BEST Verified NVIDIA NCP-AII Exam Questions (2026)
The Best Practice Test Preparation for the NCP-AII Certification Exam
NVIDIA NCP-AII Exam Syllabus Topics:
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NEW QUESTION # 19
After deploying BlueField OS, you notice that the network interfaces are not automatically configured with IP addresses. Which of the following actions would be the MOST appropriate first step to troubleshoot this issue?
- A. Re-flash the Bluefield OS image
- B. Reinstall the Mellanox OFED drivers. A corrupted driver installation could cause network configuration issues.
- C. Restart the networking service using 'systemctl restart networking'.
- D. Manually assign static IP addresses to the interfaces using the 'ifconfig' command.
- E. Check the DHCP client configuration to ensure it is enabled and properly configured to request IP addresses. Examine the logs for any errors.
Answer: E
Explanation:
In most modern systems, network interfaces are automatically configured using DHCP. Therefore, the first step is to check if the DHCP client is enabled and configured correctly. If DHCP fails, then other troubleshooting steps, such as static IP assignment or driver reinstallation, can be considered.
NEW QUESTION # 20
A BlueField-2 DPU is being used to accelerate storage access via NVMe-oF. You notice high CPU utilization on the host server, despite the DPU. What is the MOST likely cause and the MOST effective initial troubleshooting step?
- A. The host is still handling a significant portion of the 1/0 processing due to incorrect offload settings; Check the NVMe-oF initiator settings and verify that offload is enabled.
- B. Incorrect RoCE configuration; Verify RoCE version and MTU settings using 'roce_util'.
- C. Incorrect NVMe-oF target configuration on the storage array; Check target NQN and discovery settings.
- D. Firmware mismatch between the host NIC driver and the BlueField DPU firmware; Upgrade both to the latest compatible versions.
- E. Insufficient memory allocated to the BlueField DPIJ; Increase memory allocation via the BMC.
Answer: A
Explanation:
The MOST likely cause of high host CPU utilization with a DPIJ accelerating NVMe-oF is that the host is still handling significant I/O processing. The initial step is to verify NVMe-oF initiator offload settings to ensure the DPIJ is actually handling the workload. Checking target configuration or RoCE, while important, wouldn't directly address high host CPU if the offload isn't working in the first place.
NEW QUESTION # 21
You are managing a cluster of servers with NVIDIA GPUs. You want to automate the process of installing and updating NVIDIA drivers across all nodes. Which of the following approaches would be the most scalable and maintainable?
- A. Distribute the driver installation package via NFS and have each server run the installation script locally.
- B. Manually SSH into each server and run the driver installation commands.
- C. Use NVIDIA's container toolkit to deploy driver containers to each node.
- D. Create a custom script that uses 'pdsh' or 'parallel' to execute driver installation commands on all servers simultaneously.
- E. Use a configuration management tool like Ansible, Chef, or Puppet to automate the driver installation and update process.
Answer: C,E
Explanation:
Configuration management tools (Ansible, Chef, Puppet) provide the most scalable and maintainable solution. They allow you to define the desired state of the system and automatically enforce it across all nodes. NVIDIA's container toolkit encapsulates the driver and provides a consistent, isolated environment. Manually SSHing is not scalable. While 'pdsh' and 'parallel' can automate the execution of commands, they lack the robust error handling and state management of configuration management tools. NFS distribution requires managing the installation process separately on each server.
NEW QUESTION # 22
Which of the following commands or tools can be used to verify the NVIDIA driver version and the CUDA version installed on a Linux system?
- A. nvcc -version'
- B. "Ispci I grep NVIDIA'
- C. 'cat /proc/driver/nvidia/version'
- D. 'modinfo nvidia'
- E. 'nvidia-smr
Answer: A,C,D,E
Explanation:
'nvidia-smi' provides detailed information about the NVIDIA driver version and GPU status. 'nvcc -version' shows the CUDA compiler version. 'cat Iproc/driver/nvidia/version' (if the file exists) displays the driver version. 'modinfo nvidia' will display the version of the loaded kernel module. only shows the presence of NVIDIA hardware, not the driver or CUDA version.
NEW QUESTION # 23
A network engineer is tasked with configuring the management, storage, and compute networks for a new DGX BasePOD deployment. Which statement best describes the network segmentation required for optimal operation?
- A. Four networks: compute, storage, out-of-band, and management.
- B. Two networks: one for management and one for compute.
- C. A single VLAN for all types of network traffic.
Answer: A
Explanation:
NVIDIA DGX BasePOD and SuperPOD reference architectures mandate strict network segmentation to ensure performance, security, and manageability.
* Compute Network: Typically InfiniBand (or high-speed Spectrum-X Ethernet), dedicated solely to GPU-to-GPU collective communications (NCCL).
* Storage Network: A high-bandwidth Ethernet or InfiniBand fabric specifically for data ingestion and model checkpointing, often utilizing GPUDirect Storage (GDS).
* Management Network: Used for standard cluster administration, SSH, and software orchestration (e.
g., Bright Cluster Manager or Kubernetes control plane traffic).
* Out-of-Band (OOB) Network: A physically isolated network connected to the BMC ports for low- level system monitoring, power control, and remote console access, even when the OS is down.
A single VLAN (Option A) would cause massive congestion during training, as storage and management traffic would compete with high-frequency compute packets. The four-network model ensures that a "storm" in the storage fabric does not prevent an administrator from accessing the system via the management or OOB networks, which is essential for maintaining an AI Factory at scale.
NEW QUESTION # 24
You are deploying BlueField OS in a highly secure environment. Which of the following security measures are MOST important to consider during and after the OS deployment?
- A. Implementing a strict password policy for all user accounts.
- B. Configuring a strong firewall to restrict network access to only necessary services.
- C. Regularly updating the BlueField OS and all installed software to patch security vulnerabilities.
- D. Enabling secure boot to ensure only trusted code is executed during the boot process.
- E. Disabling unnecessary services and ports to reduce the attack surface.
Answer: A,B,C,D,E
Explanation:
All listed options contribute significantly to the security posture. Secure boot ensures boot integrity, a firewall restricts unauthorized network access, regular updates patch vulnerabilities, disabling unnecessary services minimizes the attack surface, and a strong password policy protects user accounts. All these are critical in a secure environment.
NEW QUESTION # 25
After NCCL burn-in reports "transport retry count exceeded," which corrective action addresses the underlying fabric issue?
- A. Reduce message size to decrease network utilization
- B. Switch from Ring to Tree algorithms via NCCL_ALGO=TREE
- C. Inspect InfiniBand link quality metrics (BER, symbol errors) and replace faulty cables
- D. Increase NCCL_IB_TIMEOUT to tolerate longer latencies
Answer: C
Explanation:
In an InfiniBand-based AI cluster, the "transport retry count exceeded" error is a critical indicator of physical layer instability within the fabric. When the NVIDIA Collective Communications Library (NCCL) attempts to move data between GPUs across nodes, it relies on the Reliable Connection (RC) transport of InfiniBand. If a packet is corrupted or lost due to signal integrity issues, the hardware attempts to retransmit the data. If the number of failed retransmissions exceeds the hardware-defined "Retry Count," the connection is torn down, and NCCL reports this error. While software workarounds like increasing timeouts (Option C) might mask the symptom briefly, they do not fix the root cause: a failing physical component. The verified troubleshooting procedure involves using tools like ibqueryerrors, ibdiagnet, or mlxlink to inspect the link quality metrics, specifically looking for high Bit Error Rates (BER) and Symbol Errors. These errors are typically caused by dirty fiber connectors, poorly seated transceivers, or marginal DAC cables that cannot maintain the signal-to- noise ratio required for 200G (HDR) or 400G (NDR) speeds. Replacing the identified faulty cable or cleaning the optics is the only way to restore the "lossless" environment required for high-performance AI training.
NEW QUESTION # 26
A large A1 model is training using a dataset stored on a network-attached storage (NAS) device. The data transfer speeds are significantly lower than expected. After initial troubleshooting, you discover that the MTU (Maximum Transmission Unit) size on the network interfaces of the training server and the NAS device are mismatched. The server is configured with an MTIJ of 1500, while the NAS device is configured with an MTU of 9000 (Jumbo Frames). What is the MOST likely consequence of this MTU mismatch, and what action should you take?
- A. The server will be unable to communicate with the NAS device. Reduce the MTU size on the server to match the MTU size of the NAS device.
- B. Data packets will be fragmented, leading to increased overhead and reduced performance. Configure both the server and the NAS device to use the same MTU size (either 1500 or 9000).
- C. The connection between the server and the NAS device will be unreliable, resulting in data corruption. Increase the MTU size on both devices to the maximum supported value.
- D. Data packets will be retransmitted, increasing the latency but still getting the full throughput. Configure the server to use Path MTU Discovery (PMTUD).
- E. The data transfer will be limited to the lowest common MTU size, but there will be no significant performance impact. No action is required.
Answer: B
Explanation:
An MTU mismatch (option A) will cause fragmentation, where larger packets are broken down into smaller packets before being transmitted, adding overhead and reducing performance. The solution is to configure both devices to use the same MTU size. Choosing 1500 ensures compatibility, while 9000 requires the entire network path to support jumbo frames.
NEW QUESTION # 27
After upgrading your NVIDIA drivers on a system with multiple GPUs, 'nvidia-smu reports 'No devices were found'. You've verified that the GPUs are physically connected correctly. What are the most likely causes and corresponding solutions?
- A. The driver installation was interrupted or corrupted. Solution: Reinstall the driver, ensuring no errors during the process.
- B. The NVIDIA kernel modules failed to load. Solution: Rebuild the kernel modules using DKMS and reboot.
- C. The X server is interfering with the driver. Solution: Stop the X server (e.g., 'sudo systemctl stop gdm3' or 'sudo systemctl stop lightdm') before running 'nvidia-smi'.
- D. The user lacks necessary permissions. Solution: Add the user to the 'video' group.
- E. The NVIDIA driver is incompatible with the installed CUDA toolkit. Solution: Downgrade or upgrade the CUDA toolkit to match the driver's compatibility requirements.
Answer: A,B
Explanation:
The most common causes are failure to load the kernel modules, often due to upgrade issues requiring a DKMS rebuild and reboot, or a corrupted installation requiring reinstallation. User permissions and CUDA toolkit version are less common in this scenario where no devices are found. While stopping the X server can sometimes help, it's not the primary solution if 'nvidia-smri' can't find the GPUs at all.
NEW QUESTION # 28
You are configuring a Mellanox InfiniBand network for a DGXAIOO cluster. What is the RECOMMENDED subnet manager for a large, high-performance A1 training environment, and why?
- A. OpenSM, because it's the default and easiest to configure.
- B. UFM (Unified Fabric Manager), because it provides advanced management, monitoring, and optimization capabilities.
- C. Any subnet manager; the performance difference is negligible.
- D. IBA management tools that ship with the OS (e.g., 'ibnetdiscover').
- E. A custom-built subnet manager using the InfiniBand verbs API.
Answer: B
Explanation:
UFM is the recommended subnet manager for large A1 training environments using DGX systems. It offers advanced features like real-time monitoring, congestion control, adaptive routing, and telemetry, which are crucial for maximizing performance and stability in demanding workloads. OpenSM lacks these advanced features and is not suitable for large, performance-critical clusters.
NEW QUESTION # 29
A user reports that their deep learning training job is crashing with a 'CUDA out of memory' error, even though 'nvidia-smi' shows plenty of free memory on the GPU. The job uses TensorFlow. What are the TWO most likely causes?
- A. The CUDA VISIBLE DEVICES environment variable is not set correctly.
- B. TensorFlow is fragmenting GPU memory, making it difficult to allocate contiguous blocks.
- C. TensorFlow is allocating memory on the CPU instead of the GPU.
- D. The TensorFlow version is incompatible with the installed NVIDIA driver.
- E. The system's swap space is full, preventing memory from being allocated.
Answer: A,B
Explanation:
'CUDA out of memory errors, despite seemingly available GPU memory, often indicate memory fragmentation or improper GPU assignment. TensorFlow can fragment GPU memory, leading to allocation failures even if sufficient total memory is available. The variable controls which GPUs TensorFlow can access. If it's not set or is set incorrectly, TensorFlow might be trying to allocate memory on a non-existent or unavailable GPU. While TensorFlow version incompatibilities can cause issues, they are less likely to directly manifest as 'CUDA out of memory' errors. TensorFlow typically prioritizes GPU memory allocation if configured correctly.
NEW QUESTION # 30
You are deploying a multi-node A1 training cluster using Kubernetes, with each node equipped with multiple NVIDIA GPUs. You want to ensure that the Kubernetes scheduler is aware of the GPU resources available on each node and can efficiently allocate GPU-enabled pods to the appropriate nodes. Besides installing the NVIDIA Container Toolkit, what other components are essential for enabling GPU-aware scheduling in Kubernetes?
- A. The Kubernetes Resource Quota controller.
- B. The Kubernetes Horizontal Pod Autoscaler (HPA).
- C. The NVIDIA GPU Operator.
- D. The NVIDIA Fabric Manager
- E. The NVIDIA Device Plugin for Kubernetes.
Answer: C,E
Explanation:
The NVIDIA Device Plugin for Kubernetes (A) is essential for advertising the GPU resources to the Kubernetes scheduler. It allows Kubernetes to understand that GPUs are available and track their usage. The NVIDIA GPU Operator (C) simplifies the deployment and management of NVIDIA drivers and other components required for GPU support in Kubernetes, including the device plugin. The Resource Quota controller (B) is useful for limiting resource consumption but doesn't directly enable GPU-aware scheduling. HPA (D) is used for autoscaling based on CPU or memory utilization, not GPU utilization. Fabric Manager is for managing GPU interconnect and not related.
NEW QUESTION # 31
You are managing an A1 infrastructure based on NVIDIA Spectrum-X switches. A new application requires strict Quality of Service (QOS) guarantees for its traffic. Specifically, you need to ensure that this application's traffic receives preferential treatment and minimal latency.
What combination of Spectrum-X features and configurations would be MOST effective in achieving this?
- A. Configure DiffServ Code Point (DSCP) marking on the application's traffic, map these DSCP values to specific traffic classes within the Spectrum-X switch, and configure Weighted Fair Queueing (WFQ) or Strict Priority Queueing on the egress ports.
- B. Enable broadcast storm protection.
- C. Use VLAN tagging to isolate the application's traffic into a separate virtual network.
- D. Increase the MTIJ size on all interfaces to reduce packet fragmentation and overall latency.
- E. Disable Adaptive Routing (AR) to ensure that traffic always takes the shortest path.
Answer: A
Explanation:
DSCP marking, traffic class mapping, and WFQ/Strict Priority Queueing are fundamental QOS mechanisms. DSCP marking allows you to classify traffic based on application requirements. Traffic classes within the switch provide different levels of service. WFQ and Strict Priority Queueing ensure that high-priority traffic receives preferential treatment on egress ports. The other options are less relevant to QOS guarantees for a specific application.
NEW QUESTION # 32
You are tasked with installing the latest NVIDIA driver on a server running Ubuntu 22.04 for A1 workloads. You have downloaded the driver package 'NVIDIA-Linux-x86 64-535.104.05.run'. Before installation, what is the most critical step to ensure a smooth process, assuming secure boot is enabled?
- A. Create a DKMS module and sign the driver with a machine owner key (MOK) for Secure Boot compatibility.
- B. Simply run the ' .run' file using 'sudo ./NVlDlA-Linux-x86_64-535.104.05.run' .
- C. Disable Secure Boot in the BIOS before installing the driver.
- D. Install the driver using 'apt install nvidia-driver-535' and let the system handle Secure Boot automatically.
- E. Ensure the 'nouveau- driver is blacklisted by adding 'blacklist nouveau- to 'letc/modprobe.d/blacklist-nouveau.conf.
Answer: A,E
Explanation:
Secure Boot requires kernel modules to be signed. Directly running the .run' file or disabling Secure Boot are generally not recommended. DKMS and MOK signing allows the driver to be validated by the system. Blacklisting nouveau ensures that it won't conflict with the NVIDIA driver. Installing via apt doesn't guarantee Secure Boot compatibility without further steps, so the best approach involves DKMS/MOK and blacklisting.
NEW QUESTION # 33
An AI infrastructure uses a combination of air-cooled and liquid-cooled NVIDIA GPUs. You want to optimize cooling performance based on the specific thermal characteristics of each GPU type and their location within the server rack. How can you achieve granular cooling control and monitoring to address these heterogeneous cooling requirements effectively? SELECT TWO.
- A. Implement rack-level airflow management solutions, such as blanking panels and cable management, to improve overall airflow uniformity.
- B. Deploy per-server cooling solutions with independent fan control for each server node, allowing for tailored airflow adjustments.
- C. Implement dynamic fan speed control based on individual GPU temperatures, leveraging tools like 'nvidia-smi' and custom scripts, for air-cooled GPUs.
- D. Use a centralized monitoring system to track GPU temperatures and power consumption, but apply the same cooling profile to all GPUs regardless of type.
- E. Employ liquid cooling only for the highest TDP GPUs and rely on ambient air cooling for all other components.
Answer: A,C
Explanation:
Implementing rack-level airflow management (A) improves overall airflow uniformity, which benefits all GPUs, regardless of cooling type. Implementing dynamic fan speed control based on individual GPU temperatures for air-cooled GPUs (E) allows for fine-grained adjustments to cooling performance. Per-server cooling solutions (C) can be helpful, but less scalable/practical in most datacenters. Using the same cooling profile for all GPUs (B) is ineffective. Cooling only high TDP GPUs (D) may not be sufficient.
NEW QUESTION # 34
A financial services firm is deploying an AI model for fraud detection that requires rapid inference and data retrieval across multiple sites. Which feature should their storage system prioritize?
- A. High capacity with moderate speed.
- B. Tape backup systems.
- C. Low-cost HDD solutions.
- D. Multi-protocol data access with low latency.
Answer: D
Explanation:
Fraud detection in financial services is a real-time AI workload. The system must ingest transaction data, retrieve historical customer profiles, and perform inference in milliseconds. This requires a storage architecture that supportsmulti-protocol access(such as S3 for ingestion and POSIX/NFS for inference engines) combined withlow latency. In these environments, storage latency directly impacts the "Time to Decision". An All-Flash storage tier is mandatory, as traditional HDD solutions (Option D) or moderate speed systems (Option B) introduce "Tail Latency" that can cause the fraud detection model to time out during peak transaction windows. Additionally, multi-site synchronization ensures that the latest model weights and historical data are available across different geographic data centers for high availability and localized inference.
NEW QUESTION # 35
You've installed a server with multiple NVIDIAAIOO GPUs intended for use with Kubernetes and NVIDIA's GPU Operaton After installing the GPU Operator, you notice that the GPUs are not being properly detected and managed by Kubernetes. Which of the following are potential causes and troubleshooting steps you should take?
- A. The NVIDIA Container Toolkit is not installed on the Kubernetes nodes. Install the toolkit according to NVIDIA's documentation.
- B. The NVIDIA drivers are not properly installed on the host operating system before installing the GPU Operator. Verify the driver installation using 'nvidia-smr.
- C. The GPU Operator's configuration is incorrect, preventing it from properly discovering and managing the GPUs. Check the GPU Operator's logs and configuration files.
- D. The 'nvidia-docker2 runtime is not set as the default runtime in '/etc/docker/daemon.json' . Change the default runtime to 'nvidia' and restart the Docker daemon.
- E. The Kubernetes nodes are not labeled correctly to indicate the presence of NVIDIA GPUs. Use 'kubectl label node nvidia.com/gpu.present=true'.
Answer: A,B,C,E
Explanation:
All the options are valid reasons. The NVIDIA driver must be present on the host, the nodes need to be labelled to be recongnized by the Kubernetes, container tookit is required for running GPU enabled container and configuration of GPU operator must be correct.
NEW QUESTION # 36
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