CERTIFICATION NCP-AIO QUESTIONS | NCP-AIO EXAM SAMPLE ONLINE

Certification NCP-AIO Questions | NCP-AIO Exam Sample Online

Certification NCP-AIO Questions | NCP-AIO Exam Sample Online

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Tags: Certification NCP-AIO Questions, NCP-AIO Exam Sample Online, NCP-AIO Test Dumps, NCP-AIO Reliable Test Pdf, Books NCP-AIO PDF

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NVIDIA AI Operations Sample Questions (Q245-Q250):

NEW QUESTION # 245
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 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.
  • D. 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.
  • E. DOCA Comm Channel: Communicate the detected intrusions with host server for the purpose of logging.

Answer: A,B,C

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 # 246
You are deploying a new AI model that requires very low latency inter-GPU communication. You have an NVSwitch-based system, and nvsm' is managing the fabric. You suspect that the default 'nvsm' configuration might not be optimal for low-latency workloads. What advanced 'nvsm' configuration options or related system settings could you investigate to further minimize NVLink latency? Describe at least TWO specific areas you would explore.

  • A. Tuning the system's CPU frequency scaling governor.
  • B. Adjusting the NVLink power management settings to favor performance over power savings.
  • C. Configuring Quality of Service (QOS) settings on the NVSwitch to prioritize NVLink traffic.
  • D. Disabling Address Space Layout Randomization (ASLR) for the application.
  • E. Disabling prefetching on the CPU.

Answer: B,C

Explanation:
Two key areas to explore for minimizing NVLink latency are: 1. NVLink Power Management: Adjust the NVLink power management settings to favor performance over power savings. NVLink, like many hardware components, might have power-saving modes that introduce latency. Configuring these settings to prioritize performance will reduce latency. 2. NVSwitch QOS: Configure Quality of Service (QOS) settings on the NVSwitch to prioritize NVLink traffic. The NVSwitch may support QOS mechanisms to prioritize certain types of traffic. Configuring QOS to give NVLink communication the highest priority can minimize latency for inter-GPU communication. Tuning the system's CPU frequency can increase performance in general, but might not have effect on the fabric. ASLR is more of a security feature and disabling could introduce vulnerabilities. CPU prefetching might influence CPU-GPU communication on a PCIE bus, but has very little impact on the dedicated NVLink link.


NEW QUESTION # 247
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. Create separate Fleet Command organizations for each device type.
  • B. Develop a custom script to determine device capabilities and deploy models accordingly.
  • C. Deploy the same model version to all devices and rely on the devices to automatically adapt to their hardware.
  • D. Manually select the appropriate model version for each device during deployment.
  • E. Use Fleet Command's device targeting feature with appropriate labels to define deployment rules based on hardware capabilities.

Answer: E

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 # 248
You are tasked with implementing data versioning and reproducibility for AI experiments. Which storage features or technologies are most relevant?

  • A. Replication for disaster recovery
  • B. Integration with version control systems like Git for data.
  • C. Data compression algorithms for minimizing storage footprint.
  • D. Encryption for security
  • E. Snapshots and cloning capabilities of the storage system.

Answer: B,E

Explanation:
Snapshots and cloning allow you to create consistent copies of data at specific points in time, facilitating reproducibility. Integrating with version control systems enables tracking changes to data and code together, ensuring experiments can be recreated accurately. While compression, replication, and encryption are important, they are not directly related to versioning and reproducibility.


NEW QUESTION # 249
A Docker container running a CUDA application terminates unexpectedly with an 'out of memory' error, despite the host machine having sufficient RAM. What are the potential causes and how would you diagnose them?

  • A. The container's memory limit is too low. Increase the container's memory limit using the -memory' flag with 'docker rum.
  • B. The host machine is experiencing swap thrashing. Monitor swap usage on the host machine and consider increasing RAM or optimizing the application's memory usage.
  • C. The GPU is running out of memory. Monitor GPU memory usage using 'nvidia-smr within the container to identify memory leaks or excessive memory allocation.
  • D. The application is experiencing a memory leak within the container. Use memory profiling tools within the container to identify and fix the memory leak.
  • E. The Docker image is corrupted. Rebuild the Docker image from scratch.

Answer: A,C,D

Explanation:
The error could stem from a constrained container memory limit, GPU memory exhaustion, or a memory leak within the application itself. Each of these requires different diagnostic approaches. Container memory limits can be checked and adjusted. GPU memory usage can be monitored. Application memory leaks need specific profiling tools inside the container.


NEW QUESTION # 250
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