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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Security and Best Practices | 10-15% | - Security fundamentals - Compliance considerations - Operational best practices |
| Topic 2: AI Infrastructure Fundamentals | 15-20% | - NVIDIA software stack overview - Data center infrastructure requirements - AI and Deep Learning concepts - GPU architecture basics |
| Topic 3: NVIDIA AI Infrastructure Components | 25-30% | - NVIDIA networking solutions (Mellanox) - NVIDIA DGX systems - Storage solutions for AI workloads - NVIDIA AI Enterprise software |
| Topic 4: Deployment and Configuration | 25-30% | - Cluster configuration - Software deployment - System installation and setup - Network configuration |
| Topic 5: Monitoring and Management | 15-20% | - Troubleshooting basics - Resource utilization - Performance monitoring - NVIDIA management tools |
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NEW QUESTION # 117
You are following the official steps to install the NVIDIA Container Toolkit using a package manager on Ubuntu. After importing the NVIDIA package repository and GPG key, what is the next action?
Answer: C
Explanation:
The NVIDIA Container Toolkit (formerly nvidia-docker2) is the essential middleware that allows Docker, Podman, or Containerd to "see" and utilize the host's GPU hardware. The standard installation workflow on Debian-based systems like Ubuntu involves three core phases: repository configuration, package installation, and runtime configuration. Once the GPG key is added (to ensure package integrity) and the .list file is placed in /etc/apt/sources.list.d/ (to point to the NVIDIA production servers), the local package index must be refreshed via apt-get update. Immediately following this, the administrator must install the toolkit using the command sudo apt-get install -y nvidia-container-toolkit. Rebooting (Option A) is unnecessary at this stage because no kernel modules have been modified yet. Downloading the CUDA Toolkit (Option D) is a separate step; notably, the Container Toolkit allows containers to run CUDA applications even if the host only has the NVIDIA driver installed, making the driver-not the host CUDA toolkit-the primary prerequisite.
NEW QUESTION # 118
A system administrator has upgraded the firmware of the DPU. What will be the state of the firmware after the upgrade?
Answer: C
Explanation:
The correct answer is that the firmware is waiting on reboot to become active. For NVIDIA networking devices and DPUs, a firmware upgrade generally writes the new firmware image to the device flash, but the currently running firmware may remain active until the device is reset, rebooted, or power-cycled. NVIDIA MFT documentation notes that loading new firmware may require mlxfwreset, reboot, or power cycle, depending on the device and situation. This distinction is important during server bring-up because administrators must verify both the flashed firmware version and the running firmware version before declaring the update complete. Assuming the firmware is immediately active can lead to version drift across nodes, inconsistent behavior, or failed validation. The firmware is not deleted, and saying it is merely copied but not installed is incomplete because the update process has written it to the device. The operational issue is activation. In production clusters, firmware upgrades should be followed by the required reset procedure and post-update validation using appropriate NVIDIA tools before returning the node to service.
NEW QUESTION # 119
You are observing high latency in your GPU-accelerated inference service deployed on Kubernetes. You suspect that GPU resource contention might be the cause. What steps can you take to diagnose and mitigate this issue within the Kubernetes environment? (Multiple Answers)
Answer: B,C,D
Explanation:
Monitoring GPU utilization helps identify resource contention. Resource quotas prevent one namespace from consuming excessive GPU resources. MIG allows partitioning GPUs for workload isolation. Increasing replicas (option D) might help, but it doesn't address the underlying contention issue. Restarting the cluster is not a solution for ongoing contention.
NEW QUESTION # 120
After Spectrum-X fabric deployment, NCCL tests show intermittent latency spikes. Which network condition most severely impacts East-West bandwidth?
Answer: C
Explanation:
Packet loss is the most severe condition because NCCL collective communication depends on predictable, reliable East-West data movement between GPUs. In Spectrum-X Ethernet fabrics, AI workloads rely on RoCE, congestion control, proper QoS, and low-loss behavior to keep GPU communication pipelines moving.
Even small amounts of packet loss can cause retransmissions, stalled collectives, increased tail latency, and reduced effective bandwidth. NCCL all-reduce and related collectives are synchronized operations, so one delayed flow or rank can slow the entire job. Transceiver firmware mismatches are serious and can contribute to instability, but the direct condition most damaging to bandwidth is actual packet loss. A 400G port running at 70% utilization is not automatically a problem if traffic is balanced and lossless behavior is maintained.
Jitter below 5 ps with consistent latency is not a severe issue; it suggests stable timing behavior. In post- deployment troubleshooting, engineers should inspect switch counters, RoCE congestion statistics, ECN/PFC behavior, retransmissions, packet drops, and NCCL logs to determine whether the fabric is causing the observed latency spikes.
NEW QUESTION # 121
During a 48-hour NeMo question-answering model burn-in test, GPU memory errors occur when processing large datasets. Which configuration strategy prevents Out-of-Memory (OOM) errors while maintaining processing efficiency?
Answer: C
Explanation:
NVIDIA NeMo and large language model (LLM) training workloads are extremely demanding onHBM (High Bandwidth Memory). Out-of-Memory (OOM) errors often occur not because the total dataset is too large, but because memory fragmentation or sudden spikes in allocation (spikes during data shuffling or batch loading) exceed the available GPU memory. To mitigate this during intensive burn-in tests, engineers utilize theRMM (RAPIDS Memory Manager)library, which provides an asynchronous allocator. Enabling RMM asynchronous allocation allows the system to pre-allocate a pool of memory and manage it more efficiently than the standard CUDA allocator, reducing the overhead of constant allocations and deallocations.
Furthermore, setting a specific blocksize (e.g., 1GB) for data loading ensures that the data ingestion pipeline reads data in manageable, deterministic chunks. This prevents the system from attempting to load massive files entirely into memory at once, which is the primary cause of OOMs in question-answering tasks involving large Parquet or JSON datasets. Switching to FP32 (Option B) would actually double the memory footprint and increase the likelihood of an OOM error.
NEW QUESTION # 122
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