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NVIDIA NCP-AII Exam Syllabus Topics:

Topic	Details
Topic 1	Troubleshoot and Optimize: Covers identifying and replacing faulty hardware components such as GPUs, network cards, and power supplies, along with performance optimization for AMD Intel servers and storage.
Topic 2	System and Server Bring-up: Covers end-to-end physical setup of GPU-based AI infrastructure, including BMC OOB TPM configuration, firmware upgrades, hardware installation, and power and cooling validation to ensure servers are workload-ready.
Topic 3	Physical Layer Management: Covers configuring BlueField network platform devices and setting up Multi-Instance GPU (MIG) partitioning for AI and HPC workloads.
Topic 4	Cluster Test and Verification: Covers full cluster validation through HPL and NCCL benchmarks, NVLink and fabric bandwidth tests, cable and firmware checks, and burn-in testing using HPL, NCCL, and NeMo.
Topic 5	Control Plane Installation and Configuration: Covers deploying the software stack including Base Command Manager, OS, Slurm Enroot Pyxis, NVIDIA GPU and DOCA drivers, container toolkit, and NGC CLI.

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NVIDIA AI Infrastructure Sample Questions (Q171-Q176):

NEW QUESTION # 171
A systems engineer is updating firmware across a large DGX cluster using automation. What is the best practice for minimizing risk and ensuring cluster health during and after the process?

A. Drain nodes from the scheduler, run pre-update diagnostics, update firmware in batches, and verify health post-update before scaling to the next batch.
B. Update nodes that have reported faults, leaving others on older firmware.
C. Drain nodes from the scheduler, update firmware in batches, skip diagnostics and verify health post- update before scaling to the next batch.
D. To save time, simultaneously update all nodes in the cluster without draining or diagnostics.

Answer: A

Explanation:
Updating firmware on an NVIDIA DGX cluster is a critical operation that involves multiple sensitive components, including the GPU baseboard, the BMC, the motherboard tray (SBC), and the InfiniBand HCAs.
In a production environment, " Batching " is the industry standard to prevent a single corrupted firmware image or update failure from taking down the entire AI factory. The process must begin with " Draining " the nodes in the workload scheduler (like Slurm or Kubernetes) to ensure no active training jobs are interrupted.
Running pre-update diagnostics-using tools like nvsm show health or dcgmi diag-is vital to establish a baseline and ensure the hardware is stable before applying changes. Once the firmware is applied in a controlled batch, post-update verification is required to confirm the system returns to a " Healthy " state and that all versions match the target manifest. This " Rolling Update " strategy allows the engineer to pause the automation if a specific node fails to return to service, protecting the overall availability of the cluster.
Skipping diagnostics (Option D) or leaving nodes on mismatched versions (Option C) creates " configuration drift, " which leads to unpredictable performance in collective communication libraries.

NEW QUESTION # 172
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. Upgrade the GPUs to models with larger memory capacity.
B. Disable other models running on the same GPU to free up memory.
C. Increase the system RAM to accommodate the growing memory footprint.
D. Reduce the batch size and concurrency of the offending model in the Triton configuration.
E. Implement CUDA memory pooling within the Triton Inference Server configuration to reuse memory allocations efficiently.

Answer: D,E

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.

NEW QUESTION # 173
After installing a new NVIDIA GPU, you attempt to run a CUDA application, but you encounter the following error: 'CUDA error: CUDA driver version is insufficient for CUDA runtime version'. You have verified the driver and CUDA toolkit are installed. What is the MOST likely reason for this error, and how do you resolve it?

A. The NVIDIA driver is too old for the CUDA toolkit. Update the NVIDIA driver to a version that supports the CUDA toolkit.
B. The CUDA toolkit is too old. Update the CIJDA toolkit.
C. The GPU is not compatible with the CUDA toolkit. Install a different GPIJ.
D. The CUDA VISIBLE DEVICES environment variable is not set correctly.
E. The CUDA runtime libraries are missing from the system path. Add them to the PATH variable.

Answer: A

Explanation:
This error indicates an incompatibility between the driver and the CUDA toolkit. The most common reason is an outdated driver. The driver must be at least as new as the CUDA toolkit's minimum required driver version. CUDA VISIBLE DEVICES relates to GPU selection, not driver version.

NEW QUESTION # 174
A media company is developing an AI platform for video content analysis that requires storing and processing large volumes of unstructured video data. The platform must support high throughput for data ingestion and provide efficient access for real-time analytics. Given these requirements, which storage strategy should the company implement?

A. Object storage for scalability and metadata management
B. Block storage for low latency and high performance
C. File storage for hierarchical organization and easy navigation
D. Tape storage for its cost-effectiveness and archival capabilities

Answer: A

Explanation:
Object storage is best suited for large volumes of unstructured video data because it scales efficiently, supports high-throughput ingestion, and uses rich metadata to organize and retrieve content for analytics workflows. This makes it a strong fit for AI-based video analysis and real- time data access patterns.

NEW QUESTION # 175
After updating BlueField-3 DPU BMC firmware via Redfish, the engineer observes "TaskState:
Running" but no progress after 15 minutes. How should they track the update's completion status?

A. Query the DPU BMC with the Task ID of the installation process.
B. Check /var/log/messages on the DPU OS for update logs.
C. Run bfrec -status on the DPU to view flash progress.
D. Power cycle the DPU immediately to force a rollback.

Answer: A

Explanation:
Redfish firmware updates create an asynchronous task that must be monitored through the task service using the returned Task ID. Querying that Task ID on the DPU BMC shows whether the installation is still running, completed successfully, or failed, without interrupting the update process.

NEW QUESTION # 176
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