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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Troubleshoot and Optimize | 12% | - Performance optimization
|
| Topic 2: Cluster Test and Verification | 33% | - Cluster diagnostics
|
| Topic 3: Physical Layer Management | 5% | - Networking and GPU partitioning
|
| Topic 4: System and Server Bring-up | 31% | - Hardware initialization and configuration
|
| Topic 5: Control Plane Installation and Configuration | 19% | - GPU utilization in containers
|
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NEW QUESTION # 32
Your tasked with updating both NVIDIA GPU drivers and DOCA drivers on a set of servers used for AI workloads. The environment previously had an older driver stack and custom kernel modules. What is the most important step to successfully upgrade the drivers without causing conflicts?
Answer: B
Explanation:
NVIDIA AI infrastructure relies on a tightly coupled stack involving the GPU driver and theDOCA (Data Center Infrastructure-on-a-Chip Architecture)drivers, which include the OFED stack for InfiniBand.
When upgrading from an older version, "residue" from previous installations or custom kernel modules can cause version mismatches or symbol errors, preventing the new drivers from loading. The "100% verified" best practice is to perform a clean installation. This involves completely uninstalling the existing drivers (using apt-get purge or the .run file's --uninstall flag) and ensuring that kernel modules are removed from memory. This prevents conflict between the legacy nvidia.ko and the new version. Once the system is "clean," the new DOCA and GPU drivers can be installed as a matched set, ensuring that features like GPUDirect RDMA function correctly without being hindered by legacy configuration files.
NEW QUESTION # 33
You have a GPU-intensive application that requires the latest features of CUDA 12. However, your host system's NVIDIA driver is only compatible with CUDA 11.8. What steps can you take to enable your application to use CUDA 12 within a Docker container, without upgrading the host driver?
Answer: B
Explanation:
The NVIDIA Container Toolkit enables compatibility between the host driver and the CUDA version within the container (B). Using a Docker image based on will allow your application to leverage CUDA 12 features. Upgrading the host driver (A) is an option but not necessary and may introduce other compatibility issues. Setting (C) is not a standard or reliable approach. Mounting CUDA libraries from a volume (D) is complex and might not resolve driver version mismatches. Downgrading the application (E) avoids the problem but sacrifices access to CUDA 12 features. Because NVIDIA ensures a degree of backwards compatibility, the newer toolkit in the container can often work with an older driver on the host.
NEW QUESTION # 34
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: B,C,D
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 # 35
For an NVIDIA Enterprise AI Factory with 256 GPUs, which storage solution characteristic is most critical to validate during scaling tests?
Answer: A
Explanation:
Scaling an AI cluster to 256 GPUs (32 nodes of DGX H100) creates a massive " Incast " problem for the storage fabric. During large-scale training, every node frequently reads huge batches of data simultaneously.
NVIDIA's reference architectures (BasePOD/SuperPOD) specify that for high-performance training, each node must be able to sustain a minimum throughput-often 8 GiB/s or more-to keep all 8 GPUs saturated. If the storage system can handle one node at high speed but chokes when all 32 nodes request data, the " Scaling Efficiency " of the AI model will drop drastically as GPUs sit idle waiting for IO. Therefore, validating consistent per-node throughput under full cluster load is the most critical metric for an AI Factory. While IOPS (Option D) are important for small files, modern AI datasets are often sharded into large binary formats (like WebDataset or TFRecord) where sequential throughput becomes the primary bottleneck.
NEW QUESTION # 36
A financial services firm is deploying an AI model for fraud detection that requires rapid inference and data retrieval across multiple sites. Which feature should their storage system prioritize?
Answer: B
Explanation:
The storage system should prioritize multi-protocol data access with low latency. Fraud detection workloads often depend on near-real-time inference, rapid lookup of transaction history, feature retrieval, and integration across multiple systems or sites. In an NVIDIA AI infrastructure environment, storage must support the AI workflow without starving GPUs, inference servers, or analytics pipelines. Multi-protocol access allows different applications and environments to access data through suitable interfaces, such as file or object protocols, while maintaining interoperability across on-premises and cloud-connected platforms. Low latency is essential because fraud decisions are time-sensitive; delayed data access can reduce model usefulness or prevent immediate action. Tape backup systems are useful for archival retention but are not suitable for live inference or fast analytics. Low-cost HDD-only storage may provide capacity but usually cannot meet latency and throughput requirements. High capacity with moderate speed is also insufficient when the business requirement is rapid retrieval across sites. For production AI operations, storage should be designed for responsiveness, availability, protocol compatibility, and consistent performance under concurrent workload demand.
NEW QUESTION # 37
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