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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: NVIDIA AI Infrastructure Components | 25-30% | - NVIDIA networking solutions (Mellanox) - NVIDIA DGX systems - Storage solutions for AI workloads - NVIDIA AI Enterprise software |
| Topic 2: Deployment and Configuration | 25-30% | - System installation and setup - Network configuration - Software deployment - Cluster configuration |
| Topic 3: Monitoring and Management | 15-20% | - Resource utilization - Performance monitoring - NVIDIA management tools - Troubleshooting basics |
| Topic 4: AI Infrastructure Fundamentals | 15-20% | - AI and Deep Learning concepts - NVIDIA software stack overview - GPU architecture basics - Data center infrastructure requirements |
| Topic 5: Security and Best Practices | 10-15% | - Compliance considerations - Security fundamentals - Operational best practices |
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NEW QUESTION # 139
An AI infrastructure team is implementing a highly available training environment. They want maintenance activities or isolated hardware failures to have minimal impact on running workloads while avoiding unnecessary complexity. Which design principle provides the strongest foundation for achieving this objective?
Answer: B
Explanation:
High availability depends on eliminating critical single points of failure through carefully planned redundancy in networking, storage, management, and compute infrastructure. Redundancy should be applied strategically to improve resiliency without creating excessive operational complexity. Consolidating services onto single systems increases operational risk rather than reducing it.
NEW QUESTION # 140
Which of the following storage technologies are most suitable for storing large training datasets used in deep learning, considering both performance and cost?
Answer: A
Explanation:
NVMe SSDs in a local RAID offer high performance and relatively low latency, making them suitable for data that needs to be accessed quickly. Parallel file systems deployed on NVMe SSDs provide the highest performance and scalability, especially for large datasets accessed concurrently by multiple training nodes. Object storage can be used for initial data ingest or archival but is generally slower than local or parallel file systems for training. SATA HDDs and Tape backup systems are a low performing option for this case.
NEW QUESTION # 141
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: D
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 # 142
An infrastructure engineer in an AI factory has successfully replaced a power supply unit on an NVIDIA DGX H100. After installation, both the IN and OUT LEDs on the new power supply illuminate solid green.
Which NVSM CLI command should the engineer use to quickly verify the overall system status and ensure it is operating as expected?
Answer: C
Explanation:
The NVIDIA System Management (NVSM) tool is the definitive CLI utility for monitoring the health of DGX platforms. While replacing a PSU (Power Supply Unit) is a common maintenance task, verifying that the new component is correctly integrated into the system's health model is mandatory. While nvsm show power would provide specific data regarding wattage and voltage for the PSU, the most comprehensive way to ensure the replacement hasn't caused secondary issues or that the system hasn't remained in a "Degraded" state is to run nvsm show health. This command performs a global check across all subsystems: GPUs, NVLink switches, storage, fans, and power. If the PSU replacement was successful and the system is back to full redundancy, nvsm show health will return a "Healthy" status. In an AI factory setting, where DGX H100 nodes pull significant power, ensuring that all 6 PSUs (in an N+N or N+1 configuration) are not only physically green but logically acknowledged by the Baseboard Management Controller (BMC) is critical for preventing unexpected shutdowns during high-load training iterations.
NEW QUESTION # 143
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: A
Explanation:
Fraud detection inference across multiple sites needs fast access to data and flexibility for different workloads and applications. Multi-protocol storage with low latency supports rapid retrieval, real-time inference pipelines, and distributed access patterns required for time-sensitive AI fraud detection.
NEW QUESTION # 144
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