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Actual NVIDIA NCP-AIO Dumps - Quick Test Preparation Tips
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NVIDIA AI Operations Sample Questions (Q63-Q68):
NEW QUESTION # 63
You are using Fleet Command to manage AI model deployments to a diverse fleet of edge devices with varying hardware capabilities.
Some devices are equipped with GPUs, while others rely on CPUs for inference. How can you ensure that the correct version of the AI model is deployed to each device type?
- A. Deploy the same model version to all devices and rely on the devices to automatically adapt to their hardware.
- B. Manually select the appropriate model version for each device during deployment.
- C. Develop a custom script to determine device capabilities and deploy models accordingly.
- D. Use Fleet Command's device targeting feature with appropriate labels to define deployment rules based on hardware capabilities.
- E. Create separate Fleet Command organizations for each device type.
Answer: D
Explanation:
Device targeting with labels is the most efficient and scalable way to manage deployments to diverse hardware. Separate organizations (A) are overly complex. Manual selection (C) is error-prone. Relying on automatic adaptation (D) might not be reliable. Custom scripts (E) add unnecessary complexity when Fleet Command provides built-in features.
NEW QUESTION # 64
You want to limit the GPU memory usage of a specific container within a Kubernetes pod running an AI inference service. How can you achieve this using NVIDIA tools and Kubernetes resources?
- A. Configure the Kubernetes scheduler to only schedule pods with GPU memory limits on nodes with sufficient free GPU memory.
- B. Use the 'nvidia-smi' command within the container to limit the GPU memory usage of the process.
- C. Set the 'CUDA VISIBLE_DEVICES' environment variable to an empty string for that container.
- D. Utilize the NVIDIA MPS (Multi-Process Service) and configure memory limits for each process using MPS control commands.
- E. Set resource limits for 'nvidia.com/gpu' in the pod's resource requests and limits.
Answer: D
Explanation:
The correct answer is C. NVIDIA MPS (Multi-Process Service) allows multiple processes to share a single GPU, and it provides mechanisms to control the memory usage of each process. By configuring MPS, you can limit the GPU memory available to a specific container. Option A disables GPU access entirely. Option B is not a reliable way to enforce memory limits. Option D only controls the number of GPUs, not the memory usage per container. Option E describes scheduling based on available memory, but doesn't enforce limits.
NEW QUESTION # 65
You are deploying a DOCA-based Intrusion Detection System (IDS) on a BlueField-3 DPU. The IDS needs to analyze network traffic in real-time to detect malicious activity. Which of the following DOCA services would be most suitable for implementing the core functionality of the IDS, and how would you configure them?
- A. DOCA RegEx: Implement regular expression matching to identify complex patterns within the network traffic, such as malware signatures or exploit attempts. Configure RegEx rules based on known threat patterns.
- B. DOCA DPI: Utilize DOCA DPI to perform deep packet inspection and identify malicious patterns or signatures within the network traffic. Configure DPI rules based on known threat intelligence feeds and custom signatures.
- C. DOCA Telemetry: Collect network traffic statistics and flow information using DOCA Telemetry. Analyze the collected data to identify anomalies or suspicious behavior that may indicate malicious activity.
- D. DOCA Flow: Define flow rules to match specific network traffic patterns associated with malicious activity, such as suspicious ports or protocols. Trigger actions such as logging, dropping, or redirecting the traffic for further analysis.
- E. DOCA Comm Channel: Communicate the detected intrusions with host server for the purpose of logging.
Answer: A,B,D
Explanation:
DOCA DPI, DOCA Flow, and DOCA RegEx are all valuable services for implementing an IDS. DOCA DPI performs deep packet inspection, DOCA Flow matches specific traffic patterns, and DOCA RegEx identifies complex patterns. Telemetry is used for monitoring, and comm channel is for communication. Configuring them with threat intelligent feeds is necessary.
NEW QUESTION # 66
A data science team is using Fleet Command to deploy AI models to edge devices in a smart city project. They've noticed that some devices are consistently failing to update due to insufficient disk space. Which of the following is the MOST effective strategy to mitigate this issue?
- A. Implement a process to automatically clean up unused files and data on the edge devices before each update.
- B. Ignore the failing devices and focus on the ones that are updating successfully.
- C. Increase the disk space on all edge devices remotely via Fleet Command.
- D. Optimize the deployed models to reduce their size, and update the affected devices only.
- E. Roll back the updates to the previous version for all devices.
Answer: A
Explanation:
Optimizing models (B) is helpful, but a cleanup process (E) addresses the root cause. Increasing disk space (A) might not be feasible or cost-effective. Ignoring devices (C) is unacceptable. Rolling back updates (D) is a temporary solution. Thus, automatically cleaning up unused files is the most proactive and sustainable approach.
NEW QUESTION # 67
You are tasked with optimizing the performance of a large-scale graph analytics application that uses NVSHMEM for distributed shared memory. The application spends a significant amount of time in remote memory accesses. Which of the following strategies would be MOST effective in reducing the overhead of these remote accesses?
- A. Reduce the size of the graph.
- B. Increase the number of GPUs per node.
- C. Use NVSHMEM collectives for bulk data transfers.
- D. Disable CUDA-Aware MPI support
- E. Switch to a CPU-based implementation.
Answer: C
Explanation:
NVSHMEM collectives provide optimized routines for performing operations on shared memory across multiple processing elements (PEs). Using collectives for bulk data transfers, such as 'nvshmem_putmem' or 'nvshmem_getmem', is significantly more efficient than performing many individual small remote memory accesses. Increasing the number of GPUs per node might help with local computations but doesn't directly address remote access overhead. Reducing the graph size is not always feasible. A CPU-based implementation would likely be slower. Disabling CUDA-Aware MPI would degrade network communication speed, so not a good option.
NEW QUESTION # 68
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