Associate-Developer-Apache-Spark Antworten, Associate-Developer-Apache-Spark Prüfungsaufgaben

Associate-Developer-Apache-Spark Antworten, Associate-Developer-Apache-Spark Prüfungsaufgaben


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>> Associate-Developer-Apache-Spark Antworten <<

Associate-Developer-Apache-Spark Prüfungsressourcen: Databricks Certified Associate Developer for Apache Spark 3.0 Exam & Associate-Developer-Apache-Spark Reale Fragen

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Databricks Certified Associate Developer for Apache Spark 3.0 Exam Associate-Developer-Apache-Spark Prüfungsfragen mit Lösungen (Q135-Q140):

135. Frage

Which of the following code blocks returns a one-column DataFrame of all values in column supplier of DataFrame itemsDf that do not contain the letter X? In the DataFrame, every value should only be listed once.

Sample of DataFrame itemsDf:

1.+------+--------------------+--------------------+-------------------+

2.|itemId| itemName| attributes| supplier|

3.+------+--------------------+--------------------+-------------------+

4.| 1|Thick Coat for Wa...|[blue, winter, cozy]|Sports Company Inc.|

5.| 2|Elegant Outdoors ...|[red, summer, fre...| YetiX|

6.| 3| Outdoors Backpack|[green, summer, t...|Sports Company Inc.|

7.+------+--------------------+--------------------+-------------------+

  • A. itemsDf.filter(col(supplier).not_contains('X')).select(supplier).distinct()
  • B. itemsDf.filter(not(col('supplier').contains('X'))).select('supplier').unique()
  • C. itemsDf.select(~col('supplier').contains('X')).distinct()
  • D. itemsDf.filter(!col('supplier').contains('X')).select(col('supplier')).unique()
  • E. itemsDf.filter(~col('supplier').contains('X')).select('supplier').distinct()

Antwort: E

Begründung:

Explanation

Output of correct code block:

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

| supplier|

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

|Sports Company Inc.|

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

Key to managing this question is understand which operator to use to do the opposite of an operation

- the ~ (not) operator. In addition, you should know that there is no unique() method.

Static notebook | Dynamic notebook: See test 1


136. Frage

The code block displayed below contains an error. The code block should return a copy of DataFrame transactionsDf where the name of column transactionId has been changed to transactionNumber. Find the error.

Code block:

transactionsDf.withColumn("transactionNumber", "transactionId")

  • A. Each column name needs to be wrapped in the col() method and method withColumn should be replaced by method withColumnRenamed.
  • B. The arguments to the withColumn method need to be reordered and the copy() operator should be appended to the code block to ensure a copy is returned.
  • C. The copy() operator should be appended to the code block to ensure a copy is returned.
  • D. The method withColumn should be replaced by method withColumnRenamed and the arguments to the method need to be reordered.
  • E. The arguments to the withColumn method need to be reordered.

Antwort: D

Begründung:

Explanation

Correct code block:

transactionsDf.withColumnRenamed("transactionId", "transactionNumber")

Note that in Spark, a copy is returned by default. So, there is no need to append copy() to the code block.

More info: pyspark.sql.DataFrame.withColumnRenamed - PySpark 3.1.2 documentation Static notebook | Dynamic notebook: See test 2


137. Frage

Which of the following code blocks silently writes DataFrame itemsDf in avro format to location fileLocation if a file does not yet exist at that location?

  • A. itemsDf.save.format("avro").mode("ignore").write(fileLocation)
  • B. itemsDf.write.format("avro").mode("ignore").save(fileLocation)
  • C. itemsDf.write.avro(fileLocation)
  • D. spark.DataFrameWriter(itemsDf).format("avro").write(fileLocation)
  • E. itemsDf.write.format("avro").mode("errorifexists").save(fileLocation)

Antwort: C

Begründung:

Explanation

The trick in this question is knowing the "modes" of the DataFrameWriter. Mode ignore will ignore if a file already exists and not replace that file, but also not throw an error. Mode errorifexists will throw an error, and is the default mode of the DataFrameWriter. The question NO:

explicitly calls for the DataFrame to be "silently" written if it does not exist, so you need to specify mode("ignore") here to avoid having Spark communicate any error to you if the file already exists.

The `overwrite' mode would not be right here, since, although it would be silent, it would overwrite the already-existing file. This is not what the question asks for.

It is worth noting that the option starting with spark.DataFrameWriter(itemsDf) cannot work, since spark references the SparkSession object, but that object does not provide the DataFrameWriter.

As you can see in the documentation (below), DataFrameWriter is part of PySpark's SQL API, but not of its SparkSession API.

More info:

DataFrameWriter: pyspark.sql.DataFrameWriter.save - PySpark 3.1.1 documentation SparkSession API: Spark SQL - PySpark 3.1.1 documentation Static notebook | Dynamic notebook: See test 1


138. Frage

The code block displayed below contains an error. The code block should return a new DataFrame that only contains rows from DataFrame transactionsDf in which the value in column predError is at least 5. Find the error.

Code block:

transactionsDf.where("col(predError) >= 5")

  • A. Instead of >=, the SQL operator GEQ should be used.
  • B. The argument to the where method should be "predError >= 5".
  • C. The expression returns the original DataFrame transactionsDf and not a new DataFrame. To avoid this, the code block should be transactionsDf.toNewDataFrame().where("col(predError) >= 5").
  • D. The argument to the where method cannot be a string.
  • E. Instead of where(), filter() should be used.

Antwort: B

Begründung:

Explanation

The argument to the where method cannot be a string.

It can be a string, no problem here.

Instead of where(), filter() should be used.

No, that does not matter. In PySpark, where() and filter() are equivalent.

Instead of >=, the SQL operator GEQ should be used.

Incorrect.

The expression returns the original DataFrame transactionsDf and not a new DataFrame. To avoid this, the code block should be transactionsDf.toNewDataFrame().where("col(predError) >= 5").

No, Spark returns a new DataFrame.

Static notebook | Dynamic notebook: See test 1

(https://flrs.github.io/spark_practice_tests_code/#1/27.html ,

https://bit.ly/sparkpracticeexams_import_instructions)


139. Frage

The code block displayed below contains an error. The code block should save DataFrame transactionsDf at path path as a parquet file, appending to any existing parquet file. Find the error.

Code block:

  • A. The code block is missing a bucketBy command that takes care of partitions.
  • B. The mode option should be omitted so that the command uses the default mode.
  • C. Given that the DataFrame should be saved as parquet file, path is being passed to the wrong method.
  • D. save() is evaluated lazily and needs to be followed by an action.
  • E. The code block is missing a reference to the DataFrameWriter.
  • F. transactionsDf.format("parquet").option("mode", "append").save(path)

Antwort: E

Begründung:

Explanation

Correct code block:

transactionsDf.write.format("parquet").option("mode", "append").save(path)


140. Frage

......

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