Troubleshoot and Optimize
NCP-AII · 24 questions
- A city permitting NOC sees rising GPU temperature and fan alerts on one inference node. What should operators do first?
- County health training jobs show NCCL failures that localize to one host's fabric path. What hardware-fault investigation is most appropriate?
- Transit ops must decide whether a GPU fault on a scheduling node is software-induced or a hardware FRU issue. Which approach fits?
- Emergency-management inference pods die intermittently with PCIe error storms under load. What troubleshooting focus is required?
- A school-district AI lab sees recurring GPU XID errors during exams-week inference. What should admins do with those codes?
- A city multi-GPU municipal server reports ECC failures but does not name which device is bad. What identification step is required?
- County health sees one PSU drop voltage only when AI training peaks, while sibling supplies stay healthy. What conclusion follows?
- Transit fabric errors localize to one PCIe slot hosting a ConnectX adapter on a GPU node. What FRU identification is correct?
- A water-utility AI rack suspects a card fault before opening an RMA. What confirmation step is required?
- Courts OCR ops declare a GPU faulty and must start the RMA path. What linking step is required?
- A city technician must replace a failed GPU in a municipal HGX host. Which replacement practice is required?
- County health must replace a failed PSU on a redundant GPU server. What replacement approach is correct?
- Transit ops replaces a failed NIC on a GPU training node. What must happen before returning the node to the pool?
- A housing authority installs a replacement GPU after a failure. What closeout steps are required?
- A library AI rack must replace dual PSUs over a maintenance window. What ordering rule protects continuity?
- A city wants less training jitter on Intel-based municipal GPU servers. Which optimization path fits Domain 5?
- County health GPU hosts run on AMD servers with uneven training throughput. What optimization guidance applies?
- Transit ops tunes CPU/platform settings across mixed AMD and Intel GPU racks. How should they prove the change helped?
- A school-district lab hears forum advice to overclock municipal GPU servers for 'free performance.' What should they do?
- Emergency management needs lower inference latency on Intel head nodes that currently favor aggressive power saving. What trade-off should they apply?
- City GPU jobs stall on I/O after storage tests show the parallel filesystem is the bottleneck. What optimization fits?
- County health public-health GPUs contend when checkpoints and datasets share one volume. What storage-layout optimization helps?
- Transit ops wants higher storage throughput on GPU nodes after confirming municipal storage can safely accept more concurrency. What tuning is appropriate?
- A water-utility digital-twin team profiles long I/O wait on GPU jobs reading hot feature stores. What optimization decision follows?