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Customizable NCP-AII Exam Mode, Customized NCP-AII Lab SimulationThis allows candidates to choose the format that best suits their learning style and preference, ensuring a seamless and effective exam preparation experience. By offering tailored solutions to meet individual needs, It-Tests has established itself as a trusted provider of top-quality NVIDIA AI Infrastructure (NCP-AII) exam preparation material. NVIDIA AI Infrastructure Sample Questions (Q30-Q35):NEW QUESTION # 30
You are observing that the memory bandwidth being achieved by your CUDA application on an NVIDIAAIOO GPU is significantly lower than the theoretical peak bandwidth. Which of the following could be potential causes for this, and what actions can you take to validate or mitigate them? (Select all that apply)
A. The application is using a small transfer size per kernel launch. Increase the amount of data processed per kernel launch to amortize the overhead of kernel launch and data transfer.
B. The system memory is fully occupied. Deallocate some memory.
C. The application is using single precision floating-point operations. Switch to double precision to increase memory bandwidth utilization.
D. The application is using uncoalesced memory access patterns. Refactor the code to ensure contiguous memory access by threads within a warp.
E. The GPU is being limited by power capping. Increase the power limit using 'nvidia-smi -pl' (if permitted) to allow the GPU to operate at higher clock speeds.
Answer: A,D,E
Explanation:
Uncoalesced memory access, small transfer sizes, and power capping are all factors that can limit achieved memory bandwidth. Switching to double precision will increase memory usage not necessarily bandwidth utilization (though the impact can vary depending on the workload). Power cap can definitely limit GPU performance, so raising it could help, as could code optimization. Therefore, the answer is A, B, and C, E is not usually relevant.
NEW QUESTION # 31
After installing the NGC CLI, you attempt to run 'ngc config set' and encounter the following error: 'Error: API key is invalid or missing'.
What are the most likely causes of this issue and how can you resolve them?
A. The NGC CLI is not properly installed. Reinstall the package using 'pip install -upgrade nvidia-cli'
B. The NGC CLI configuration file is corrupted. Delete the file (A.ngc/config.json') and reconfigure the CLI.
C. The host does not have network access to NGC.
D. The NGC service is down. Check the NVIDIA NGC status page for any known outages.
E. The NGC API key is incorrect or has expired. Verify the API key in your NVIDIA account and update the configuration using 'ngc config set'.
Answer: B,C,E
Explanation:
The most likely cause is an invalid API key (B) or a corrupted configuration file (C), or the host lacks network access (E). Reinstalling the package (A) might not resolve the issue if the problem lies with the API key or config file. While NGC service outages (D) are possible, they are less common.
NEW QUESTION # 32
You are designing a storage solution for a multi-tenant AI cluster. Different teams will be running training jobs concurrently. Which of the following considerations are MOST important for ensuring fair resource allocation and preventing performance bottlenecks?
A. Using a single large storage volume shared by all tenants
B. Isolating tenants using separate storage namespaces or volumes
C. Prioritizing I/O requests from users with higher privileges
D. Implementing storage quotas and quality-of-service (QOS) policies
E. Using only SSDs for all storage tiers.
Answer: B,D
Explanation:
Storage quotas prevent individual tenants from consuming excessive storage resources, while QOS policies ensure that each tenant receives a fair share of I/O bandwidth. Isolating tenants using separate storage namespaces or volumes prevents noisy neighbor effects, where one tenant's I/O -intensive workload impacts the performance of other tenants. A single large volume doesn't provide isolation. Prioritizing I/O based on privileges is generally not a fair approach in a multi-tenant environment.
NEW QUESTION # 33
You need to uninstall all NVIDIA drivers and associated packages from a Linux system cleanly. Which command sequence is the most reliable for achieving this after stopping the display manager (e.g., 'sudo systemctl stop gdm3')?
A. Deleting the Vusr/lib/nvidia and 'lusr/share/nvidia directories.
B. Running the .run' installer with the '-uninstall' option (if the driver was installed this way)
C. 'sudo dnf remove nvidia- (on Fedora-based systems)
D. 'sudo yum remove nvidia- (on RHEL/CentOS-based systems)
E. 'sudo apt purge nvidia- (on Debian/Ubuntu-based systems)
Answer: B,C,D,E
Explanation:
The appropriate package manager command (like 'apt purge' , 'yum remove' , or 'dnf remove' with the 'nvidia- wildcard) is the most reliable for removing driver packages. If the driver was installed using the ' .'run' installer, running it with '-uninstall' is also effective. Directly deleting directories is not recommended as it may leave behind configuration files and dependencies. Always stop the display manager before uninstalling to avoid conflicts.
NEW QUESTION # 34
Consider a scenario where you need to isolate GPU workloads in a multi-tenant Kubernetes cluster. Which of the following Kubernetes constructs would be MOST suitable for achieving strong isolation at both the resource and network level?
A. Using node affinity only.
B. Using labels and selectors to schedule workloads on specific GPU nodes.
C. Using namespaces with resource quotas and network policies.
D. Using taints and tolerations to dedicate GPU nodes to specific workloads.
E. Using pod affinity and anti-affinity rules to control pod placement.
Answer: C
Explanation:
Namespaces provide logical isolation within a Kubernetes cluster. Resource quotas limit the resources (including GPIJs) that a namespace can consume, while network policies control network traffic between namespaces, ensuring strong isolation. Options B, C, D, and E provide some level of control over pod placement but do not offer the same level of resource and network isolation as namespaces with resource quotas and network policies.
NEW QUESTION # 35
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