Practice Test 3 | Google Cloud Certified Professional Data Engineer | Dumps | Mock Test

Question 13

An online bank system allows its clients to log into their accounts to check their balance, transfer money, enable and disable debit & credit cards, print transaction logs and offers many other online services. As a rule for security measurements, session logs for each client is recorded with events incoming every 10 seconds from the client’s web browser including details about session ID, timestamp, current page, and network IP address. These logs are stored in BigTable for further aggregation and analysis.

The online system should detect if the client is idle for more than 600 seconds. In case of the idle session, the system should automatically log out and the client is obligated to enter his credentials again to log in. In order to detect that the client is idle within the time window of 600 seconds, data in BigTable should be aggregated and transformed accordingly for the server-side system to deactivate all tokens linked to sessions considered idle. You are using Dataflow to build a data pipeline to aggregate the data.

Which time window should be applied for this scenario?

A. Fixed-time window with a duration of 10 minutes.
B. Sliding-time window with a duration of 10 minutes.
C. Per-session window with a time gap of 10 minutes.
D. Single global window with a time-based trigger of 10 minutes.

Answer

Correct Answer: C

A session window function defines windows around the 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 the data that has the same key. Each key in your data collection will, therefore, be grouped into disjoint windows of differing sizes.