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NVIDIA AI Infrastructure Sample Questions (Q181-Q186):

NEW QUESTION # 181
You're deploying a BlueField-2 DPU in a cloud environment and need to ensure the integrity of the DPU's firmware. You want to verify that the firmware hasn't been tampered with. Which of the following methods provides the strongest level of assurance for firmware integrity?

A. Checking the MD5 checksum of the firmware image against a known good value.
B. Checking the file size of the firmware image against a known good value.
C. Verifying the SHA256 checksum of the firmware image against a known good value provided by NVIDIA.
D. Using a digitally signed firmware image and verifying the signature using NVIDIA's public key.
E. Comparing the firmware version reported by the DPU with the version listed in the NVIDIA release notes.

Answer: D

Explanation:
Digitally signed firmware provides the strongest guarantee of integrity. The signature verifies that the firmware hasn't been tampered with since it was signed by NVIDIA. SHA256 checksums are good, but digital signatures are cryptographically stronger. MD5 checksums are considered weak and easily compromised. Firmware version and file size offer minimal assurance against sophisticated attacks.

NEW QUESTION # 182
You are installing four NVIDIAAIOO GPUs into a server designed for AI training. The server motherboard has multiple PCIe Gen4 x16 slots. However, the server's power supply unit (PSU) only has three 8-pin PCIe power connectors available. What is the BEST course of action to ensure all GPUs receive adequate power?

A. Replace the existing PSU with a higher wattage PSU that has at least four 8-pin PCIe power connectors.
B. Use a PCIe power splitter cable on one of the 8-pin connectors to power two GPUs.
C. Underclock the GPUs significantly to reduce their power consumption below the available PSU capacity.
D. Connect the GPUs using the motherboard's internal SATA power connectors.
E. Install only three GPUs and leave the fourth unpowered.

Answer: A

Explanation:
Using a splitter can overload the original connector and cause instability or damage. Leaving a GPU unpowered defeats the purpose. SATA connectors are not designed for the high power requirements of GPUs. Underclocking might work, but is not a reliable long-term solution. Replacing the PSU is the safest and most reliable solution.

NEW QUESTION # 183
You are troubleshooting an issue where a Docker container utilizing NVIDIA GPUs intermittently fails with a 'CUDA ERROR OUT OF MEMORY error. The host system has sufficient memory and the individual GPU has enough memory as well. You suspect that the problem might be related to how memory is being allocated within the container environment. What steps can you take to investigate and potentially mitigate this issue?

A. Monitor GPU memory usage both inside and outside the container using 'nvidia-smi' to identify memory leaks or excessive allocation.
B. Increase the shared memory size for the container using the '-shm-size' flag when running the container.
C. Adjust the environment variable inside the container to ensure consistent GPU ordering.
D. Set the environment variable inside the container to limit the number of GPUs visible to the application.
E. Lower the compute capability using '-compute' parameter on docker run.

Answer: A,B

Explanation:
A 'CUDA ERROR OUT OF MEMORY' error can occur due to insufficient shared memory within the container (A). Increasing the shared memory size allows the container to allocate more memory for inter-process communication and GPU data transfers. Monitoring GPU memory usage both inside and outside the container (D) is crucial to identify the source of the memory exhaustion. 'CUDA VISIBLE DEVICES' and (B & C) are primarily used for GPU selection and ordering, not memory management, although limiting GPU visibility could indirectly reduce overall memory consumption if the application is poorly designed and tries to allocate memory on all visible GPUs regardless of need. Lowering compute capability won't directly affect memory usage, although the application will need less memory to process, it might cause issues.

NEW QUESTION # 184
You have a server with 8 NVIDIA A100 GPUs. You want to configure each GPU to be used by a different user, ensuring resource isolation and preventing one user's workload from monopolizing the entire GPU. Which NVIDIA technology is most suitable for this scenario?

A. NVIDIA SLI (Scalable Link Interface)
B. NVIDIA CUDA MPS (Multi-Process service)
C. NVIDIA NVLink
D. NVIDIA vGPU (virtual GPU)
E. NVIDIA MIG (Mufti-instance GPU)

Answer: E

Explanation:
NVIDIA MIG (Multi-lnstance GPU) is designed specifically for this scenario. It allows partitioning a single physical GPU into multiple isolated GPU instances, each with its own dedicated memory, compute, and isolation. CUDA MPS allows multiple CUDA applications to share a single GPU but does not provide the same level of resource isolation as MIG. vGPU is primarily for virtualized environments. SLI and NVLink are for GPU interconnection, not resource isolation.

NEW QUESTION # 185
You're setting up a BlueField-3 DPIJ to offload storage virtualization tasks. Specifically, you want to use SPDK (Storage Performance Development Kit) on the DPIJ. What are the MINIMUM required steps to enable SPDK on the BlueField-3 after the DPIJ has been flashed with the appropriate OS image? (Select TWO)

A. Enable the SPDK service using 'systemctl enable spdK and 'systemctl start spdk'.
B. Configure the network interfaces on the DPIJ to support RDMA or NVMe-oF, depending on the desired storage protocol.
C. Configure the Huge Pages settings in the DPU's kernel to allocate sufficient memory for SPDK.
D. Install the SPDK packages using the DPU's package manager (e.g., 'apt install spdk').
E. Download and compile the SPDK source code directly on the DPU.

Answer: C,D

Explanation:
The minimum steps involve installing the SPDK packages using the DPU's package manager, assuming prebuilt packages are available, and configuring Huge Pages. SPDK relies heavily on Huge Pages for memory management. While configuring network interfaces and starting services are important, installing SPDK and configuring Huge Pages are required first steps. Downloading and compiling from source might be necessary in some cases but not minimally required if packages are available.

NEW QUESTION # 186
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