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| Section | Weight | Objectives |
|---|---|---|
| Security and Best Practices | 10-15% | - Security fundamentals - Operational best practices - Compliance considerations |
| Deployment and Configuration | 25-30% | - Cluster configuration - Network configuration - System installation and setup - Software deployment |
| Monitoring and Management | 15-20% | - Troubleshooting basics - NVIDIA management tools - Performance monitoring - Resource utilization |
| AI Infrastructure Fundamentals | 15-20% | - NVIDIA software stack overview - Data center infrastructure requirements - GPU architecture basics - AI and Deep Learning concepts |
| NVIDIA AI Infrastructure Components | 25-30% | - NVIDIA networking solutions (Mellanox) - NVIDIA DGX systems - NVIDIA AI Enterprise software - Storage solutions for AI workloads |
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NEW QUESTION # 103
Which function is used to collect the cluster counters information?
Answer: D
Explanation:
The correct answer is PM, which refers to the Performance Manager or performance-management function used for collecting performance counter information from the InfiniBand fabric. In NVIDIA AI infrastructure, cluster counters are essential for troubleshooting and optimization because they expose link-level and port- level behavior across switches, HCAs, and fabric paths. These counters can include transmitted and received data, symbol errors, link errors, congestion indicators, XmitWait, packet drops, and other telemetry used to detect performance degradation. SM, or Subnet Manager, is responsible for fabric discovery, LID assignment, routing, and maintaining the logical InfiniBand subnet, but it is not primarily the performance-counter collection function. GM and FM are not the standard answers for collecting cluster counters in this context.
NVIDIA UFM Telemetry and diagnostic tooling collect and monitor InfiniBand port statistics such as bandwidth, congestion, errors, and latency, while UFM diagnostic output also includes PM counter dumps. In AI clusters, these counters help identify cabling faults, congestion, degraded links, and issues that can reduce NCCL and RDMA performance.
NEW QUESTION # 104
An A1 server is exhibiting unusually high CPU utilization during a GPU-accelerated workload. How can you determine if the CPU is becoming a bottleneck, preventing the GPUs from achieving their full potential?
Answer: E
Explanation:
All the options provide valid methods for identifying a CPU bottleneck. Monitoring CPU and GPU utilization, profiling the application, and running parallel benchmarks all help to determine if the CPU is limiting GPU performance.
NEW QUESTION # 105
You are leading a project to enhance the energy efficiency of a data center that heavily relies on AI workloads. NVIDIA suggests moving beyond traditional metrics like Power Usage Effectiveness (PUE) to better capture the efficiency of modern data centers. Which strategy should you prioritize to develop more accurate energy-efficiency metrics?
Answer: D
Explanation:
The best strategy is to use workload-specific benchmarks such as MLPerf-style AI benchmarks to understand energy efficiency in real-world scenarios. NVIDIA has argued that traditional PUE is not enough for modern AI data centers because PUE measures facility overhead relative to IT power, but it does not measure useful computational output. For AI infrastructure, the important question is not only how much power the facility consumes, but how much useful AI work is completed per unit of energy. NVIDIA's discussion of next- generation efficiency metrics emphasizes useful work per energy and the need to account for real applications.
Kilowatt-hours are useful for measuring energy consumed, but they do not by themselves capture productive AI output. Watts-used is only instantaneous power and does not reflect completed work. PUE remains useful for facilities management, but relying on it as the primary metric misses the performance and efficiency characteristics of accelerated computing. Workload-specific benchmarks allow teams to compare training, inference, and system performance against energy consumed in practical AI operations.
NEW QUESTION # 106
You are monitoring a server with 8 GPUs used for deep learning training. You observe that one of the GPUs reports a significantly lower utilization rate compared to the others, even though the workload is designed to distribute evenly. 'nvidia-smi' reports a persistent "XID 13" error for that GPU. What is the most likely cause?
Answer: D
Explanation:
XID 13 errors in 'nvidia-smi' typically indicate a hardware fault within the GPU. Driver bugs or memory issues would likely cause different error codes or system instability across multiple GPUs. CUDA version mismatch might prevent the application from running altogether, but is less likely to lead to a specific XID error on a single GPU. Exclusive Process mode will lead to it being used by a different process but not necessarily cause that XID error.
NEW QUESTION # 107
An AI server utilizes a QSFP28 transceiver with MPO connector. During troubleshooting, you suspect a faulty transceiver. Which steps are most important to perform when physically inspecting and testing the transceiver?
Answer: E
Explanation:
The MOST important steps involve visual inspection for damage, cleaning the connector to ensure a good optical connection, and verifying DOM information to assess the transceivers health and signal levels. OTDR is more suited for cable diagnosis. While firmware and voltage are relevant, the DOM provides immediate health indicators.
NEW QUESTION # 108
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