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NEW QUESTION # 29
Refer to the exhibit. Which type of NVIDIA LinkX cable has a teal color code?
Answer: B
Explanation:
NVIDIA LinkX teal-colored cables indicate active optical cables. AOCs use optical fiber with active circuitry in the cable ends to support high-speed, longer-distance data center interconnects.
NEW QUESTION # 30
Which of the following methods are considered the most reliable ways to install NVIDIA drivers on a production server running a stable Linux distribution (e.g., RHEL, CentOS, or Ubuntu LTS) to minimize downtime and ensure system stability?
Answer: A,C,E
Explanation:
Using the distribution's package manager is generally the safest and most reliable method for installing NVIDIA drivers. This approach ensures that dependencies are managed correctly and updates are handled through the system's standard update mechanisms. Containerization isolates the driver and application dependencies. Nvidia's data center driver program provides enterprise-grade support and reliability. Running the .run installer directly can sometimes lead to dependency issues and conflicts. Building from source is complex and not generally recommended for production environments.
NEW QUESTION # 31
An engineer needs to verify the current firmware versions of all components (ATF, BSP, NIC, UEFI) on a BlueField-3 DPU's BMC. Which Redfish API command provides this information?
Answer: C
Explanation:
The Redfish UpdateService/FirmwareInventory endpoint is used to retrieve firmware inventory details from the BlueField DPU BMC, including installed firmware versions for components such as ATF, BSP, NIC, and UEFI.
NEW QUESTION # 32
After successfully installing the NVIDIA Container Toolkit and configuring Docker, you're attempting to build a container image that leverages the GPU. You're using a Dockerfile but encounter the following error during the 'docker build' process: 'error during connect: this error may indicate that the docker daemon is not running'. However, the Docker daemon IS running. What is the most likely reason the build process is failing to connect, specifically in the context of GPU-enabled containers?
Answer: A
Explanation:
The error 'error during connect: this error may indicate that the docker daemon is not running' during a 'docker build', when the daemon actually is running, can indicate a failure to connect to the daemon for a specific reason related to GPU access. 'docker build' requires - gpus all' to be passed in order for CUDA to correctly build the image. Permissions(A) are unlikely to cause this specific connection error. User group issues(B) are usually related to running containers, not building them. A networking issue (D) is possible but less likely in the context of a local build. The same can be said for container exceeding the hosts' available memory.
NEW QUESTION # 33
You are responsible for ensuring interoperability between AI applications deployed across a diverse IT landscape, including an on-premises data center equipped with NVIDIA GPUs and multiple cloud platforms from different vendors. These environments need to support complex AI workflows that involve large-scale data processing, real-time analytics, and machine learning model training. To maintain consistent performance and flexibility, which strategy should you prioritize?
Answer: A
Explanation:
The best strategy is to ensure compatible storage protocols and APIs across the on-premises and cloud environments. AI workflows often move through multiple stages, including data ingestion, preprocessing, training, checkpointing, validation, inference, and analytics. If each environment uses incompatible storage interfaces, applications may require custom integration work, data copies, or workflow redesign. Using common protocols and APIs such as NFS for file-based access or S3-compatible APIs for object access improves portability and allows AI tools, data pipelines, and orchestration systems to operate more consistently across platforms. Standardizing on one vendor may reduce management complexity, but it can limit flexibility and create lock-in. Using only native cloud storage with middleware can work, but it may add complexity and inconsistent performance. Increasing network bandwidth helps data movement, but it does not solve protocol compatibility or application integration. In NVIDIA AI infrastructure, storage design must support high-throughput data access while also preserving operational flexibility across DGX, HGX, Kubernetes, Slurm, and cloud-connected AI environments.
NEW QUESTION # 34
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