persist() can achieve the exact same behavior, however not with the StorageLevel.MEMORY_ONLY option listed here. It is also worth noting that cache() does not have any arguments.

If you have troubles finding the storage level information in the documentation, please also see this student Q&A thread that sheds some light here.

Static notebook | Dynamic notebook: See test 2, QUESTION NO: 30 (Databricks import instructions)

Explanation

Correct code block:

def count_to_target(target):

if target is None:

return

result = list(range(target))

return result

count_to_target_udf = udf(count_to_target, ArrayType(IntegerType()))

transactionsDf.select(count_to_target_udf('predError'))

Output of correct code block:

+--------------------------+

|count_to_target(predError)|

+--------------------------+

| [0, 1, 2]|

| [0, 1, 2, 3, 4, 5]|

| [0, 1, 2]|

| null|

| null|

| [0, 1, 2]|

+--------------------------+

This QUESTION NO: is not exactly easy. You need to be familiar with the syntax around UDFs (user-defined functions). Specifically, in this QUESTION NO: it is important to pass the correct types

to the udf

method - returning an array of a specific type rather than just a single type means you need to think harder about type implications than usual.

Remember that in Spark, you always pass types in an instantiated way like ArrayType(IntegerType()), not like ArrayType(IntegerType). The parentheses () are the key here - make sure you do not

forget those.

You should also pay attention that you actually pass the UDF count_to_target_udf, and not the Python method count_to_target to the select() operator.

Finally, null values are always a tricky case with UDFs. So, take care that the code can handle them correctly.

More info: How to Turn Python Functions into PySpark Functions (UDF) – Chang Hsin Lee – Committing my thoughts to words.

Static notebook | Dynamic notebook: See test 3, QUESTION NO: 24 (Databricks import instructions)