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| Section | Weight | Objectives |
|---|---|---|
| Troubleshoot and Optimize | 12% | - Performance optimization
|
| Control Plane Installation and Configuration | 19% | - GPU utilization in containers
|
| Physical Layer Management | 5% | - Networking and GPU partitioning
|
| Cluster Test and Verification | 33% | - Performance and stress testing
|
| System and Server Bring-up | 31% | - Physical infrastructure validation
|
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NEW QUESTION # 23
An engineer must ensure that a BlueField-3 NIC firmware download matches the cluster's PSID. Which step is critical before installation?
Answer: A
Explanation:
The critical step is to query the device PSID using mstflint -d < PCI_ID > query before selecting or installing the firmware image. The PSID identifies the exact NVIDIA networking device variant, board configuration, and supported firmware branch. In a BlueField-3 or ConnectX-based AI infrastructure environment, using firmware built for the wrong PSID can cause installation failure, unsupported behavior, or device instability.
This is especially important in cluster operations because firmware consistency must be maintained across nodes, DPUs, SuperNICs, and adapters. Checking BMC reachability may help with platform management but does not confirm firmware-device compatibility. Firmware file size is not a reliable compatibility indicator.
Hash verification can confirm file integrity, but it does not prove the image matches the device PSID.
NVIDIA MFT and mstflint workflows are designed for querying device identity and firmware details before burn or update operations, and NVIDIA documentation references PSID-based firmware querying in MFT workflows.
NEW QUESTION # 24
You have a deep learning application that requires a specific version of the CUDA toolkit inside the container. How should you best ensure that the correct CUDA version is available within the container, considering the NVIDIA Container Toolkit is installed on the host?
Answer: A
Explanation:
The recommended approach is to use a base image that already contains the desired CUDA version. NVIDIA provides pre-built images on NGC (NVIDIA GPU Cloud) that are specifically designed for deep learning and include the appropriate CUDA versions and other dependencies. Installing CUDA on the host and expecting it to be magically mapped (A) is not reliable. The NVIDIA Container Toolkit doesn't install CUDA on the fly (B). Manually copying libraries (D) is error-prone and doesn't handle dependencies well. While technically possible, using nvidia- container-cli to modify the image is more complex than using a base image.
NEW QUESTION # 25
What command is needed to measure BER (Bit Error Rate)?
Answer: A
Explanation:
In NVIDIA networking environments, specifically those utilizing InfiniBand or high-speed Ethernet via ConnectX adapters, monitoring the physical link quality is critical for preventing packet loss and RDMA retransmissions. The mlxlink tool is part of the NVIDIA Firmware Tools (MFT) package and is the primary utility for checking the status and health of the physical link. Using the -d flag specifies the device (e.g., /dev
/mst/mt4123_pciconf0), while the -c (counters) and -e (error counters/BER) flags provide a detailed readout of the link's performance. Bit Error Rate (BER) is a fundamental metric for signal integrity. NVIDIA systems typically distinguish between "Raw BER" (errors before Forward Error Correction) and "Effective BER" (errors remaining after FEC). A high BER often points to a failing transceiver, a dirty fiber connector, or a marginal DAC cable. While ethtool can show general statistics in Ethernet mode, mlxlink is the verified method for granular BER measurement across InfiniBand and high-speed fabrics, allowing engineers to determine if a link meets the "Error-Free" operation standards required for large-scale AI collective communications like NCCL.
NEW QUESTION # 26
You're deploying a new cluster with multiple NVIDIAAIOO GPUs per node. You want to ensure optimal inter-GPU communication performance using NVLink. Which of the following configurations are critical for achieving maximum NVLink bandwidth?
Answer: B,E
Explanation:
For optimal NVLink performance, several conditions must be met. GPUs of the same model and firmware ensure compatibility and prevent performance bottlenecks. Physical placement is critical; GPUs must be installed in slots that maximize direct NVLink connections, as defined by the server's architecture and documentation. While PCle Gen5 is beneficial for overall system performance, it does not directly impact NVLink bandwidth. NVLink is typically enabled by default. Some CPU models may be preferable, but it's the motherboard's NVLink topology that is more important.
NEW QUESTION # 27
During a 72-hour HPL burn-in test on a DGX H100 cluster, one node shows a 15% performance drop after 48 hours. What are the two most likely causes and diagnostic steps?
Pick the 2 correct responses below.
Answer: A,B
Explanation:
The two most likely causes are network packet loss and thermal throttling. A performance drop after 48 hours of HPL burn-in is less likely to be a simple launch-time MPI configuration issue, because MPI affinity errors usually appear from the beginning of the run as consistently poor performance. A delayed degradation suggests the system changed state under sustained load. Thermal throttling is a common cause: after many hours, rack cooling imbalance, blocked airflow, high inlet temperature, or fan behavior can cause GPU clocks to drop. nvidia-smi dmon helps monitor GPU temperature, power, utilization, and clocks over time. Network packet loss is also likely in multi-node HPL because HPL depends on heavy communication across the InfiniBand fabric. Link errors, symbol errors, retransmissions, degraded cables, or congestion can reduce sustained performance. ibdiagnet is the correct fabric-level diagnostic tool to collect and analyze InfiniBand health, topology, counters, and link issues. Rebooting and reducing matrix size would hide the symptom rather than diagnose it. Correct burn-in practice is to preserve evidence, inspect thermal telemetry, review network diagnostics, and compare the affected node against healthy peers.
NEW QUESTION # 28
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