KServe uye Model Kushanda paKubernetes Quiz

Mhinduro uye tsananguro zvinoonekwa paunenge uchitora bvunzo.

1. Nderipi raiva zita rekare reKServe risati rava chirongwa chakazvimirira?
   • KFServing
   • KubeML
   • ModelMesh
   • Knative Models
2. Ndeipi yekutanga Kubernetes tsika sosi yaunotsanangura kuendesa modhi neKServe?
   • ModelDeployment
   • InferenceService
   • PredictPod
   • ServingChart
3. Ndeupi kugona kunoita kuti maKServe mamodheru ashandise zero compute kusvika chikumbiro chasvika?
   • Canary kuburitswa
   • Sharding
   • Chikero-kusvika-zero
   • Batch inference
4. Ndeipi yepasi purojekiti inopa KServe's chikumbiro-inotungamirwa autoscaling?
   • Prometheus
   • Helm
   • Argo
   • Knative
5. KServe inowanzowana sei zvigadzirwa zvemhando mupodhi inoshumira pakutanga?
   • Kuburikidza neyekutanga yekuchengetedza iyo inodhonza kubva mukuchengetedza chinhu
   • Nekubheka modhi mumufananidzo wemudziyo chete
   • Nekutumira email iyo modhi faira
   • Kuburikidza nebhuku kubectl kopi
6. Ndeipi chiyero chinoshandiswa neKServe kuitira kuti vatengi vafonere mhando dzakasiyana nenzira imwechete?
   • GraphQL Federation
   • Iyo Open Inference Protocol
   • SIPO
   • JDBC
7. In KServe’s InferenceService pipeline, which component can apply pre- or post-processing around the predictor?
   • The storage initializer
   • The Knative autoscaler
   • The transformer
   • The Kubernetes scheduler
8. What is a typical trade-off of scaling a model deployment down to zero replicas?
   • The model becomes permanently unavailable
   • Every request avoids model loading
   • The Kubernetes API is replaced
   • Idle compute is reduced, but the next request may incur a cold-start delay