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NVIDIA NCP-AII Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: Software Stack Deployment25%- Orchestration and workload management
  • 1. Slurm, Kubernetes and container orchestration
    • 2. NGC catalog and software deployment
      - NVIDIA software components
      • 1. GPU drivers, container toolkit and runtime
        • 2. Base Command Manager and cluster management tools
          Topic 2: GPU Resource Management15%- Multi-Instance GPU (MIG) configuration
          • 1. Isolation and performance tuning
            • 2. Partitioning and resource allocation
              - GPU scheduling and optimization
              • 1. NVLink and fabric management
                • 2. Workload placement and sharing
                  Topic 3: Networking and Storage Configuration20%- NVIDIA networking solutions
                  • 1. InfiniBand and Ethernet fabric setup
                    • 2. BlueField DPU configuration
                      - Storage integration
                      • 1. Parallel file systems and object storage
                        • 2. Storage performance for AI workloads
                          Topic 4: System and Server Bring-up20%- Firmware and system configuration
                          • 1. BMC, BIOS, TPM and firmware updates
                            • 2. OS installation and base configuration
                              - Hardware installation and validation
                              • 1. Power, cooling and physical connectivity verification
                                • 2. Server, GPU, network and storage components setup
                                  Topic 5: Validation, Troubleshooting and Optimization20%- Troubleshooting and maintenance
                                  • 1. Hardware and software fault isolation
                                    • 2. Performance optimization and best practices
                                      - Cluster validation and benchmarking
                                      • 1. HPL, NCCL and performance testing
                                        • 2. Health checks and error detection

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                                          NVIDIA AI Infrastructure Sample Questions (Q72-Q77):

                                          NEW QUESTION # 72
                                          An enterprise is deploying an AI Factory using NVIDIA DGX BasePOD architecture. The infrastructure team must ensure high availability and efficient data transfer between compute nodes. Which network topology should they implement for the InfiniBand fabric?

                                          Answer: C

                                          Explanation:
                                          DGX BasePOD InfiniBand fabrics use a Fat-Tree topology with rail-optimized design to provide high availability, low latency, and high-bandwidth East-West communication between compute nodes. This supports efficient distributed AI training by minimizing oversubscription and maintaining predictable GPU-to-GPU communication performance.


                                          NEW QUESTION # 73
                                          Which of the following statements are true regarding the use of Congestion Management (CM) and Congestion Avoidance (CA) techniques within an InfiniBand fabric using NVIDIA technology? (Select TWO)

                                          Answer: A,C,E

                                          Explanation:
                                          CM and CA are crucial for maintaining performance in InfiniBand fabrics. CM addresses congestion that has already occurred, while CA tries to prevent it. ECN is a key CA mechanism in InfiniBand. InfiniBand is lossless but experiences performance degradation if congestion occurs; CM helps mitigate this. They are not implemented at the IP layer; they are integral parts of the InfiniBand transport. Rate limiting is a viable CM strategy.


                                          NEW QUESTION # 74
                                          You are designing an AI infrastructure cluster for training large language models (LLMs). The dataset consists of 10TB of image data and 5TB of text dat a. You estimate that intermediate training data (checkpoints, temporary files) will require an additional 20TB of storage. You want to use a parallel file system for optimal performance. Considering a replication factor of 2 for data redundancy and a 20% overhead for file system metadata, what is the minimum raw storage capacity you should provision?

                                          Answer: B

                                          Explanation:
                                          Total data size: IOTB + 5TB + 20TB = 35TB. With a replication factor of 2, the storage required is 35TB 2 = 70TB. Adding 20% overhead for metadata, we get 70TB 1.2 = 84 T B. Therefore, the minimum raw storage capacity is 84 + 8.4 = 92.4 TB. Overhead needs to be calcualted from after replication is implemented, so replication + 20% overhead.


                                          NEW QUESTION # 75
                                          A customer has just completed the first boot of their DGX system and is prompted to create an administrative user. What is the correct approach for setting up this user to ensure secure BMC and GRUB access?

                                          Answer: B

                                          Explanation:
                                          During initial DGX setup, the administrative user should be created with unique, strong credentials because it is used for secure management access, including BMC and GRUB-related authentication. Avoiding default or weak credentials reduces the risk of unauthorized system control.


                                          NEW QUESTION # 76
                                          Which of the following tests should be used to check for the lowest possible latency between two nodes in a fabric?

                                          Answer: A

                                          Explanation:
                                          The ib_write_lat test is used to check low-level RDMA write latency between two nodes in an InfiniBand fabric. In NVIDIA AI infrastructure, latency validation is important because distributed training workloads depend on fast GPU-to-GPU synchronization across servers. Operations such as all-reduce, reduce-scatter, and parameter synchronization are sensitive to delay, especially as the number of nodes increases. The ib_write_lat utility is part of the RDMA perftest toolset and is commonly used during fabric validation to confirm that adapters, switches, cabling, routing, firmware, and Subnet Manager configuration are working correctly. ib_read_bw measures RDMA read bandwidth, not latency. ib_write_bw measures RDMA write bandwidth, not the lowest latency behavior. ib_read_lat measures read latency, but write latency is commonly used as a practical low-level latency check for RDMA fabric health. Consistently low and stable ib_write_lat results help confirm that the fabric is ready for higher-level validation with NCCL, HPL, and distributed AI workload tests.


                                          NEW QUESTION # 77
                                          ......

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