NCP-AII NVIDIA Certified Professional AI Infrastructure PDF Questions

1. NVIDIA NCP-AII Exam NVIDIA Certified Professional AI Infrastructure https://www.passquestion.com/ncp-aii.html 35% OFF on All, Including NCP-AII Questions and Answers Pass NVIDIA NCP-AII Exam with PassQuestion NCP-AII questions and answers in the first attempt. https://www.passquestion.com/ 1 / 9

2. 1.A GPU in your AI server consistently overheats during inference workloads. You’ve ruled out inadequate cooling and software bugs. Running ‘nvidia-smi’ shows high power draw even when idle. Which of the following hardware issues are the most likely causes? A. Degraded thermal paste between the GPU die and the heatsink. B. A failing voltage regulator module (VRM) on the GPU board, causing excessive power leakage. C. Incorrectly seated GPU in the PCle slot, leading to poor power delivery. D. A BIOS setting that is overvolting the GPU. E. Insufficient system RAM. Answer: A,B,C Explanation: Degraded thermal paste loses its ability to conduct heat effectively. A failing VRM can cause excessive power draw and heat generation. An incorrectly seated GPU can cause instability and poor power delivery, leading to overheating. Overvolting in BIOS will definitely cause overheating. While insufficient RAM can cause performance issues, it is less likely to lead to overheating. 2.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? A. A driver bug causing incorrect workload distribution. B. Insufficient system memory preventing data transfer to that GPU. C. A hardware fault within the GPU, such as a memory error or core failure. D. An incorrect CUDA version installed. E. The GPU’s compute mode is set to ‘Exclusive Process’. Answer: C 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. 3.You notice that one of the fans in your GPU server is running at a significantly higher RPM than the others, even under minimal load. ipmitool sensor’ output shows a normal temperature for that GPU. What could be the potential causes? A. The fan’s PWM control signal is malfunctioning, causing it to run at full speed. B. The fan bearing is wearing out, causing increased friction and requiring higher RPM to maintain airflow. C. The fan is attempting to compensate for restricted airflow due to dust buildup. D. The server’s BMC (Baseboard Management Controller) has a faulty temperature sensor reading, causing it to overcompensate. E. A network connectivity issue is causing higher CPU utilization, leading to increased system-wide heat. Answer: A,B,C Explanation: 2 / 9

3. A malfunctioning PWM control signal, worn fan bearings, or restricted airflow can all cause a fan to run at higher RPMs. While a faulty BMC sensor could be a cause, the question states that ‘ipmitool sensor’ shows a normal temperature. Network connectivity issues are less likely to cause an isolated fan to run high, if the GPU temperature is normal. 4.After upgrading the network card drivers on your A1 inference server, you experience intermittent network connectivity issues, including packet loss and high latency. You’ve verified that the physical connections are secure. Which of the following steps would be most effective in troubleshooting this issue? A. Roll back the network card drivers to the previous version. B. Check the system logs for error messages related to the network card or driver. C. Run network diagnostic tools like ‘ping’, ‘traceroute’, and ‘iperf3’ to assess the network performance. D. Reinstall the operating system. E. Update the server’s BIOS. Answer: A,B,C Explanation: Rolling back drivers is a quick way to revert to a known working state. Checking system logs will provide valuable information about driver errors or network issues. Network diagnostic tools will quantify the network performance and help isolate the problem. Reinstalling the OS is drastic and should be a last resort. Updating the BIOS is unlikely to resolve driver-related network issues unless specifically recommended for the network card. 5.Your deep learning training job that utilizes NCCL (NVIDIA Collective Communications Library) for multi-GPU communication is failing with "NCCL internal error, unhandled system error" after a recent CUDA update. The error occurs during the ‘all reduce’ operation. What is the most likely root cause and how would you address it? A. Incompatible NCCL version with the new CUDA version. Update NCCL to a version compatible with the installed CUDA version. B. Insufficient shared memory allocated to the CUDA context. Increase the shared memory limit using ‘cudaDeviceSetLimit(cudaLimitSharedMemory, new_limity. C. Firewall rules blocking inter-GPU communication. Configure the firewall to allow communication on the NCCL-defined ports (typically 8000-8010). D. Faulty network cables used for inter-node communication (if the training job spans multiple servers). Replace the network cables with certified high-speed cables. E. GPU Direct RDMA is not properly configured. Check ‘dmesg’ for errors and ensure RDMA is enabled. Answer: A Explanation: NCCL relies on specific CUDA versions. An incompatibility after a CUDA update is the most probable cause. Insufficient shared memory is less likely to cause a system error within NCCL. Firewall rules usually manifest as connection refused errors. Faulty network cables affect inter-node communication, not intra-node. While RDMA issues can cause problems, they typically don’t present as ‘unhandled system error’ immediately after a CUDA update, and are more likely if RDMA was working previously. 6.You are deploying a new A1 inference service using Triton Inference Server on a multi-GPU system. 3 / 9

4. After deploying the models, you observe that only one GPU is being utilized, even though the models are configured to use multiple GPUs. What could be the possible causes for this? A. The model configuration file does not specify the ‘instance_group’ parameter correctly to utilize multiple GPUs. B. The Triton Inference Server is not configured to enable CUDA Multi-Process Service (MPS). C. Insufficient CPU cores are available for the Triton Inference Server, limiting its ability to spawn multiple inference processes. D. The models are not optimized for multi-GPU inference, resulting in a single GPU bottleneck. E. The GPUs are not of the same type and Triton cannot properly schedule across them. Answer: A,B Explanation: The ‘instance_group’ parameter in the model configuration dictates how Triton distributes the model across GPUs. Without proper configuration, it may default to a single GPIJ. CUDA MPS allows multiple CUDA applications (in this case, Triton inference processes) to share a single GPU, improving utilization. Insufficient CPU cores or non-optimized models could limit performance, but wouldn’t necessarily restrict usage to a single GPIJ. While dissimilar GPIJs can affect performance, Triton will attempt to schedule across them if configured correctly. 7.An A1 server exhibits frequent kernel panics under heavy GPU load. ‘dmesg’ reveals the following error: ‘NVRM: Xid (PCl:0000:3B:00): 79, pid=..., name=..., GPU has fallen off the bus.’ Which of the following is the least likely cause of this issue? A. Insufficient power supply to the GPIJ, causing it to become unstable under load. B. A loose or damaged PCle riser cable connecting the GPU to the motherboard. C. A driver bug in the NVIDIA drivers, leading to GPU instability. D. Overclocking the GPU beyond its stable limits. E. A faulty CPU. Answer: E Explanation: The error message GPU has fallen off the bus strongly suggests a hardware-related issue with the GPU’s connection to the motherboard or its power supply. Insufficient power, a loose riser cable, driver bugs and overclocking can all lead to this. A faulty CPU, while capable of causing system instability, is less directly related to the GPIJ falling off the bus and therefore the least likely cause in this specific scenario. 8.You are using GPU Direct RDMA to enable fast data transfer between GPUs across multiple servers. You are experiencing performance degradation and suspect RDMA is not working correctly. How can you verify that GPU Direct RDMA is properly enabled and functioning? A. Check the output of ‘nvidia-smi topo -m’ to ensure that the GPUs are connected via NVLink and have RDMA enabled. B. Examine the ‘cimesg’ output for any errors related to RDMA or InfiniBand drivers. C. Use the ‘ibstat command to verify that the InfiniBand interfaces are active and connected. D. Run a bandwidth benchmark using a tool like or to measure the RDMA throughput. E. Ping the other servers to ensure network connectivity. Answer: B,C,D 4 / 9

5. Explanation: ‘dmesg’ will show errors during RDMA driver initialization. Sibstat’ confirms the InfiniBand interface status. Benchmarking with or validates the actual RDMA throughput. ‘nvidia-smi topo -m’ shows the topology but not necessarily active RDMA. Pinging only verifies basic network connectivity, not RDMA functionality. 9.You’re debugging performance issues in a distributed training job. ‘nvidia-smi’ shows consistently high GPU utilization across all nodes, but the training speed isn’t increasing linearly with the number of GPUs. Network bandwidth is sufficient. What is the most likely bottleneck? A. Inefficient data loading and preprocessing pipeline, causing GPUs to wait for data. B. NCCL is not configured optimally for the network topology, leading to high communication overhead. C. The learning rate is not adjusted appropriately for the increased batch size across multiple GPUs. D. The global batch size has exceeded the optimal point for the model, reducing per-sample accuracy and slowing convergence. E. CUDA Graphs is not being utilized. Answer: A,B,C,D Explanation: If GPUs are highly utilized but scaling is poor, the bottleneck is likely not GPU compute itself. Inefficient data pipelines mean GPUs spend time idle waiting for data. Suboptimal NCCL configurations result in communication overhead negating the benefit of more GPUs. Incorrect learning rate with larger batch size will impact covergence. Batch sizes can affect convergence and model effectiveness. While CUDA Graphs improves performance, the other answers are more pertinent to the question. 10.You have a server equipped with multiple NVIDIA GPUs connected via NVLink. You want to monitor the NVLink bandwidth utilization in real-time. Which tool or method is the most appropriate and accurate for this? A. Using ‘nvidia-smi’ with the ‘—display=nvlink’ option. B. Parsing the output of *nvprof during a representative workload. C. Utilizing DCGM (Data Center GPU Manager) with its NVLink monitoring capabilities. D. Monitoring network interface traffic using ‘iftop’ or ‘tcpdump’ . E. Using ‘gpustat’ . Answer: C Explanation: DCGM is specifically designed for monitoring and managing GPUs in data centers, including detailed NVLink statistics in real time. ‘nvidia-smi —display=nvlink’ provides a snapshot, not real-time data. ‘nvprof is a profiling tool and not ideal for continuous monitoring. ‘iftop’ and ‘tcpdump’ monitor network traffic, not NVLink. ‘gpustat’ does not offer the granular NVLink data of DCGM. 11.Your A1 inference server utilizes Triton Inference Server and experiences intermittent latency spikes. Profiling reveals that the GPU is frequently stalling due to memory allocation issues. Which strategy or tool would be least effective in mitigating these memory allocation stalls? A. Using CIJDA memory pools to pre-allocate memory and reduce allocation overhead during inference requests. 5 / 9

6. B. Enabling CUDA graph capture to reduce kernel launch overhead. C. Reducing the model’s memory footprint by using quantization or pruning techniques. D. Increasing the GPU’s TCC (Tesla Compute Cluster) mode priority. E. Optimize the model using TensorRT. Answer: D Explanation: CUDA memory pools directly address memory allocation overhead. CUDA graph capture reduces kernel launch overhead, which can indirectly reduce memory pressure. Model quantization/pruning reduces the overall memory footprint. Optimizing using TensorRT reduces memory footprint. Increasing TCC priority primarily affects preemption behavior and doesn’t directly address memory allocation issues. Therefore it will have less impact than others. 12.You are tasked with troubleshooting a performance bottleneck in a multi-node, multi-GPU deep learning training job utilizing Horovod. The training loss is decreasing, but the overall training time is significantly longer than expected. Which of the following monitoring approaches would provide the most insight into the cause of the bottleneck? A. Using ‘nvidia-smi’ on each node to monitor GPU utilization and memory usage. B. Enabling Horovod’s timeline and profiling features to visualize the communication patterns and identify synchronization bottlenecks. C. Monitoring network bandwidth utilization on each node using ‘iftop’ or ‘iperf3’ D. Analyzing the training loss curve to identify potential issues with the model architecture or hyperparameters. E. Using Shtop’ to monitor CPIJ utilization on each node. Answer: B Explanation: Horovod’s timeline and profiling tools are specifically designed to visualize communication patterns and identify bottlenecks in distributed training jobs. While ‘nvidia-smr and network monitoring can provide useful information, they don’t give the holistic view of communication overhead that Horovod’s tools provide. Loss curve analysis helps with model-related issues, not distributed training bottlenecks. ‘htop’ isn’t related to network or GPU specific issues in distributed processing. 13.You’re troubleshooting a DGX-I server exhibiting performance degradation during a large-scale distributed training job. ‘nvidia-smü shows all GPUs are detected, but one GPU consistently reports significantly lower utilization than the others. Attempts to reschedule orkloads to that GPU frequently result in CUDA errors. Which of the following is the MOST likely cause and the BEST initial roubleshooting step? A. A driver issue affecting only one GPU; reinstall NVIDIA drivers completely. B. A software bug in the training script utilizing that specific GPU’s resources inefficiently; debug the training script. C. A hardware fault with the GPU, potentially thermal throttling or memory issues; run ‘nvidia-smi -i -q’ to check temperatures, power limits, and error counts. D. Insufficient cooling in the server rack; verify adequate airflow and cooling capacity for the rack. E. Power supply unit (PSU) overload, causing reduced power delivery to that GPU; monitor PSU load and 6 / 9

7. check PSU specifications. Answer: C Explanation: While all options are possibilities, the consistently lower utilization and CUDA errors point strongly to a hardware fault. Running nvidia-smi -i -q’ provides detailed telemetry data, including temperature, power limits, and ECC error counts, which are crucial for diagnosing GPU hardware issues. 14.An A1 inferencing server, using NVIDIA Triton Inference Server, experiences intermittent crashes under peak load. The logs reveal CUDA out-of-memory errors (00M) despite sufficient system RAM. You suspect a GPU memory leak within one of the models. Which strategy BEST addresses this issue? A. Increase the system RAM to accommodate the growing memory footprint. B. Implement CUDA memory pooling within the Triton Inference Server configuration to reuse memory allocations efficiently. C. Reduce the batch size and concurrency of the offending model in the Triton configuration. D. Upgrade the GPUs to models with larger memory capacity. E. Disable other models running on the same GPU to free up memory. Answer: B,C Explanation: Options B and C directly address the 00M issue. CUDA memory pooling enables efficient reuse of GPU memory, minimizing allocations and deallocations. Reducing batch size and concurrency decreases the memory footprint of the model, alleviating the pressure on GPU memory. While upgrading GPUs (D) is a solution, it is more costly than optimizing the current configuration. Increasing system RAM (A) does not solve GPU memory issues. Disabling other models (E) reduces load but doesn’t address the core problem of the memory leak in the first place. 15.You are managing a cluster of GPU servers for deep learning. You observe that one server consistently exhibits high GPU temperature during training, causing thermal throttling and reduced performance. You’ve already ensured adequate airflow. Which of the following actions would be MOST effective in addressing this issue? A. Reduce the ambient temperature of the data center. B. Lower the GPU power limit using ‘nvidia-smi —power-limit*. C. Update the NVIDIA drivers to the latest version. D. Re-seat the GPU in its PCle slot to ensure proper contact and heat dissipation. E. Increase the fan speed of the GPU cooler using ‘nvidia-smi --fan’. Answer: D,E Explanation: Re-seating the GPU (D) ensures a proper connection between the GPU and the motherboard, which is crucial for effective heat dissipation. Increasing fan speed (E) can directly improve cooling. Lowering the power limit (B) reduces temperature but also reduces performance. Updating drivers (C) may help in some cases, but it is less likely to solve a thermal throttling problem. Lowering the ambient temperature (A) is generally beneficial but might not be specific enough to fix the overheating issue on a single server. 16.A DGX A100 server with dual power supplies reports a critical power event in the BMC logs. One PSU 7 / 9

8. shows a ‘degraded’ status, while the other appears normal. What immediate actions should you take to ensure continued operation and prevent data loss? A. Immediately shut down the server gracefully to prevent further damage to the faulty PSIJ. B. Hot-swap the degraded PSU with a replacement unit. C. Monitor the remaining PSU’s load and temperature closely; if stable, continue operation until a scheduled maintenance window. D. Reduce the GPU power limit using ‘nvidia-smi’ to decrease the overall power consumption of the server. E. Migrate all workloads to other servers in the cluster to minimize the impact of a potential complete PSU failure. Answer: B,E Explanation: Hot-swapping the degraded PSU (B) restores redundancy. Migrating workloads (E) minimizes the risk of data loss or service interruption if the remaining PSU fails. Shutting down the server (A) causes unnecessary downtime if hot-swapping is possible. Monitoring the remaining PSU (C) is a good practice, but it’s not a replacement for restoring redundancy or mitigating risk. Reducing GPU power limits (D) may help prevent further strain but is a temporary solution that impacts performance. 17.You are running a distributed training job on a multi-GPU server.After several hours, the job fails with a NCCL (NVIDIA Collective Communications Library) error. The error message indicates a failure in inter-GPU communication. ‘nvidia-smi’ shows all GPUs are healthy. What is the MOST probable cause of this issue? A. A bug in the NCCL library itself; downgrade to a previous version of NCCL. B. Incorrect NCCL configuration, such as an invalid network interface or incorrect device affinity settings. C. Insufficient inter-GPU bandwidth; reduce the batch size to decrease communication overhead. D. A faulty network cable connecting the server to the rest of the cluster. E. Driver incompatibility issue between NCCL and the installed NVIDIA driver version. Answer: B,E Explanation: NCCL errors during inter-GPU communication often stem from configuration issues (B) or driver incompatibilities (E). Incorrect network interface or device affinity settings can prevent proper communication. Driver versions might not fully support the NCCL version being used. Reducing batch size (C) might alleviate symptoms but doesn’t address the root cause. A faulty network cable (D) would likely cause broader network issues beyond NCCL. Downgrading NCCL (A) is a potential workaround but not the ideal first step. 18.Your AI infrastructure includes several NVIDIAAI 00 GPUs. You notice that the GPU memory bandwidth reported by ‘nvidia-smi’ is significantly lower than the theoretical maximum for all GPUs. System RAM is plentiful and not being heavily utilized. What are TWO potential bottlenecks that could be causing this performance issue? A. Insufficient CPU cores assigned to the training process. B. Inefficient data loading from storage to GPU memory. C. The GPUs are connected via PCle Gen3 instead of PCle Gen4. D. The CPU is using older DDR4 memory with low bandwidth 8 / 9

9. E. The NVIDIA drivers are not configured to enable peer-to-peer memory access between GPUs. Answer: B,C Explanation: Inefficient data loading (B) can starve the GPUs, preventing them from reaching their full memory bandwidth potential. If the storage system or data pipeline is slow, the GPUs will spend time waiting for data. PCle Gen3 (C) has lower bandwidth than PCle Gen4, limiting the data transfer rate to the GPUs. While insufficient CPU cores (A) can be a bottleneck, it’s less directly related to GPU memory bandwidth. Driver configuration (E) affects inter-GPU communication, not the memory bandwidth of individual GPUs. CPU’s RAM type does not directly impact GPIJ memory bandwidth(D). 19.You suspect a faulty NVIDIA ConnectX-6 network adapter in a server used for RDMA-based distributed training. Which commands or tools can you use to diagnose potential issues with the adapter’s hardware and connectivity? A. Ispci -v to verify the adapter is detected and its resources are allocated correctly. B. ibstat to check the adapter’s status, link speed, and active ports. C. ethtool to examine the adapter’s Ethernet settings and statistics. D. ping to test basic network connectivity. E. nvsmimonitord to monitor GPU metrics and detect anomalies. Answer: A,B,C,D Explanation: All options except E are relevant for diagnosing network adapter issues. ‘Ispci -v’ (A) verifies hardware detection. ‘ibstat’ (B) checks InfiniBand-specific details. ‘ethtoor (C) examines Ethernet settings. ‘ping’ (D) tests basic connectivity. ‘nvsmimonitord’ (E) focuses on GPU monitoring, not network adapters. 20.An AI server with 8 GPUs is experiencing random system crashes under heavy load. The system logs indicate potential memory errors, but standard memory tests (memtest86+) pass without any failures. The GPUs are passively cooled. What are the THREE most likely root causes of these crashes? A. Incompatible NVIDIA driver version with the installed Linux kernel. B. GPIJ memory errors that are not detectable by standard CPU-based memory tests. C. Insufficient airflow within the server, leading to overheating of the GPUs and VRMs. D. A faulty power supply unit (PSU) that is unable to provide stable power under peak load. E. Network congestion causing intermittent data corruption during distributed training. Answer: B,C,D Explanation: GPU memory errors (B) are a strong possibility, as CPU-based tests don’t test GPU memory directly. Insufficient airflow (C) is likely due to the passive cooling, leading to thermal instability. A faulty PSU (D) can cause random crashes under load due to power fluctuations. Driver incompatibility (A) is less likely to cause random crashes after initial setup, and network congestion (E) usually results in training slowdowns rather than system crashes. 9 / 9