Serving and scaling models
Practise only this domain. 66 questions with explanations, 20 at a time.
Start serving and scaling modelsSample questions
- A fintech app calls a fraud scorer for every card swipe and must answer within 100 milliseconds at the 95th percentile, with traffic that spikes during evenings. Which hosting choice best fits?Easy
- A retailer's recommendation endpoint must keep p99 latency under 50 ms during a flash sale that could bring ten times normal load. Which configuration best meets this requirement?Medium
- An ML team has produced a new version of a recommendation model. They want only a small slice of live requests to reach it at first, with that share raised step by step as dashboards confirm quality. Which rollout approach fits?Easy
- A marketing team must score 40 million customer rows once a week and write the scores back to storage. No one needs answers in real time. Which option is the most economical?Medium
- A logistics firm scores about 100 million shipment rows that live in a warehouse table. A TensorFlow model needs roughly 45 minutes to run on that volume, and the output must be ready before the morning reports. Which option keeps spend lowest while finishing on time?Medium
- A lender runs a credit model trained six months ago that has a strong production record. A newer candidate trained last week scores better on the hold-out set. Leadership wants evidence on live traffic before any customer-facing change. Which rollout is safest?Medium
- After a rollout, p95 latency on a serving deployment has climbed sharply, yet accelerator and CPU graphs on the serving nodes look mostly idle. The model call alone benchmarks quickly when run in isolation. What should be checked first?Medium