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

Static notebook | Dynamic notebook: See test 1, QUESTION NO: 23 (Databricks import instructions) ,

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.join(broadcast(itemsDf), "transactionId", "left_semi")

This QUESTION NO: is extremely difficult and exceeds the difficulty of questions in the exam by far.

A first indication of what is asked from you here is the remark that "the query should be executed in an optimized way". You also have qualitative information about the size of itemsDf and

transactionsDf. Given that itemsDf is "very small" and that the execution should be optimized, you should consider instructing Spark to perform a broadcast join, broadcasting the "very small"

DataFrame itemsDf to all executors. You can explicitly suggest this to Spark via wrapping itemsDf into a broadcast() operator. One answer option does not include this operator, so you can disregard

it. Another answer option wraps the broadcast() operator around transactionsDf - the bigger of the two DataFrames. This answer option does not make sense in the optimization context and can

likewise be disregarded.

When thinking about the broadcast() operator, you may also remember that it is a method of pyspark.sql.functions. One answer option, however, resolves to itemsDf.broadcast([...]). The DataFrame

class has no broadcast() method, so this answer option can be eliminated as well.

All two remaining answer options resolve to transactionsDf.join([...]) in the first 2 gaps, so you will have to figure out the details of the join now. You can pick between an outer and a left semi join. An

outer join would include columns from both DataFrames, where a left semi join only includes columns from the "left" table, here transactionsDf, just as asked for by the question. So, the correct

answer is the one that uses the left_semi join.