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NVIDIA NCP-AII Exam Syllabus Topics:

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

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                                          NVIDIA AI Infrastructure Sample Questions (Q31-Q36):

                                          NEW QUESTION # 31
                                          You are installing the NGC CLI using 'pip' behind a corporate proxy. The installation fails due to connection errors. How do you configure pip' to use the proxy during the NGC CLI installation?

                                          Answer: A,C,E

                                          Explanation:
                                          You can configure 'pip' to use a proxy by setting environment variables (A), using the '-proxy' option with the 'pip install' command (B), or creating a 'pip.conf file (C). Downloading the package manually (D) is a workaround for the initial installation but doesn't address ongoing communication with NGC. Option E is incorrect since NGC CLI does support proxy configuration.


                                          NEW QUESTION # 32
                                          A system administrator wants to configure MIG for seven slices on an H100 GPU in an NVIDIA HGX system. Which command should be used?

                                          Answer: A

                                          Explanation:
                                          mig-parted is used to configure MIG partition layouts on NVIDIA GPUs, including creating a seven-slice MIG configuration on an H100 in an HGX system. It provides a structured way to apply MIG profiles across supported GPUs.


                                          NEW QUESTION # 33
                                          Your AI training pipeline involves a pre-processing step that reads data from a large HDF5 file. You notice significant delays during this step. You suspect the HDF5 file structure might be contributing to the slow read times. What optimization technique is MOST likely to improve read performance from this HDF5 file?

                                          Answer: D

                                          Explanation:
                                          Reorganizing the HDF5 file (option C) to improve data contiguity and chunking is the most effective optimization. HDF5 performance is highly dependent on how the data is laid out within the file. Contiguous data and optimal chunk sizes allow for more efficient 1/0 operations. Converting to CSV (A) loses the hierarchical structure of HDF5. Storing on NFS (B) adds network overhead. Compression (D) can reduce storage space but increases decompression overhead. Encryption (E) adds overhead without improving read performance.


                                          NEW QUESTION # 34
                                          The system administrator plans to use Multi-Instance GPU profiles. What command should be used to verify that the GPU has this mode enabled?

                                          Answer: C

                                          Explanation:
                                          The correct command is nvidia-smi. Multi-Instance GPU, or MIG, is managed and verified through NVIDIA System Management Interface utilities. On supported GPUs such as NVIDIA A100 and H100, nvidia-smi can be used to check whether MIG mode is enabled, list available GPU instances, and confirm how the GPU is partitioned. For example, administrators commonly use commands such as nvidia-smi, nvidia-smi -L, or nvidia-smi -i < GPU_ID > -q to inspect GPU state and MIG information. The options nvidia-mode, nvidia- mig, and nvidia-enable are not standard NVIDIA commands for verifying MIG mode. This is an important server bring-up step because MIG profiles must be confirmed before workloads are scheduled through Kubernetes, Slurm, NVIDIA GPU Operator, or other cluster management tools. If MIG is not enabled or profiles are not created correctly, users may not see the expected GPU slices, and AI inference or multi-tenant workloads can fail placement or run with incorrect resource isolation.


                                          NEW QUESTION # 35
                                          A cluster administrator is preparing to update the firmware on a DGX H100 system, including the GPU tray (baseboard). What is the correct sequence of steps to perform a safe and successful firmware upgrade?

                                          Answer: A

                                          Explanation:
                                          A safe DGX H100 firmware upgrade starts by stopping GPU activity to avoid workload disruption, then updating and rebooting the BMC so the management controller is current. After that, motherboard and tray components such as the GPU tray/baseboard are updated, followed by a cold reset to activate firmware fully and final verification to confirm completion.


                                          NEW QUESTION # 36
                                          ......

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