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Session 8 Part 1 Quiz (PySpark in Colab)

Question 1

What is PySpark?

  • A Python API for Apache Spark
  • A package for editing images
  • A local-only spreadsheet program
  • A JavaScript web framework

Answer: 1 Type: single Time: 40 Explanation: PySpark lets Python code use Apache Spark.

Question 2

Why are we using Google Colab in Part 1?

  • So students can run PySpark without local Spark installation
  • So SQL is disabled
  • So Python files cannot be created
  • So Spark runs only on phones

Answer: 1 Type: single Time: 45 Explanation: Colab provides a temporary environment where PySpark can be installed and practiced quickly.

Question 3

Which object is the main entry point for PySpark work in this session?

  • SparkSession
  • DataFolder
  • RequestLock
  • QuizRunner

Answer: 1 Type: single Time: 40 Explanation: SparkSession is used to create DataFrames and run Spark operations.

Question 4

Which command creates a Spark DataFrame from Python rows and column names?

  • spark.createDataFrame(rows, columns)
  • spark.readPython(rows, columns)
  • DataFrame.open(rows, columns)
  • spark.make_csv(rows, columns)

Answer: 1 Type: single Time: 45 Explanation: createDataFrame creates a Spark DataFrame from local Python data.

Question 5

What does df.printSchema() show?

  • The notebook title
  • Column names and data types
  • The current Git branch
  • Only the first row

Answer: 2 Type: single Time: 45 Explanation: The schema describes the DataFrame columns and their types.

Question 6

Which operation filters students with scores of at least 90?

  • df.filter(df["score"] >= 90)
  • df.select("score" >= 90)
  • df.groupBy("score" >= 90)
  • df.stop(90)

Answer: 1 Type: single Time: 50 Explanation: filter keeps rows that match the condition.

Question 7

What does withColumn do?

  • Adds or replaces a column
  • Deletes the whole DataFrame
  • Installs PySpark
  • Opens Google Colab

Answer: 1 Type: single Time: 45 Explanation: withColumn returns a DataFrame with a new or replaced column.

Question 8

Which function is used in Part 1 to calculate average score by track?

  • avg
  • install
  • schema
  • notebook

Answer: 1 Type: single Time: 40 Explanation: avg is an aggregate function imported from pyspark.sql.functions.

Question 9

Why should we call spark.stop() at the end?

  • To release the Spark session resources
  • To delete the notebook
  • To install Java
  • To convert Spark code into pandas automatically

Answer: 1 Type: single Time: 45 Explanation: Stopping Spark releases resources used by the Spark session.

Question 10

Which statement best describes Spark local mode in this session?

  • Spark runs on the current machine for practice
  • Spark always uses a remote production cluster
  • Spark cannot run DataFrames
  • Spark only runs SQL and no Python

Answer: 1 Type: single Time: 45 Explanation: local[*] runs Spark locally, which is useful for learning.