Practice Free NCP-AII Exam Online Questions
You are troubleshooting a network performance issue in your NVIDIA Spectrum-X based A1 cluster. You suspect that the Equal-Cost Multi-Path (ECMP) hashing algorithm is not distributing traffic evenly across available paths, leading to congestion on some links.
Which of the following methods would be MOST effective for verifying and addressing this issue?
- A . Use ‘ping’ or ‘traceroute’ to analyze the paths taken by packets between the affected nodes. If they always take the same path, ECMP is likely not working correctly.
- B . Use switch telemetry tools (e.g., NVIDIA What’s Up Gold, Mellanox NEO, or similar) to monitor link utilization across all available paths between the nodes. Look for significant imbalances in traffic volume.
- C . Restart the switches to force the ECMP hashing algorithm to recalculate paths.
- D . Disable ECMP entirely and rely solely on static routing.
- E . Reduce the TCP window size.
B
Explanation:
Switch telemetry tools provide the most direct and comprehensive way to monitor link utilization and identify imbalances in traffic distribution caused by ECMP. While ‘ping’ and ‘traceroute’ can provide path information, they don’t give insight into traffic volume. Restarting the switches might temporarily alleviate the issue but doesn’t address the underlying problem with the ECMP hashing. Disabling ECMP is a last resort and can reduce overall bandwidth.
Consider a scenario where you’re using GPUDirect Storage to enable direct memory access between GPUs and NVMe drives. You observe that while GPUDirect Storage is enabled, you’re not seeing the expected performance gains.
What are potential reasons and configurations you should check to ensure optimal GPUDirect Storage performance? Select all that apply.
- A . Verify that the NVMe drives are properly configured in a RAID 0 configuration.
- B . Ensure that the NVMe drives are connected to the system via PCle Gen4 or Gen5.
- C . Confirm that the CUDA driver version is compatible with GPIJDirect Storage.
- D . Check if the file system supports direct I/O (e.g., using ‘directio’ mount option).
- E . Disable CPU-side caching to force all I/O operations to go directly to the GPU memory.
B,C,D
Explanation:
GPUDirect Storage requires PCle Gen4/Gen5 for sufficient bandwidth (B). The CUDA driver must be compatible with GPUDirect Storage (C). Direct I/O support in the file system is essential to bypass the OS cache and allow direct GPU access (D). RAID 0 (A) is about storage speed but not directly related to GDS functionality. Disabling CPU-side caching (E) is usually detrimental as it can reduce overall system performance. Note, this is not always bad but needs to be tested depending on application.
After installing NGC CLI using pip, you encounter ‘ngc’ command not found error even though pip install reported successful.
What can be the cause?
- A . The python executable where NGC CLI got installed is not in the system PATH.
- B . The NGC CLI installation was corrupted. Run ‘pip install ―force-reinstall nvidia-cli’
- C . The shell needs to be reloaded or a new terminal session initiated for PATH changes to take effect.
- D . NGC CLI only works inside Docker containers.
- E . The host’s operating system is not supported by NGC CLI.
A,C
Explanation:
The most common reason the ‘ngc’ command isn’t found is that the python environment’s executable path isn’t in the system PATH (A). A quick fix to ensure environment variables are updated in your current shell is to reload the shell or start a new session (C).
You’re deploying a distributed training workload across multiple NVIDIAAIOO GPUs connected with NVLink and InfiniBand.
What steps are necessary to validate the end-to-end network performance between the GPUs before running the actual training job? (Select all that apply)
- A . Run NCCL tests (e.g., to measure NVLink bandwidth and latency between GPUs on the same node.
- B . Use ‘ibstat’ to verify the status and link speed of the InfiniBand interfaces on each node.
- C . Employ ‘iperf3’ or ‘nc’ to measure TCP/UDP bandwidth between nodes over the InfiniBand network.
- D . Ping all nodes to confirm basic network connectivity
- E . Manually inspect the physical cabling of NVLink bridges and InfiniBand connections.
A,B,C
Explanation:
Validating end-to-end network performance requires checking NVLink performance within nodes (NCCL tests), verifying InfiniBand interface status Cibstat), and measuring inter-node bandwidth using tools like ‘iperf3’ or ‘ne. Pinging is a basic connectivity check, but doesn’t assess bandwidth. Physical cabling is important to visually inspect, however ibstat also shows the active link and cabling errors.
You are implementing a distributed deep learning training setup using multiple servers connected via NVLink switches. You want to ensure optimal utilization of the NVLink interconnect.
Which of the following strategies would be MOST effective in achieving this goal?
- A . Configure NCCL to use GPUDirect RDMA for inter-GPU communication across servers.
- B . Use a standard TCP/IP socket connection for inter-GPU communication across servers.
- C . Implement a data compression algorithm that can be processed by the CPU before sending data over NVLink.
- D . Disable peer-to-peer GPU memory access within each server to avoid contention.
- E . Increase the batch size to reduce the frequency of inter-GPU communication.
A,E
Explanation:
Configuring NCCL to use GPUDirect RDMA is key as it bypasses the CPU and reduces latency. Increasing batch size helps amortize the cost of communication. TCP/IP will not utilize NVLink; CPU-based compression adds overhead, and disabling peer-to-peer access hurts performance. GPUDirect RDMA allows direct memory access and it is the most critical piece in NVLink switch based system setup. Large batch size would also help to saturate the link bandwidth and less freqeuntly communicate between GPUs.
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.
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.
You’ve successfully deployed BlueField OS to your SmartNlC. You need to verify that the Mellanox Ethernet driver (mlx5) is loaded and functioning correctly.
What command would you use to confirm this?
A )
B )
C )
D )
E )
- A . Option A
- B . Option B
- C . Option C
- D . Option D
- E . Option E
C
Explanation:
The ‘Ismod’ command lists loaded kernel modules. ‘grep mlx5 filters the output to show if the Mellanox Ethernet driver is loaded. ‘Ispci’ lists PCl devices, ‘ifconfig’ shows network interfaces but not necessarily the driver, and ‘ethtool’ displays driver information for a specific interface.
You have an NVIDIAAIOO GPU and need to configure it for optimal performance across two distinct AI workloads: a large language model (LLM) training job and a computer vision inference service. The LLM benefits from maximum memory bandwidth, while the inference service requires low latency and high throughput.
Which MIG configuration would best suit this scenario?
- A . Create two 7g.80gb MIG instances, one for each workload.
- B . Create one 14g.160gb MIG instance for the LLM and use CUDA MPS to multiplex the inference service.
- C . Create a single full-GPU instance and use Kubernetes resource quotas to isolate the workloads.
- D . Create one log. 120gb instance for the LLM and one 4g.40gb instance for inference.
- E . Utilize Time-Slicing on a single full-GPU instance, allocating specific time slots to each workload using NVIDIA Vgpu technology
D
Explanation:
Creating a log. 120gb instance for the memory-intensive LLM and a 4g.40gb instance for the inference service provides dedicated resources that cater to the specific needs of each workload, without the overhead or limitations of CUDA MPS or Kubernetes resource quotas.
Option A is too conservative, potentially limiting the LLM performance.
Option B sacrifices dedicated resources for inference, which may hurt latency.
Option C does not leverage MIG and does not guarantee resource isolation and performance consistency.
Option E introduces complexities associated with Time-Slicing and might not be suitable for real-time processing.
Which of the following storage technologies are most suitable for storing large training datasets used in deep learning, considering both performance and cost?
- A . High-performance NVMe SSDs in a local RAID configuration
- B . SATA HDDs in a network-attached storage (NAS) configuration
- C . Object storage (e.g., AWS S3, Azure Blob Storage) accessed directly from the training nodes
- D . A parallel file system (e.g., BeeGFS, Lustre) deployed on NVMe SSDs
- E . Tape backup systems
D
Explanation:
NVMe SSDs in a local RAID offer high performance and relatively low latency, making them suitable for data that needs to be accessed quickly. Parallel file systems deployed on NVMe SSDs provide the highest performance and scalability, especially for large datasets accessed concurrently by multiple training nodes. Object storage can be used for initial data ingest or archival but is generally slower than local or parallel file systems for training. SATA HDDs and Tape backup systems are a low performing option for this case.
An InfiniBand fabric is experiencing intermittent packet loss between two high-performance compute nodes. You suspect a faulty cable or connector.
Besides physically inspecting the cables, what software-based tools or techniques can you employ to diagnose potential link errors contributing to this packet loss?
- A . Use ‘ibdiagnet’ to perform a comprehensive fabric analysis, including link integrity checks and error detection.
- B . Monitor the port counters on the InfiniBand switches connected to the compute nodes. Look for excessive CRC errors, symbol errors, or other link-related error counts.
- C . Run ‘ipeff or ‘ibperf between the two compute nodes and analyze the reported packet loss rate. Correlate this with the error counters on the switches.
- D . All of the above
- E . Disable port mirroring.
D
Explanation:
All of the mentioned options are valid techniques for diagnosing link errors. ‘ibdiagnet’ performs a thorough fabric analysis. Monitoring switch port counters reveals link-level errors. ‘iperf/ibperf identifies packet loss, which can be correlated with switch error counters. A comprehensive approach combining these methods is most effective.