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

While only one of these code blocks works, the DataFrame API is pretty flexible when it comes to accepting columns into the pow() method. The following code blocks would also work:

transactionsDf.withColumn("predErrorSquared", pow("predError", 2))

transactionsDf.withColumn("predErrorSquared", pow("predError", lit(2)))

Static notebook | Dynamic notebook: See test 1, QUESTION NO: 26 (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

Output of correct code block:

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

|itemName |col |

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

|Thick Coat for Walking in the Snow|blue |

|Thick Coat for Walking in the Snow|winter|

|Thick Coat for Walking in the Snow|cozy |

|Outdoors Backpack |green |

|Outdoors Backpack |summer|

|Outdoors Backpack |travel|

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

The key to solving this QUESTION NO: is knowing about Spark's explode operator. Using this operator, you can extract values from arrays into single rows. The following guidance steps through

the

answers systematically from the first to the last gap. Note that there are many ways to solving the gap questions and filtering out wrong answers, you do not always have to start filtering out from the

first gap, but can also exclude some answers based on obvious problems you see with them.

The answers to the first gap present you with two options: filter and where. These two are actually synonyms in PySpark, so using either of those is fine. The answer options to this gap therefore do

not help us in selecting the right answer.

The second gap is more interesting. One answer option includes "Sports".isin(col("Supplier")). This construct does not work, since Python's string does not have an isin method. Another option

contains col(supplier). Here, Python will try to interpret supplier as a variable. We have not set this variable, so this is not a viable answer. Then, you are left with answers options that include col

("supplier").contains("Sports") and col("supplier").isin("Sports"). The QUESTION NO: states that we are looking for suppliers whose name includes Sports, so we have to go for the contains operator

here.

We would use the isin operator if we wanted to filter out for supplier names that match any entries in a list of supplier names.

Finally, we are left with two answers that fill the third gap both with "itemName" and the fourth gap either with explode("attributes") or "attributes". While both are correct Spark syntax, only explode

("attributes") will help us achieve our goal. Specifically, the QUESTION NO: asks for one attribute from column attributes per row - this is what the explode() operator does.

One answer option also includes array_explode() which is not a valid operator in PySpark.

More info: pyspark.sql.functions.explode — PySpark 3.1.2 documentation

Static notebook | Dynamic notebook: See test 3, QUESTION NO: 39 (Databricks import instructions)