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| Section | Weight | Objectives |
|---|---|---|
| System and Server Bring-up | 31% | - Deployment and validation lifecycle
|
| Control Plane Installation and Configuration | 19% | - GPU utilization in containers
|
| Troubleshoot and Optimize | 12% | - Fault detection and remediation
|
| Cluster Test and Verification | 33% | - Performance and stress testing
|
| Physical Layer Management | 5% | - Networking and GPU partitioning
|
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NEW QUESTION # 90
You're optimizing a deep learning model for deployment on NVIDIA Tensor Cores. The model uses a mix of FP32 and FP16 precision. During profiling with NVIDIA Nsight Systems, you observe that the Tensor Cores are underutilized. Which of the following strategies would MOST effectively improve Tensor Core utilization?
Answer: B
Explanation:
Padding input tensors (C) to multiples of 8 is crucial for optimal Tensor Core performance, as Tensor Cores operate most efficiently on data with these dimensions. Using FP16 (B) is important, but proper alignment is key for full utilization. Increasing batch size (A) can improve overall throughput but doesn't directly address Tensor Core utilization. CIJDA graph capture (D) reduces kernel launch overhead, not Tensor Core utilization directly. Decreasing learning rate (E) is unrelated to Tensor Core performance.
NEW QUESTION # 91
Your A1 inference server utilizes Triton Inference Server and experiences intermittent latency spikes. Profiling reveals that the GPU is frequently stalling due to memory allocation issues. Which strategy or tool would be least effective in mitigating these memory allocation stalls?
Answer: B
Explanation:
CUDA memory pools directly address memory allocation overhead. CUDA graph capture reduces kernel launch overhead, which can indirectly reduce memory pressure. Model quantization/pruning reduces the overall memory footprint. Optimizing using TensorRT reduces memory footprint. Increasing TCC priority primarily affects preemption behavior and doesn't directly address memory allocation issues. Therefore it will have less impact than others.
NEW QUESTION # 92
An administrator needs to manually deploy the BlueField image on a target DPU. The administrator downloads the new image file and needs to flash it to the hardware. Which command should the administrator use?
Answer: A
Explanation:
The correct command is bfb-install --rshim, normally used with the BFB image path and the appropriate RShim device, such as sudo bfb-install --rshim rshim0 --bfb < image_path.bfb > . BlueField software images are commonly deployed as BFB files, and RShim provides the host-side path used to push the image to the BlueField device. NVIDIA documentation states that the bfb-install utility is included with the RShim package and is used to push the BFB image to the BlueField side while reporting installation progress. The mlnx_fw_updater.pl tool is for firmware updates, not full BlueField OS image deployment. apt install doca- runtime installs DOCA runtime packages but does not flash a BlueField image. Using dd directly to a made- up device path is unsafe and not the supported method for deploying a BlueField boot stream image. During bring-up, using the supported BFB installation workflow helps ensure the DPU boots a valid signed image and enters a known operational state.
NEW QUESTION # 93
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: B
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 # 94
An infrastructure engineer runs an NCCL burn-in on an eight-node GPU cluster. Over a 12-hour period, all GPUs are tested with repeated all-reduce collectives. Monitoring tools show the following observations:
Aggregate bandwidth remains within 5% of documented reference for the hardware on every run.
No errors or timeouts are reported in NCCL logs.
On three occasions, one GPU logged single-run bandwidth dips of 15-20% compared to its normal performance, but performance recovered on the next run and stayed stable afterward. System logs show no hardware or driver errors.
Two minor NCCL WARN-level messages about "unexpected latency spike" appear in system logs for separate nodes, but could not be reproduced.
Which conclusion is the best strategy before releasing the cluster to production?
Answer: C
Explanation:
The best conclusion is to proceed, because the cluster met sustained bandwidth expectations, reported no NCCL errors or timeouts, and showed no persistent hardware, driver, or fabric faults. In NVIDIA AI infrastructure validation, burn-in testing is intended to detect repeatable failures, degraded links, unstable GPUs, NCCL communication errors, timeouts, or sustained performance below reference values. Short, unreproducible latency or bandwidth variation can occur because of transient system activity, monitoring overhead, scheduler noise, background services, or brief congestion. Since aggregate bandwidth stayed within
5% of the documented reference on every run and the dips recovered immediately without recurring on the same component, the evidence does not justify declaring the burn-in failed. Option B is too strict because one unreproduced transient dip is not enough to prove hardware failure. Option C is also excessive because excluding nodes without repeatable evidence reduces cluster capacity unnecessarily. The correct operational strategy is to approve the cluster while preserving logs, documenting the anomalies, and continuing normal monitoring during early production workloads.
NEW QUESTION # 95
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