Data Processing Cycle 4

Question 1

In stream processing, a 'tumbling window' cycle processes events in:

Accepted Answer

Non-overlapping fixed-size time windows

Answer

Tumbling windows are fixed-size and non-overlapping; each event belongs to exactly one window, making them simple for aggregation cycles.

Question 2

A 'sliding window' in a streaming data cycle differs from a tumbling window because it:

Accepted Answer

Advances by a step smaller than its size, so consecutive windows share events

Answer

Sliding windows move forward by a step interval smaller than the window size, so events can appear in multiple overlapping windows.

Question 3

Data lineage tracking in a processing cycle is primarily used to:

Accepted Answer

Trace the origin, transformations, and movement of data through the pipeline

Answer

Data lineage records where data came from and every transformation it underwent, enabling impact analysis, debugging, and compliance audits.

Question 4

In a MapReduce cycle, the 'shuffle and sort' phase occurs between:

Accepted Answer

The Map phase and the Reduce phase

Answer

Shuffle and sort transfers intermediate key-value pairs from mappers to the appropriate reducers, grouping all values for each key together.

Question 5

Which data processing cycle pattern is best suited for continuously updating fraud detection scores as transactions arrive?

Accepted Answer

Real-time stream processing

Answer

Real-time stream processing evaluates each transaction as it arrives, enabling immediate fraud detection with minimal latency.

Data Processing Practice Test