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.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:

spark.createDataFrame([("red",), ("blue",), ("green",)], ["color"])

The createDataFrame syntax is not exactly straightforward, but luckily the documentation (linked below) provides several examples on how to use it. It also shows an example very similar to the

code block presented here which should help you answer this QUESTION NO: correctly.

More info: pyspark.sql.SparkSession.createDataFrame — PySpark 3.1.2 documentation

Static notebook | Dynamic notebook: See test 2, QUESTION NO: 23 (Databricks import instructions)

Explanation

Static notebook | Dynamic notebook: See test 1, QUESTION NO: 56 (Databricks import instructions)