Exam NCP-AIO Objectives Pass Certify| Reliable NCP-AIO Latest Braindumps Questions: NVIDIA AI Operations
Exam NCP-AIO Objectives Pass Certify| Reliable NCP-AIO Latest Braindumps Questions: NVIDIA AI Operations
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NVIDIA AI Operations Sample Questions (Q15-Q20):
NEW QUESTION # 15
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. Data deduplication for minimizing storage costs.
- B. High sustained write throughput for incoming video streams.
- C. Low latency for accessing video frames during inference.
- D. Large capacity for long-term video archival.
- E. Support for cold storage.
Answer: B,C
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 # 16
You are deploying BCM in a high-availability (HA) configuration. What considerations are critical for ensuring data consistency and minimal downtime during a failover scenario?
- A. Ensure that all BCM instances share a common storage volume for persistent data.
- B. Use a highly available database cluster (e.g., PostgreSQL with replication) for the BCM database.
- C. Implement a mechanism for automatically failing over the BCM service to a backup instance in case of a primary instance failure.
- D. Configure a load balancer to distribute traffic across multiple BCM instances.
- E. Configure regular backups of the BCM database to a remote location.
Answer: B,C,D
Explanation:
In a HA configuration, a highly available database cluster is crucial for data consistency. A load balancer distributes traffic across multiple BCM instances, ensuring availability even if one instance fails. An automatic failover mechanism ensures minimal downtime by automatically switching to a backup instance. Sharing a common storage volume is generally not recommended due to potential data corruption issues. Regular backups are important but are more relevant for disaster recovery than immediate failover.
NEW QUESTION # 17
Which of the following are benefits of using NVIDIA Fleet Command (Select all that apply)?
- A. Automated over-the-air (OTA) updates for AI models and system software.
- B. Enhanced security and access control for AI deployments.
- C. Simplified AI model deployment and management at the edge.
- D. Centralized monitoring and management of edge devices.
- E. Automatic GPU driver updates for non-NVIDIA GPUs.
Answer: A,B,C,D
Explanation:
Fleet Command provides simplified deployment (A), centralized monitoring (B), OTA updates (C), and enhanced security (D). It does not provide driver updates for non-NVIDIA GPUs (E).
NEW QUESTION # 18
You are using GPUDirect Storage (GDS) to accelerate data loading directly from NVMe drives to GPU memory. After implementing GDS, you observe no performance improvement. What could be the reason?
- A. GDS only supports single-GPU configurations.
- B. The NVMe drives are not directly attached to the GPUs via PCle.
- C. The CUDA driver version is incompatible with the GDS version.
- D. The system memory is a bottleneck and data is being staged to system memory before going to the GPU.
- E. The software libraries used for data loading (e.g., TensorFlow, PyTorch) are not GDS-aware.
Answer: B,C,D,E
Explanation:
GDS requires direct PCle connection between NVMe and GPU for optimal performance. The software libraries must be updated with a version that is GDS-aware to use this feature. Incompatible CUDA/GDS versions can cause failures. If the data has to go to system memory first before going to the GPU then you bypass GDS.
NEW QUESTION # 19
You've deployed a container from NGC on a Kubernetes cluster, but the application is experiencing intermittent GPU errors. You suspect memory leaks within the container are causing the issue. What is the most effective method to diagnose this problem?
- A. Analyze the application's logs for CUDA error messages related to memory allocation.
- B. Monitor the container's CPU usage using 'kubectl top pod'.
- C. Use NVIDIA Nsight Systems to profile the application and identify memory allocation patterns.
- D. Restart the container regularly to clear potential memory leaks.
- E. Use 'nvidia-smi' within the container to monitor GPU memory usage over time.
Answer: A,C,E
Explanation:
B, C, and E are correct. 'nvidia-smi' provides real-time GPU memory usage. Application logs often contain CUDA errors indicating memory issues. Nsight Systems offers detailed profiling to pinpoint memory leaks. A is not relevant to GPU memory leaks. D is a workaround, not a diagnostic solution.
NEW QUESTION # 20
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