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

Explanation

Correct code block:

transactionsDf.select((col("storeId").between(20, 30)) & (col("productId")==2))

Although this QUESTION NO: may make you think that it asks for a filter or where statement, it does not. It asks explicity to return a column with booleans – this should point you to the select

statement.

Another trick here is the rarely used between() method. It exists and resolves to ((storeId >= 20) AND (storeId <= 30)) in SQL. geq() and leq() do not exist.

Another riddle here is how to chain the two conditions. The only valid answer here is &. Operators like && or and are not valid. Other boolean operators that would be valid in Spark are | and.

Static notebook | Dynamic notebook: See test 1, QUESTION NO: 42 (Databricks import instructions)

Explanation

Correct code block:

transactionsDf.write.format("parquet").mode("overwrite").option("compression", "snappy").save(storeDir)

Solving this QUESTION NO: requires you to know how to access the DataFrameWriter (link below) from the DataFrame API - through DataFrame.write.

Another nuance here is about knowing the different modes available for writing parquet files that determine Spark's behavior when dealing with existing files. These, together with the compression

options are explained in the DataFrameWriter.parquet documentation linked below.

Finally, bracket __5__ poses a certain challenge. You need to know which command you can use to pass down the file path to the DataFrameWriter. Both save and parquet are valid options here.

More info:

- DataFrame.write: pyspark.sql.DataFrame.write — PySpark 3.1.1 documentation

- DataFrameWriter.parquet: pyspark.sql.DataFrameWriter.parquet — PySpark 3.1.1 documentation

Static notebook | Dynamic notebook: See test 1, QUESTION NO: 58 (Databricks import instructions)

Explanation

Correct code block:

transactionsDf.filter(col('predError').isin([3, 6])).count()

The isin method is the correct one to use here – the in method does not exist for the Column object.

More info: pyspark.sql.Column.isin — PySpark 3.1.2 documentation