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| Section | Weight | Objectives |
|---|---|---|
| Troubleshoot and Optimize | 12% | - Performance optimization
|
| Cluster Test and Verification | 33% | - Network and hardware validation
|
| System and Server Bring-up | 31% | - Deployment and validation lifecycle
|
| Control Plane Installation and Configuration | 19% | - GPU utilization in containers
|
| Physical Layer Management | 5% | - Networking and GPU partitioning
|
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NEW QUESTION # 54
For a 48-hour NCCL burn-in test, which parameters ensure sustained fabric stress while detecting silent data corruption?
Answer: B
Explanation:
The NVIDIA Collective Communications Library (NCCL) tests are the gold standard for validating the interconnect performance of a GPU cluster. For a long-duration burn-in (48 hours), the goal is not just to measure peak bandwidth, but to stress the fabric under load to catch intermittent hardware failures or "Silent Data Corruption" (SDC). The all_reduce_perf test is the most intensive as it involves bidirectional data flow across all GPUs. The specific parameters in Option B are critical: -b 8G -e 32G sets the message size range to large buffers that saturate the 400G InfiniBand links; -c 1000 ensures a high number of iterations for statistical significance; -z 1 (check) is the most vital flag, as it enables verification of the mathematical result.
If a bit flips during transmission due to a faulty transceiver, the -z 1 flag will catch the mismatch and report a failure. Finally, -G 1000 ensures the test runs long enough to reach thermal equilibrium across the switches and HCAs.
NEW QUESTION # 55
You have a dataset with many small files (e.g., images). Directly reading these files can result in high metadata access overhead. What are the MOST effective strategies to mitigate this problem?
Answer: B,D,E
Explanation:
Combining small files reduces the number of metadata operations. A key-value store can provide efficient access to small data items. Increasing metadata servers reduces the load on individual metadata servers. Smaller block sizes exacerbate the problem. Replicating data doesn't address metadata overhead.
NEW QUESTION # 56
After running a 24-hour stress test on a DGX node, the administrator should verify which two key metrics to ensure system stability?
Answer: B
Explanation:
A 24-hour stress test (using tools like HPL or NCCL) is designed to push the thermal and electrical limits of a DGX system. To verify a "Pass," the administrator must ensure that the hardware maintained its performance targets without degradation.Consistent GPU utilization >95%confirms that the workload successfully saturated the compute cores for the entire duration. Crucially, the absence ofthermal throttling events (verified via nvidia-smi -q -d PERFORMANCE) ensures that the system's cooling solution (fans and heatsinks) is adequate for the environment; if throttling occurred, the GPUs would have slowed down to protect themselves, indicating a potential cooling failure or environmental heat issue. While power consumption (Option D) and CPU usage (Option A) are interesting, they are not the primary indicators of
"Stability" under extreme AI training loads. System stability is defined by the ability to run at peak speeds indefinitely without hardware-level interventions or slowdowns.
NEW QUESTION # 57
During HPL execution on a DGX cluster, the benchmark fails with "not enough memory' errors despite sufficient physical RAM. Which HPL.dat parameter adjustment is most effective?
Answer: A
Explanation:
The HPL problem size controls the benchmark's total memory footprint. Reducing the problem size lowers memory demand while keeping the block size stable, resolving "not enough memory" failures without unnecessarily changing the computational blocking behavior.
NEW QUESTION # 58
After installing NGC CLI on RHEL, a user runs ngc registry image list but sees no results. The API key and organization are correctly configured. What resolves this?
Answer: A
Explanation:
The best answer is to configure the correct team context using ngc config set --team team-name. If the API key and organization are already correct, an empty image list often indicates that the CLI is looking in the wrong namespace or default team context. NGC registry commands support organization, ACE, and team context; NVIDIA NGC CLI documentation shows registry image commands using --org and --team, with the team option specifying the team name and defaulting to the current configuration. Setting the correct team ensures that private registry images, resources, and containers scoped to that team are visible. Disabling SELinux is not appropriate because this is an NGC namespace or access-context issue, not a local Linux security policy issue. Reinstalling the CLI does not fix a correct installation with incomplete context. Registry read permissions are also required in real operations, but the question states the API key and organization are correctly configured and asks what resolves the no-results condition. In this scenario, specifying the team is the targeted fix.
NEW QUESTION # 59
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