Determine the Requirements Needed for Inferencing CERTIFIED-AI-EXPERT Practice Questions (Page 5)

Questions 21–25

1. 21foundation · easy

   Which of the following is the primary purpose of defining inference requirements before deploying an AI model?

   • ATo ensure the model is trained on the most recent data
   • BTo identify the hardware, software, and environmental resources needed for effective inference
   • CTo guarantee the model will achieve 100% accuracy
   • DTo reduce the size of the training dataset
   Reveal Solution Discussion Select an answer first
2. 22expert · hard

   A social-media company runs a content-filtering model that must process 50,000 images per minute during peak hours, with a p99 latency under 500 ms. The team has a Kubernetes cluster with autoscaling enabled, but they are concerned about cold-start latency when new pods are created. Which approach should they take to ensure consistent latency during traffic spikes?

   • APre-warm a pool of inference pods and use a load balancer to distribute traffic.
   • BUse a larger GPU in each pod to handle more requests per pod.
   • CRely on Kubernetes horizontal pod autoscaling to add pods as CPU utilization increases.
   • DIncrease the batch size on each pod to improve throughput.
   Reveal Solution Discussion Select an answer first
4. 23foundation · easy

   What is the primary goal of scalability planning for an inference service?

   • ATo ensure the service can handle increasing or fluctuating request volumes
   • BTo reduce the model's training time
   • CTo improve the model's accuracy
   • DTo reduce the size of the model
   Reveal Solution Discussion Select an answer first
5. 24expert · hard

   A logistics company has deployed a demand-forecasting model that runs nightly batch jobs. The model's accuracy has been declining, and the team suspects data drift. They need to detect drift and retrain the model, but they have limited engineering resources and want to minimize manual intervention. Which monitoring and maintenance strategy should they implement?

   • AMonitor GPU utilization and set an alert if it drops below 50%.
   • BRetrain the model every week on the latest data, regardless of drift.
   • CManually review model performance reports on a monthly basis.
   • DSet up automated drift detection that compares prediction distributions to actual outcomes and triggers retraining when drift exceeds a threshold.
   Reveal Solution Discussion Select an answer first
6. 25expert · hard

   A medical-imaging company is deploying a 3D segmentation model that requires 32 GB of GPU memory in FP32. The company has a cluster of GPUs with 16 GB each. The model must produce results within 5 seconds, and the team cannot afford new hardware. Which approach should they take?

   • AQuantize the model to INT8 and run it on a single 16 GB GPU.
   • BDeploy the model on a CPU server with large RAM.
   • CUse a larger batch size to improve GPU utilization on a single GPU.
   • DUse model parallelism to split the model across two 16 GB GPUs.
   Reveal Solution Discussion Select an answer first