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Question#1

An AI infrastructure utilizes NVIDIA ConnectX-7 NICs for inter-node communication. The requirement is to achieve a bandwidth of 400GbE with low latency over a distance of 100 meters.
Which transceiver and cable type combination is MOST suitable for this scenario?

A. QSFP-DD SR8 transceiver with OM3 multi-mode fiber cable.

B. QSFP-DD DR4 transceiver with single-mode fiber cable.

C. QSFP-DD LR8 transceiver with OM4 multi-mode fiber cable.

D. QSFP-DD SR4 transceiver with OM4 multi-mode fiber cable.

E. QSFP-DD FR4 transceiver with single-mode fiber cable.

Explanation:
QSFP-DD SR4 with OM4 provides 400GbE over short distances (up to 100m) using multi-mode fiber. DR4 and FR4 require single- mode fiber and are typically used for longer distances. LR8 typically requires single mode fibre for specified distances. Using SR8 with OM3 is unlikely to achieve 400GbE as SR8 works best with OM4/OM5.

Question#2

You are deploying a multi-tenant AI infrastructure where different users or groups have isolated network environments using VXLAN.
Which of the following is the MOST important consideration when configuring the VTEPs (VXLAN Tunnel Endpoints) on the hosts to ensure proper network isolation and performance?

A. Using the default MTU size of 1500 bytes for VXLAN traffic.

B. Ensuring that each tenant has a unique VXLAN Network Identifier (VNI) to isolate their traffic.

C. Using the same IP address for all VTEPs to simplify routing.

D. Disabling multicast routing to prevent broadcast traffic.

E. Using the same VNI for all tenants to maximize network utilization.

Explanation:
The most critical aspect of VXLAN configuration for multi-tenancy is ensuring that each tenant has a unique VNI. The VNI is a 24-bit identifier that segments the VXLAN network into isolated logical networks. Using unique VNIs guarantees that traffic from one tenant remains isolated from other tenants, preventing data leakage and ensuring security. Using overlapping or shared VNIs would defeat the purpose of VXLAN- based isolation.

Question#3

You are tasked with validating a newly installed NVIDIAAIOO Tensor Core GPU within a server. You need to confirm the GPU is correctly recognized and functioning at its expected performance level. Describe the process, including commands and tools, to verify the following aspects: 1) GPU presence and basic information, 2) PCle bandwidth and link speed, and 3) Sustained computational performance under load.

A. 1) Use ‘Ispci I grep NVIDIA’ for presence, ‘nvidia-smi’ for basic info. 2) Use ‘nvidia-smi -q -d pcie’ for bandwidth/speed. 3) Run a TensorFlow ResNet50 benchmark.

B. 1) Use ‘nvidia-smi’ for presence and basic info. 2) PCle speed is irrelevant. 3) Run the ‘nvprof profiler during a CUDA application.

C. 1) Check BIOS settings for GPU detection. 2) Use ‘Ispci -vv’ to check PCle speed. 3) Run a PyTorch ImageNet training script.

D. 1) Use ‘nvidia-smi’ for presence and basic info. 2) Use ‘nvidia-smi -q -d pcie’ for bandwidth/speed. 3) Run a CUDA-based matrix multiplication benchmark (e.g., using cuBLAS) with increasing matrix sizes and monitor performance.

E. 1) Use ‘nvidia-smi’ for presence and basic info. 2) Use ‘nvlink-monitor’ for bandwidth/speed. 3) Run a CPU-bound benchmark to avoid GPU bottlenecks.

Explanation:
‘nvidia-smi’ is the primary tool for NVIDIA GPU information. ‘nvidia-smi -q -d pcie’ provides PCle details. A CUDA-based benchmark isolates GPU performance. Other options have elements of truth but aren’t complete or optimally targeted (e.g., ResNet50 relies on other frameworks). Using a CPU-bound benchmark wouldn’t test the GPU’s capabilities.

Question#4

You need to verify the integrity of the BlueField OS image before flashing it to the SmartNIC.
Which method would provide the strongest guarantee that the image has not been tampered with?

A. Comparing the image size with the expected size from the download source.

B. Checking the MD5 checksum of the image against the published MD5 checksum.

C. Verifying the SHA256 checksum of the image against the published SHA256 checksum.

D. Downloading the image from a trusted source.

E. Utilizing gpg to verify the image.

Explanation:
SHA256 provides a stronger collision resistance than MD5, making it a more reliable method for verifying image integrity. Comparing the image size is not a reliable method, as a small change can be made without significantly altering the size. Downloading from a trusted source is important, but doesn’t guarantee the image hasn’t been tampered with after it was published. GPG provides cryptographic assurance that the file originated from and was signed by an identified party.

Question#5

You’re configuring a RoCEv2 network for your AI infrastructure.
Which UDP port number range is commonly used for RoCEv2 traffic, and why is it important to be aware of this?

A. 0-1023, because these are well-known ports.

B. 4791, which is reserved for VXLA

C. 49152-65535, the dynamic/private port range, to avoid conflicts with other services.

D. 1024-49151, the registered port range, for general application use.

E. Any UDP port number can be used without issue.

Explanation:
RoCEv2 commonly uses the dynamic/private port range (49152-65535). This avoids potential conflicts with well-known or registered ports used by other services. Knowing this range is crucial for configuring firewalls and network policies to allow RoCEv2 traffic to flow freely.

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