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NCP-AII Reliable Dumps Free | Certification NCP-AII Test Answers

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NVIDIA AI Infrastructure Sample Questions (Q122-Q127):

NEW QUESTION # 122
After installing the NGC CLI, you attempt to run 'ngc config set' and encounter the following error: 'Error: API key is invalid or missing'.
What are the most likely causes of this issue and how can you resolve them?

A. The NGC service is down. Check the NVIDIA NGC status page for any known outages.
B. The NGC API key is incorrect or has expired. Verify the API key in your NVIDIA account and update the configuration using 'ngc config set'.
C. The host does not have network access to NGC.
D. The NGC CLI configuration file is corrupted. Delete the file (A.ngc/config.json') and reconfigure the CLI.
E. The NGC CLI is not properly installed. Reinstall the package using 'pip install -upgrade nvidia-cli'

Answer: B,C,D

Explanation:
The most likely cause is an invalid API key (B) or a corrupted configuration file (C), or the host lacks network access (E). Reinstalling the package (A) might not resolve the issue if the problem lies with the API key or config file. While NGC service outages (D) are possible, they are less common.

NEW QUESTION # 123
You are tasked with deploying a cluster of NVIDIAAIOO GPUs in a high-density server environment. The server chassis has a limited power budget and cooling capacity. Which of the following strategies is MOST effective in validating that the power and cooling infrastructure can adequately support the GPU workload during peak performance, minimizing the risk of thermal throttling and system instability?

A. Monitor GPU temperature using 'nvidia-smi' during a sustained compute-intensive workload and compare it to the GPU's thermal threshold. If the temperature remains below the threshold, the cooling is adequate.
B. Simulate the Ai workload with a synthetic benchmark (e.g., Linpack) and extrapolate power consumption based on the benchmark's performance metrics.
C. Employ a power monitoring tool (e.g., IPMI, Redfish) to measure the actual power consumption of the server during a stress test that mimics the intended Ai workload. Cross-reference this with the power supply unit's (PSU) rating and the cooling system's capacity.
D. Observe the GPU clock speeds during a workload. If the clock speeds are at the maximum rated speed, the power and cooling are sufficient.
E. Rely solely on the GPU manufacturer's stated Thermal Design Power (TDP) specifications and allocate power based on these values.

Answer: C

Explanation:
Option C provides the most comprehensive approach. TDP is a theoretical maximum and doesn't reflect real-world power consumption. Monitoring temperature is important but doesn't account for total power draw. Synthetic benchmarks may not accurately represent the Ai workload. Monitoring actual power consumption and comparing it to the PSU rating and cooling capacity offers the most accurate validatiom.

NEW QUESTION # 124
You are building a cloud-native application that uses both CPU and GPU resources. You want to optimize resource utilization and cost by scheduling CPU-intensive tasks on nodes without GPUs and GPU-intensive tasks on nodes with GPUs. How would you achieve this node selection and workload placement in Kubernetes?

A. Use taints and tolerations to dedicate nodes without GPUs to CPU-intensive tasks and nodes with GPUs to GPU-intensive tasks.
B. Use node affinity rules to schedule CPU-intensive tasks on nodes without GPUs and GPU-intensive tasks on nodes with GPUs.
C. Use node affinity rules to schedule CPU-intensive tasks on nodes with GPUs and GPU-intensive tasks on nodes without GPUs.
D. Use resource quotas to limit the CPU resources available on nodes with GPUs and the GPU resources available on nodes without GPUs.
E. Use labels to identify the CPU and GPU-intensive nodes.

Answer: B

Explanation:
Node affinity rules allow you to specify constraints on which nodes a pod can be scheduled on. By using node affinity rules, you can ensure that CPU-intensive tasks are scheduled on nodes without GPUs and GPU-intensive tasks are scheduled on nodes with GPUs. This optimizes resource utilization and cost. Taints and tolerations can be used, but affinity is more flexible. Resource quotas limit resource usage but do not control placement.

NEW QUESTION # 125
Your AI training workload involves a large dataset stored in an object storage system (like AWS S3). You notice that fetching data directly from S3 during training is a bottleneck. Which of the following strategies can significantly improve data loading performance?

A. Use a caching layer (e.g., using Alluxio or JuiceFS) to cache frequently accessed data locally or on faster storage
B. Use S3 Glacier for data storage.
C. Increase the number of S3 request retries
D. Switch to a different object storage provider
E. Reduce the number of concurrent data loaders

Answer: A

Explanation:
A caching layer brings frequently accessed data closer to the compute nodes, reducing latency and improving throughput. Increasing retries only addresses transient errors. Reducing data loaders worsens performance. Switching providers might not solve the underlying latency issue. S3 Glacier is for archival, so will result in worse performance.

NEW QUESTION # 126
When deploying a BlueField DPIJ in an inline security appliance role, which networking configuration is MOST efficient and provides the lowest latency for inspecting all traffic passing through the server?

A. Implement a hairpin configuration where traffic is sent to the DPIJ and then looped back to the same physical interface after inspection.
B. Configure the BlueField as a traditional bridge, forwarding traffic between two physical interfaces.
C. Utilize a switchdev-based configuration where the DPIJ directly programs the switch ASIC to steer traffic.
D. Configure the BlueField as a router, performing Layer 3 forwarding between subnets.
E. Use SR-IOV to create Virtual Functions (VFs) for both the host and the DPIJ, and then route traffic between them.

Answer: C

Explanation:
A switchdev-based configuration offers the lowest latency because the DPIJ directly programs the switch ASIC. This allows for hardware-accelerated traffic steering and inspection without the overhead of traditional bridging or routing. Hairpinning introduces unnecessary latency, while SR-IOV and bridging add software processing overhead.

NEW QUESTION # 127
......

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