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

Explanation

spark.read.json(filePath)

Correct. spark.read accesses Spark's DataFrameReader. Then, Spark identifies the file type to be read as JSON type by passing filePath into the DataFrameReader.json() method.

spark.read.path(filePath)

Incorrect. Spark's DataFrameReader does not have a path method. A universal way to read in files is provided by the DataFrameReader.load() method (link below).

spark.read.path(filePath, source="json")

Wrong. A DataFrameReader.path() method does not exist (see above).

spark.read().json(filePath)

Incorrect. spark.read is a way to access Spark's DataFrameReader. However, the DataFrameReader is not callable, so calling it via spark.read() will fail.

spark.read().path(filePath)

No, Spark's DataFrameReader is not callable (see above).

More info: pyspark.sql.DataFrameReader.json — PySpark 3.1.2 documentation, pyspark.sql.DataFrameReader.load — PySpark 3.1.2 documentation

Static notebook | Dynamic notebook: See test 3, QUESTION NO: 34 (Databricks import instructions)

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.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)