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NEW QUESTION # 29
You are deploying a multi-tenant AI infrastructure where different users or groups have isolated network environments using VXLAN. Which of the following is the MOST important consideration when configuring the VTEPs (VXLAN Tunnel Endpoints) on the hosts to ensure proper network isolation and performance?
- A. Using the same IP address for all VTEPs to simplify routing.
- B. Using the default MTU size of 1500 bytes for VXLAN traffic.
- C. Disabling multicast routing to prevent broadcast traffic.
- D. Ensuring that each tenant has a unique VXLAN Network Identifier (VNI) to isolate their traffic.
- E. Using the same VNI for all tenants to maximize network utilization.
Answer: D
Explanation:
The most critical aspect of VXLAN configuration for multi-tenancy is ensuring that each tenant has a unique VNI. The VNI is a 24-bit identifier that segments the VXLAN network into isolated logical networks. Using unique VNIs guarantees that traffic from one tenant remains isolated from other tenants, preventing data leakage and ensuring security. Using overlapping or shared VNIs would defeat the purpose of VXLAN- based isolation.
NEW QUESTION # 30
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 Kubernetes nodes are not labeled correctly to indicate the presence of NVIDIA GPUs. Use 'kubectl label node nvidia.com/gpu.present=true'.
- B. 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.
- C. The NVIDIA Container Toolkit is not installed on the Kubernetes nodes. Install the toolkit according to NVIDIA's documentation.
- D. The NVIDIA drivers are not properly installed on the host operating system before installing the GPU Operator. Verify the driver installation using 'nvidia-smr.
- E. 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.
Answer: A,C,D,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 # 31
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. 'modinfo nvidia'
- D. 'nvidia-smr
- E. 'cat /proc/driver/nvidia/version'
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 # 32
A BlueField-3 DPUis configured to run both control plane and data plane functions. After a recent software update, you notice that the data plane performance has significantly degraded, but the control plane remains responsive. What is the MOST likely cause, assuming the update didn't introduce any code bugs, and what is the BEST approach to diagnose this issue?
- A. Firmware corruption; Re-flash the BlueField DPIJ with the latest firmware image.
- B. Power throttling; Check the DPU's power consumption and thermal status via the BMC.
- C. Network misconfiguration; Verify the MTU and QOS settings on the network interfaces.
- D. Driver incompatibility; Downgrade the Mellanox OFED drivers to the previous version.
- E. Resource contention; Use 'perf or 'bpftrace' to profile the data plane processes and identify resource bottlenecks (CPU, memory, cache).
Answer: E
Explanation:
Resource contention is the MOST likely cause, assuming no code bugs. The update may have increased the resource demands of either the control or data plane, leading to contention. Profiling the data plane processes with 'perf or 'bpftrace' helps pinpoint the bottlenecks. Downgrading drivers or reflashing firmware are more drastic steps to take after confirming resource contention isn't the issue.
NEW QUESTION # 33
A data center is designed for A1 training with a high degree of east-west traffic. Considering cost and performance, which network topology is generally the most suitable?
- A. Three-Tier
- B. Bus
- C. Spine-Leaf
- D. Ring
- E. Mesh
Answer: C
Explanation:
Spine-Leaf architecture is designed to handle high-bandwidth, low-latency traffic patterns characteristic of AI training. It provides a non-blocking fabric with equal cost paths between any two servers, making it ideal for east-west communication. Three-Tier is more suited for traditional applications with north-south traffic. Ring and Bus are less scalable and perform poorly under heavy load. Mesh is complex and expensive for large-scale deployments.
NEW QUESTION # 34
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?
- A. Visually inspect the connector for damage, clean the connector end-face with an appropriate fiber optic cleaning tool, and perform an Optical Time Domain Reflectometer (OTDR) test.
- B. Check the transceiver's firmware version, measure the transceiver's temperature using an infrared thermometer, and perform a bit error rate test (BERT).
- C. Visually inspect the connector for damage, clean the connector end-face with an appropriate fiber optic cleaning tool, and verify the transceiver's DOM (Digital Optical Monitoring) information, particularly TX power, RX power, and temperature.
- D. Examine the transceiver's EEPROM data, measure the supply voltage with a multimeter, and perform a link speed auto-negotiation test.
- E. Verify the transceiver's DOM (Digital Optical Monitoring) information, clean the connector end-face with a dry cloth, and perform a ping test.
Answer: C
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 # 35
You are running a distributed training job across multiple nodes, using a shared file system for storing training dat a. You observe that some nodes are consistently slower than others in reading data. Which of the following could be contributing factors to this performance discrepancy? Select all that apply.
- A. Different CPU architectures on the nodes.
- B. Insufficient RAM on the slower nodes for caching data.
- C. Variations in the speed of the local temporary storage (e.g., /tmp) used for intermediate files.
- D. Uneven data distribution across the storage nodes.
- E. Network congestion between the slower nodes and the storage system.
Answer: B,D,E
Explanation:
Network congestion (A) can directly impact the data transfer rate between the slower nodes and the storage. IJneven data distribution (B) means some storage nodes are more heavily loaded, leading to slower response times for nodes accessing data on those overloaded nodes. Insufficient RAM (D) limits the amount of data that can be cached locally, forcing more frequent reads from the slower storage system. CPU architecture (C) primarily affects compute performance, not 1/0. The speed of /tmp (E) is relevant if the training job uses local storage extensively for temporary files, but the question focuses on reading training data from the shared file system.
NEW QUESTION # 36
Consider a scenario where you are setting up a high-performance computing cluster with several GPU-accelerated nodes using Slurm as the resource manager. You want to ensure that jobs requesting GPUs are only scheduled on nodes with the appropriate NVIDIA drivers and CUDA toolkit installed. How can you achieve this within Slurm?
- A. Use Slurm's 'GresTypeS configuration option in 'slurm.conf to define a generic resource type called 'gpu' and then configure each node to advertise the available GPIJs. Slurm will automatically ensure that jobs requesting GPUs are only scheduled on nodes with the 'gpu' resource.
- B. Use Slurm's node features to tag nodes with the "Feature=' keyword in 'slurm.conf. For example, tag nodes with GPUs as 'Feature=gpu' . Jobs can then request nodes with the 'gpu' feature using the option.
- C. Create a custom Slurm script that checks for the presence of the NVIDIA driver and CUDA toolkit before submitting a job to a node. If the requirements are not met, the job is rejected.
- D. Utilize Slurm's Prolog and Epilog scripts to dynamically install the necessary NVIDIA drivers and CUDA toolkit on each node before and after a job runs. This ensures that the required software is always available.
- E. Install the NVIDIA Data Center GPU Manager (DCGM) on each node and configure Slurm to query DCGM for GPU availability and health. Slurm will then only schedule jobs on healthy and available GPUs.
Answer: B
Explanation:
Using Slurm's node features is the most straightforward and recommended approach for tagging nodes with specific capabilities. The '-constraint' option allows jobs to request nodes with particular features. GresTypeS can be used, but node features provide more flexibility and control. Installing drivers dynamically is impractical and inefficient. DCGM is primarily for monitoring, not core scheduling requirements.
NEW QUESTION # 37
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. Check if the file system supports direct I/O (e.g., using 'directio' mount option).
- B. Verify that the NVMe drives are properly configured in a RAID 0 configuration.
- C. Disable CPU-side caching to force all I/O operations to go directly to the GPU memory.
- D. Confirm that the CUDA driver version is compatible with GPIJDirect Storage.
- E. Ensure that the NVMe drives are connected to the system via PCle Gen4 or Gen5.
Answer: A,D,E
Explanation:
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.
NEW QUESTION # 38
A cluster administrator is preparing to update the firmware on a DGX H100 system, including the GPU tray (baseboard). What is the correct sequence of steps to perform a safe and successful firmware upgrade?
- A. Update the GPU tray first, then the motherboard tray, and reboot the BMC after all updates are complete.
- B. Perform a cold reset, stop all GPU activity, update and reboot the BMC, update motherboard and tray components, and verify completion.
- C. Stop all GPU activity, update and reboot the BMC, update motherboard and tray components, perform a cold reset, and verify completion.
- D. Update the BMC and skip the GPU tray and motherboard tray updates if the system appears healthy.
Answer: C
Explanation:
Updating firmware on an NVIDIA DGX H100 is a multi-stage process that requires careful orchestration to prevent hardware corruption. The first and most critical step is to ensure no workloads are running (stopping all GPU activity) to avoid conflicts during the flashing process. The standard NVIDIA procedure begins with updating and rebooting theBaseboard Management Controller (BMC). This is because the BMC manages the power sequencing and communication for all other trays; having the latest management logic active is a prerequisite for the subsequent steps. Once the BMC is updated and back online, the administrator proceeds with the motherboard and GPU tray updates. However, these updates are staged in flash memory and often do not "take effect" until the hardware undergoes acold reset(removing power completely). This physical or logical power cycle forces the various CPLDs and silicon root-of-trust modules to boot from the newly written firmware images. Finally, the administrator must verify completion using tools like nvsm show health or the BMC dashboard to ensure all components report the target versions and a "Healthy" status. Skipping the BMC update first (Option C) or the cold reset (Option B) can lead to mismatched firmware states that may cause system instability or boot failures.
NEW QUESTION # 39
You have a large dataset stored on a network file system (NFS) and are training a deep learning model on an AMD EPYC server with NVIDIA GPUs. Data loading is very slow. What steps can you take to improve the data loading performance in this scenario? Select all that apply.
- A. Mount the NFS share with the 'nolock' option.
- B. Switch to a parallel file system like Lustre or BeeGFS.
- C. Increase the number of NFS client threads on the AMD EPYC server.
- D. Use a local SSD or NVMe drive to cache frequently accessed data.
- E. Reduce the batch size to decrease the amount of data loaded per iteration.
Answer: B,C,D
Explanation:
Increasing NFS client threads enables more concurrent data access. Caching frequently accessed data on a local SSD/NVMe drive reduces network I/O. Switching to a parallel file system provides higher bandwidth and lower latency compared to NFS. 'nolock' can improve performance but sacrifices data consistency. Reducing batch size reduces the amount of data loaded but doesn't address the underlying NFS bottleneck.
NEW QUESTION # 40
You are validating the environment of an NVIDIA GPU-accelerated data center during post-deployment checks. Which one action is essential to confirm that power and cooling are sufficient for the stable operation of NVIDIA DGX H100 systems?
- A. Review the system BIOS to ensure GPU overclocking is enabled for maximum performance.
- B. Verify that each DGX system is connected to redundant, properly rated PDUs and that all power supplies are reporting nominal input.
- C. Use NVSM to disable unused PCIe devices to reduce overall system heat output.
- D. Confirm the system fans are running at 100% under all workloads to prevent overheating.
Answer: B
Explanation:
Stable operation of high-density AI infrastructure like the DGX H100 requires strict adherence to power and thermal specifications. A single DGX H100 system can draw up to10.2kWunder peak load. Therefore, the most essential validation step is ensuring the electrical "infrastructure-to-server" handoff is healthy. This involves verifying that the system is connected to redundant PDUs (Power Distribution Units) capable of handling the amperage requirements without tripping breakers. UsingNVSM (NVIDIA System Management), an administrator must check that all six power supplies (PSUs) are functional and receiving nominal input voltage (typically 200V-240V). If a PSU reports sub-optimal input or a "Loss of Redundancy," the system may throttle performance or shut down unexpectedly during a heavy training run. Fans running at
100% (Option A) at all times would actually indicate an inefficient or failed cooling policy, as fans should dynamically scale based on thermals. Overclocking (Option B) is not supported or recommended for enterprise DGX systems, as they are already factory-tuned for the highest stable performance.
NEW QUESTION # 41
A systems administrator is preparing a new DGX server for deployment. What is the most secure approach to configuring the BMC port during initial setup?
- A. Enable remote access to the BMC over the internet using the default admin credentials for initial troubleshooting.
- B. Leave the BMC port disconnected until after the operating system is fully configured and in production.
- C. Connect the BMC port directly to the production network and retain default admin credentials for convenience.
- D. Connect the BMC port to a dedicated and firewalled network and change the default admin credentials.
Answer: D
Explanation:
The Baseboard Management Controller (BMC) is a powerful tool that allows for total control over the DGX system, including the ability to flash firmware, cycle power, and access the serial console. Because of this, it is a high-value target for security threats. The "100% verified" secure approach (Option D) involves two critical layers:
* Network Isolation: The BMC port should never be exposed to the public internet (Option A) or even the general production network (Option B). It must reside on adedicated Out-of-Band (OOB) network that is firewalled and accessible only to authorized administrators.
* Credential Management: Standard NVIDIA factory defaults (like admin/admin) must be changed immediately upon first access. As part of the DGX first-boot wizard, the system prompts the administrator to create a strong, unique password for the primary user, which is then synchronized to the BMC.
Leaving the port disconnected (Option C) is unfeasible for modern data center operations, as the BMC is required for remote monitoring and "headless" deployment. Following the isolated/firewalled approach ensures the AI Factory remains resilient against both external attacks and internal lateral movement.
NEW QUESTION # 42
You are tasked with implementing a monitoring solution for power consumption and thermal performance in an NVIDIA-powered Ai cluster. You want to collect data from the Baseboard Management Controllers (BMCs) of the servers using Redfish. Which of the following Python code snippets demonstrates the correct approach for authenticating with the BMC and retrieving power and temperature readings?
- A.
- B.
- C.
- D.
- E. None of the above. Redfish requires specialized hardware and cannot be accessed directly via Python.
Answer: C
Explanation:
Option A provides a valid example using the 'redfish' library to connect to a BMC, authenticate, and retrieve power and temperature readings. It uses the correct Redfish API structure to access the relevant data. Option B uses 'ipmitoor' , which is another valid approach but less modern than Redfish. Option C uses 'pyghmi.ipmi', which is an older IPMI library. Option D is incorrect; Redfish can be accessed via Python. Option E is nonsense; Redfish is not an email protocol.
NEW QUESTION # 43
Which configuration file should be modified to blacklist the 'nouveau' driver on a system running systemd to prevent conflicts with the NVIDIA driver?
- A. /etc/modprobe.d/blacklist-nouveau.conf
- B. /boot/grub/grub.cfg
- C. /etc/systemd/system.conf
- D. /etc/default/grub
- E. /etc/modules
Answer: A
Explanation:
The file '/etc/modprobe.d/blacklist-nouveau.conf is the standard location to blacklist the 'nouveau' driver. This prevents it from loading and conflicting with the NVIDIA driver. "/etc/modules' is a list of modules to load at boot, but not for blacklisting. '/boot/grub/grub.cfg' is the GRUB configuration file, not for module blacklisting. S/etc/systemd/system.conf is the systemd configuration file, not module related. 'letc/default/grub' is where you configure Grub settings and update GRUB, but it is not the right location for blacklisting modules.
NEW QUESTION # 44
You are designing a large-scale AI training cluster spanning multiple racks. The networking topology necessitates both short-reach (within rack) and long-reach (inter-rack) connections. Which combination of cable types and transceivers is MOST cost-effective and suitable for this scenario, assuming a mix of 200GbE and 400GbE links?
- A. AOC cables for all connections, both intra-rack and inter-rack.
- B. Passive copper cables for all intra-rack connections and ER4 transceivers with single-mode fiber for all inter-rack connections.
- C. DAC cables for all intra-rack connections and FR4 transceivers with single-mode fiber for inter-rack connections.
- D. DAC cables for all intra-rack connections and AOC cables for all inter-rack connections.
- E. DAC cables for all intra-rack connections, and a mix of SR4 and LR4 transceivers with multimode and single-mode fiber, respectively, for inter-rack connections.
Answer: E
Explanation:
DAC cables are cost-effective and suitable for short-reach, high-bandwidth connections within a rack. For inter-rack connections, SR4 transceivers with multimode fiber (for shorter inter-rack links) and LR4 transceivers with single-mode fiber (for longer inter-rack links) provide a good balance of cost and performance. AOCs are generally more expensive than DACs. ER4 is overkill for many inter-rack scenarios and is more expensive than LR4.
NEW QUESTION # 45
A team is validating a DGX BasePOD deployment. Using cmsh, they run a command to check GPU health across all nodes. What indicates that the system is ready for AI workloads?
- A. Only the head node's GPUs need to be healthy.
- B. The command output is ignored if the system powers on without errors.
- C. All GPUs report Status_Health = OK and Health = OK for each device.
- D. At least half of the GPUs report Status_Health = OK.
Answer: C
Explanation:
In an NVIDIA DGX BasePOD or SuperPOD environment, "Cluster Health" is a binary state: either the entire fabric and all compute resources are ready, or the cluster is considered degraded. Using the Bright Cluster Manager (BCM) shell (cmsh), administrators can aggregate telemetry from every node in the cluster. For a system to be considered "Production Ready," every single GPU across the multi-node deployment must report a status of Health = OK. This verification ensures that the hardware is communicating correctly over the PCIe bus, the NVLink fabric is initialized, and no ECC (Error Correction Code) memory errors are present. If even a single GPU in a 32-node cluster is unhealthy, collective communication libraries like NCCL may hang or experience significant performance penalties during "All-Reduce" operations, as the entire job typically scales to the speed of the slowest/unhealthiest component. Therefore, seeing Status_Health = OK for every device is the mandatory exit criterion for the bring-up phase.
NEW QUESTION # 46
Consider the following 'ibroute' command used on an InfiniBand host: 'ibroute add dest Oxla dev ib0'. What is the MOST likely purpose of this command?
- A. To add a default route for all traffic destined outside the InfiniBand subnet.
- B. To disable routing on the ib0 interface.
- C. To configure a static route for traffic destined to IP address Ox1a, using the InfiniBand interface ib0.
- D. To create a static route for traffic destined to LID Ox1a, using the InfiniBand interface ib0.
- E. To configure the MTU size on the ib0 interface to Ox1a bytes.
Answer: D
Explanation:
The 'ibroute add dest Ox1a dev ibC command creates a static route for traffic destined for the InfiniBand LID (Local Identifier) Ox1a, using the InfiniBand interface named 'ib0'. InfiniBand routing is primarily based on LIDS, not IP addresses directly (though IP over 1B is possible). The 'dest' parameter specifies the destination LID.
NEW QUESTION # 47
What is the role of GPUDirect RDMA in an NVLink Switch-based system, and how does it improve performance?
- A. It provides a mechanism for GPUs to offload compute-intensive tasks to the CPU, improving overall system throughput.
- B. It encrypts data transmitted between GPUs, enhancing security.
- C. It enables direct communication between GPUs and storage devices, bypassing the network interface.
- D. It allows GPUs to directly access each other's memory without involving the CPIJ, reducing latency and CPU overhead.
- E. It facilitates the virtualization of GPUs, allowing multiple virtual machines to share a single physical GPIJ.
Answer: D
Explanation:
GPUDirect RDMA enables direct memory access between GPUs, bypassing the CPU and reducing latency. This significantly improves performance for applications that require frequent data transfers between GPUs. Other options describe functionalities that are not associated with RDMA in this context.
NEW QUESTION # 48
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. Parsing the output of *nvprof during a representative workload.
- B. Using 'gpustat' .
- C. Monitoring network interface traffic using 'iftop' or 'tcpdump' .
- D. Utilizing DCGM (Data Center GPU Manager) with its NVLink monitoring capabilities.
- E. Using 'nvidia-smi' with the '-display=nvlink' option.
Answer: D
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.
NEW QUESTION # 49
You are setting up a multi-node A1 cluster with NVIDIA GPUs and InfiniBand for inter-node communication. You need to ensure the InfiniBand network is functioning optimally for GPU-accelerated workloads. What steps would you take to validate the InfiniBand installation and performance?
- A. Verify the InfiniBand drivers are installed and then run a standard TCP benchmark between the nodes.
- B. Run 'ibstat' to check InfiniBand interface status, use 'ping' to test connectivity, and rely on NCCL's internal checks during training.
- C. Configure a static IP address on the InfiniBand interfaces, and rely on the operating system's network diagnostics.
- D. Use 'nvidia-smi' to monitor InfiniBand traffic, and rely on CUDA-aware MPl for communication validation.
- E. Run 'ibstat' to check InfiniBand interface status, use 'ibping' and 'ibperf to test latency and bandwidth, and verify correct NCCL configuration (e.g., during a distributed training run.
Answer: E
Explanation:
Sibstat' verifies interface status. 'ibping' and 'ibperf are InfiniBand-specific tools for latency and bandwidth testing. NCCL (NVIDIA Collective Communications Library) is critical for distributed training, and provides valuable diagnostic information. The other options are either incomplete or rely on tools not specific to InfiniBand.
NEW QUESTION # 50
A data scientist reports slow data loading times when training a large language model. The data is stored in a Ceph cluster. You suspect the client-side caching is not properly configured. Which Ceph configuration parameter(s) should you investigate and potentially adjust to improve data loading performance? Select all that apply.
- A. mds cache size
- B. fuse_client_max_background
- C. client cache size
- D. client quota
Answer: B,C
Explanation:
Client-side caching in Ceph is primarily controlled by 'client cache size' which determines the amount of memory the Ceph client uses for caching data. 'mds cache size' controls the metadata server cache size, impacting metadata operations. controls the maximum number of background requests a FUSE client can make, influencing concurrency. affects the number of threads used by the OSDs, not the client-side caching, and 'client quota' limits storage usage, not caching.
NEW QUESTION # 51
You are replacing a faulty NVIDIA Tesla V 100 GPU in a server. After physically installing the new GPU, the system fails to recognize it. You've verified the power connections and seating of the card. Which of the following steps should you take next to troubleshoot the issue?
- A. Check if the new GPU requires a different driver version than the currently installed one and update if needed.
- B. Reinstall the operating system to ensure proper driver installation.
- C. Immediately RMA the new GPU as it is likely defective.
- D. Update the system BIOS and BMC firmware to the latest versions.
- E. Disable and re-enable the GPU slot in the system BIOS.
Answer: A,D
Explanation:
After verifying the physical installation, the next steps are to ensure the system's firmware is up-to-date and that the correct drivers are installed. Older BIOS/BMC firmware may not properly recognize newer GPUs, and incorrect drivers will prevent the GPU from functioning correctly. RMAing the new GPU or reinstalling the OS prematurely are inefficient troubleshooting steps. The system BIOS may have an option to disable and enable the GPU slot, but that would be rare.
NEW QUESTION # 52
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Updated NVIDIA Study Guide NCP-AII Dumps Questions: https://www.itexamdownload.com/NCP-AII-valid-questions.html
Dumps Questions [2026] Pass for NCP-AII Exam: https://drive.google.com/open?id=1AyCJ5QkunmSMEHet-sYTKswDsEMiZBsE