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Session 9 Part 2 Quiz (Derived Columns and Time Features)

Question 1

Which method adds or replaces a Spark DataFrame column?

  • makeColumn
  • withColumn
  • add_cell
  • installColumn

Answer: 2 Type: single Time: 40 Explanation: withColumn returns a DataFrame with the new or replaced column.

Question 2

Which expression calculates total_bytes?

  • service + region
  • event_time / service
  • bytes_in + bytes_out
  • latency_ms - service

Answer: 3 Type: single Time: 40 Explanation: Total bytes are incoming bytes plus outgoing bytes.

Question 3

Why does the tutorial calculate error_rate only when request_count > 0?

  • To sort services alphabetically
  • To stop Spark
  • To remove timestamp columns
  • To avoid division by zero

Answer: 4 Type: single Time: 50 Explanation: Safe division prevents errors or invalid results when the denominator is zero.

Question 4

Which function extracts the hour from a timestamp column?

  • hour
  • weekday
  • schema
  • rank

Answer: 1 Type: single Time: 40 Explanation: hour(col("event_time")) extracts the hour value.

Question 5

Which function converts a timestamp into a date?

  • to_folder
  • to_date
  • to_schema
  • to_rank

Answer: 2 Type: single Time: 40 Explanation: to_date extracts the date part from a timestamp.

Question 6

What does when(...).otherwise(...) help create?

  • A permanent database
  • A virtual environment
  • Conditional values such as latency_band
  • A CSV header

Answer: 3 Type: single Time: 50 Explanation: when is used for conditional column logic.

Question 7

Which SQL query finds total requests by hour?

  • SELECT event_hour FROM service_events_enriched STOP BY request_count
  • SUM event_hour USING request_count
  • GROUP request_count FROM event_hour
  • SELECT event_hour, SUM(request_count) FROM service_events_enriched GROUP BY event_hour

Answer: 4 Type: single Time: 60 Explanation: The query groups rows by hour and sums request counts.

Question 8

Which final project feature is most similar to event_hour?

  • symbol
  • trade_hour
  • close
  • price_range

Answer: 2 Type: single Time: 45 Explanation: Both are hour features extracted from timestamp columns.

Question 9

Which final project feature is most similar to latency_band?

  • quote_volume
  • open_time
  • candle_direction
  • trade_count

Answer: 3 Type: single Time: 45 Explanation: Both are category labels created from row-level conditions.

Question 10

Why register service_events_enriched after creating derived columns?

  • So the raw CSV is deleted
  • So Java is installed
  • So Spark no longer needs memory
  • So SQL queries can use the new columns

Answer: 4 Type: single Time: 50 Explanation: Registering the enriched DataFrame lets Spark SQL query the derived columns.