Web analyzer application
BDPC · 30 questions
- A city wants a web-analyzer application that shows how residents use the municipal portal. Which end-to-end purpose best describes that architecture?
- Which primary input typically feeds a municipal portal web-analyzer application?
- Engineers designing collection for a civic web analyzer debate how to capture portal behavior. Which technology options fit that collection layer?
- Raw portal events arrive faster than daily aggregates can be finalized. Where should those raw events typically land first in a web-analyzer architecture?
- The civic web analyzer needs both accurate daily visitor sessions and near-real-time live counters. Which processing approach aligns batch and stream for that goal?
- A city UX team needs top pages, conversion funnels, and portal error rates without querying the raw clickstream lake each morning. Which serving choice best fits a municipal web analyzer?
- Before a municipal portal analyzer can compare renewals across departments, what application-code concern must be settled first?
- A library portal records many page hits from the same anonymous visitor. How should the web analyzer turn those hits into usable visit units?
- A parks department dashboard shows huge overnight 'traffic' that never leads to permit completions. What web-analyzer practice keeps civic UX metrics meaningful?
- Permit staff notice many residents start an online application but never finish. Which web-analyzer outcome best informs a redesign?
- A city legal notice limits unnecessary personal data in portal telemetry. What privacy practice should the web analyzer follow?
- Portal ops need immediate notice of error spikes, while content editors only need yesterday's popular pages. How should latency be matched inside the analyzer architecture?
- A civic portal must feed both Spark jobs and a hot counter store from the same page events. Which ingestion pattern fits the web analyzer?
- Holiday week hits a city portal with huge traffic, many event types, and broken tags on some pages. How do the 4 V’s map to that web-analytics situation?
- A digital services office wants evidence before choosing between two portal layouts for license renewal. What should the analyzer support?
- Residents abandon 311 web forms when pages freeze or scripts crash. Beyond marketing popularity, what should the analyzer track?
- Parks, libraries, and the main city site all need analytics without mixing their data incorrectly. What architecture approach fits?
- A transit mobile app shows screens that behave like portal pages for analytics purposes. How should the city reuse its web-analyzer patterns?
- Marketing credits a 'renew license' campaign using last-click only. What analytical caution should the web analyzer’s consumers remember?
- Storage costs climb if every raw portal hit is kept forever at full fidelity. Which retention design fits a civic web analyzer?
- Analytics beacons on a city portal accept POSTed events from browsers. What security concern must designers address?
- Service desks wonder whether busy portal days predict long in-person counters. What cross-channel idea fits a privacy-aware web analyzer?
- Leadership celebrates the most-viewed portal page, yet few residents complete the related service. What metric lesson should the analyzer emphasize?
- Marketing keeps adding campaign tags to portal events without a rigid relational migration each time. Which lake-friendly approach supports that evolution?
- Executives want a weekly view of how residents adopt digital city services. What serving output should the web analyzer provide?
- Traffic on a licensing portal suddenly collapses or surges far beyond normal. What should anomaly alerting imply for responders?
- A city documents its portal analyzer as collection, ingest, store, process, and serve. What does that mapping illustrate?
- Editors need live page-view counters while analysts keep multi-year history. How should NoSQL and the lake share duties?
- A mis-tagged funnel step corrupted last month's permit metrics. Raw logs still exist. What resilience action should the analyzer support?
- A city redesigns its portal analytics stack and must pick collection, Kafka ingestion, Spark processing, a lake, NoSQL counters, and dashboards that work together. What selection principle should guide the capstone design?