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Free PDF Quiz 2025 NVIDIA NCP-AIO: First-grade NVIDIA AI Operations Exam Collection
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NVIDIA AI Operations Sample Questions (Q120-Q125):
NEW QUESTION # 120
You are designing storage for an AI model that performs real-time object detection on streaming video. Which of the following storage characteristics are most important?
- A. Large capacity for long-term video archival.
- B. Support for cold storage.
- C. Data deduplication for minimizing storage costs.
- D. High sustained write throughput for incoming video streams.
- E. Low latency for accessing video frames during inference.
Answer: D,E
Explanation:
Real-time object detection requires both the ability to ingest incoming video streams quickly (high write throughput) and access frames with minimal delay (low latency) for inference. Long-term archival and deduplication are less critical for real-time performance. Cold storage is not relevant in the operational data flow.
NEW QUESTION # 121
Which of the following is NOT a typical benefit of using GPUDirect Storage?
- A. Increased GPU memory capacity
- B. Elimination of CPU memory copies
- C. Reduced CPU utilization
- D. Lower latency for data access
- E. Improved end-to-end training throughput
Answer: A
Explanation:
GPUDirect Storage focuses on optimizing data transfer between storage and GPU memory, reducing CPU overhead and latency, and improving overall throughput. It does not increase the GPU's memory capacity. The main advantages are bypassing CPU memory, reducing CPU utilization, and accelerating data transfer.
NEW QUESTION # 122
You have a cluster dedicated to AI inference, serving models from a persistent volume. You're experiencing high latency and CPU usage on the nodes serving inference requests. You suspect that storage access patterns are contributing to the issue. Your persistent volume is backed by a distributed file system. Describe a strategy, including relevant tools and techniques, to analyze the storage I/O profile of your inference workloads and identify potential optimizations.
- A. Use 'iotop' or 'iostat' on the compute nodes to monitor real-time I/O activity and identify processes with high disk I/O. Then check the related containers that are doing more of these reads/writes.
- B. Utilize the distributed file system's monitoring tools (if available) to analyze I/O patterns at the file system level. This can reveal hotspots or inefficient data access patterns.
- C. Implement storage QOS (Quality of Service) policies to prioritize inference workloads and limit the impact of other I/O-intensive processes.
- D. Capture network traffic using 'tcpdump' or Wireshark to analyze the communication patterns between the compute nodes and the storage system. Look for excessive network latency or congestion. Also monitor the network latency using tools like 'ping' or 'iperf.
- E. Randomly restart the inference pods. If the issue goes away, it means the storage system was temporarily overloaded.
Answer: A,B,C,D
Explanation:
'iotopTiostat' identifies I/O-heavy processes. 'tcpdump'/Wireshark/ping/iperf helps analyze network communication. File system monitoring tools reveal data access patterns. Implementing storage QOS prioritizes inference workloads. Only restart the inference pods if you have a strong reason, otherwise troubleshooting the storage using one of the other methods is best practice.
NEW QUESTION # 123
You need to implement a highly available and fault-tolerant Fleet Command deployment for a mission-critical AI application. What architectural considerations are MOST important for ensuring resilience?
- A. Rely solely on the edge devices' ability to operate independently during Fleet Command outages.
- B. Regularly back up the Fleet Command server's configuration and data.
- C. Deploy a single Fleet Command server in a standard configuration.
- D. Utilize a multi-node Fleet Command cluster with redundancy and failover mechanisms, combined with geographically diverse edge device deployments.
- E. Monitor the Fleet Command server's performance and resource utilization.
Answer: D
Explanation:
A multi-node cluster provides redundancy and failover capabilities, ensuring high availability. Geographically diverse edge deployments minimize the impact of regional outages. A single server (A) is a single point of failure. Backups (C) are important but don't prevent downtime. Monitoring (D) helps identify issues but doesn't ensure resilience. Solely relying on edge devices (E) limits manageability and control.
NEW QUESTION # 124
You need to upgrade the Run.ai platform on your on-premise Kubernetes cluster. What is the RECOMMENDED procedure for performing this upgrade to minimize downtime and ensure a smooth transition?
- A. Simply restart all Run.ai pods in the cluster.
- B. Delete the existing Run.ai deployment and install the new version from scratch.
- C. Edit the Run.ai deployment YAML file directly and apply the changes.
- D. Follow the official Run.ai documentation for the specific upgrade path, typically involving a rolling update procedure using Helm or kubectl apply.
- E. Upgrade the Kubernetes cluster itself before upgrading Run.ai.
Answer: D
Explanation:
Following the official Run.ai documentation is crucial for a smooth upgrade. Run.ai typically provides detailed instructions for upgrading the platform, often involving a rolling update procedure using Helm or kubectl apply. This minimizes downtime by updating components incrementally. Deleting and reinstalling is disruptive and risky. Directly editing the deployment YAML is not recommended. While Kubernetes version compatibility is important, Run.ai upgrades should generally be performed independently, following Run.ai's documented procedure. Restarting pods is insufficient for a platform upgrade.
NEW QUESTION # 125
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