Practice Test 2 | Google Cloud Certified Professional Data Engineer | Dumps | Mock Test

Question 8

A gaming app allows up to 32 players to compete in “battle royale” mode in a single gaming session. Recently, players are sending feedback complaining some users are idle and not competing in the session which breaks the experience for them. The development team decided to end the session for players who are idle for more than 60 seconds to solve this problem.

Gaming app sends events every second contain player’s state (active, idle, pending) and other details. You want to build a Dataflow pipeline which aggregates these events so idle players can be detected in the time frame specified by development team.

Which windowing function you should choose to design the pipeline?

A. Fixed-time window with duration of 60 seconds.
B. Sliding-time window with duration of 60 seconds.
C. Per-session window with time gap duration of 60 seconds.
D. Single global window with time-based trigger of 60 seconds.

Answer

Answer: C.

A session window function defines windows around areas of concentration in the data. Session windowing is useful for data that is irregularly distributed with respect to time; for example, a data stream representing user mouse activity may have long periods of idle time interspersed with high concentrations of clicks. Session windowing groups the high concentrations of data into separate windows and filters out the idle sections of the data stream. Note that session windowing applies on a per-key basis: That is, grouping into sessions only takes into account data that has the same key. Each key in your data collection will therefore be grouped into disjoint windows of differing sizes.