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NVIDIA NCP-AII Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: Physical Layer Management5%- Networking and GPU partitioning
  • 1. MIG (Multi-Instance GPU) configuration
    • 2. BlueField network platform configuration
      Topic 2: System and Server Bring-up31%- Deployment and validation lifecycle
      • 1. Sequence of deployment and validation events
        • 2. Network topologies for AI factories
          - Hardware initialization and configuration
          • 1. GPU server installation and validation
            • 2. BMC, OOB, and TPM initial configuration
              • 3. Firmware upgrades including HGX and fault detection
                • 4. Hardware validation for workloads
                  - Physical infrastructure validation
                  • 1. Power and cooling validation
                    • 2. Storage parameter initialization
                      • 3. Cable and transceiver types validation
                        Topic 3: Control Plane Installation and Configuration19%- GPU utilization in containers
                        • 1. Docker GPU usage validation
                          - Infrastructure software stack deployment
                          • 1. Operating system installation
                            • 2. Cluster setup (Slurm, Enroot, Pyxis)
                              • 3. Base Command Manager (BCM) installation and HA configuration
                                - Drivers and toolkits
                                • 1. NVIDIA GPU and DOCA drivers installation/update
                                  • 2. NVIDIA container toolkit installation
                                    • 3. NGC CLI deployment
                                      Topic 4: Troubleshoot and Optimize12%- Fault detection and remediation
                                      • 1. Replacement of faulty hardware components
                                        • 2. GPU, fan, network card fault identification
                                          - Performance optimization
                                          • 1. Storage optimization
                                            • 2. Server performance tuning (Intel/AMD platforms)
                                              Topic 5: Cluster Test and Verification33%- Cluster diagnostics
                                              • 1. ClusterKit multi-node assessment
                                                • 2. Storage testing
                                                  - Performance and stress testing
                                                  • 1. NCCL communication testing
                                                    • 2. Single-node stress testing
                                                      • 3. Cluster burn-in tests (HPL, NCCL, NeMo)
                                                        • 4. HPL (High-Performance Linpack) benchmarking
                                                          - Network and hardware validation
                                                          • 1. Cabling and signal verification
                                                            • 2. Firmware validation (switches, transceivers, BlueField)
                                                              • 3. NVLink validation

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                                                                최신 NVIDIA-Certified Professional NCP-AII 무료샘플문제 (Q191-Q196):

                                                                질문 # 191
                                                                An engineer needs to completely remove NVIDIA GPU drivers from an Ubuntu 22.04 system to troubleshoot conflicts. Which command sequence ensures all driver components are purged?

                                                                정답：C

                                                                설명：
                                                                Purging nvidia-* removes NVIDIA driver packages and related configuration files, while autoremove cleans up unused dependencies and kernel module packages left behind. This provides a more complete cleanup than removing only a single driver package.


                                                                질문 # 192
                                                                A company has a registered NGC account and their server has NGC CLI installed. What step should be taken first to gain access to NGC?

                                                                정답：D

                                                                설명：
                                                                The NVIDIA GPU Cloud (NGC) is the central repository for AI-optimized containers, pre-trained models, and specialized SDKs. To interact with the NGC registry via the command line, the ngc CLI must be authenticated to the user's account. The command ngc config set is the verified first step to configure these credentials. When this command is executed, the user is prompted to provide their API Key, which is generated from the NGC web portal. This configuration process creates a local config file (typically in ~/.ngc
                                                                /config) that stores the authentication token, the preferred organization, and the team settings. Without running ngc config set, the CLI cannot authenticate requests to pull private containers or upload models. ngc init (Option B) is not a standard configuration command for the current NGC CLI architecture, and ngc config get (Option A) is only useful for viewing an existing configuration that has already been established.


                                                                질문 # 193
                                                                You are responsible for ensuring interoperability between AI applications deployed across a diverse IT landscape, including an on-premises data center equipped with NVIDIA GPUs and multiple cloud platforms from different vendors. These environments need to support complex AI workflows that involve large-scale data processing, real-time analytics, and machine learning model training. To maintain consistent performance and flexibility, which strategy should you prioritize?

                                                                정답：A

                                                                설명：
                                                                Compatible storage protocols and APIs such as NFS and S3 allow AI applications, data pipelines, and analytics workflows to access and exchange data consistently across on-premises GPU infrastructure and multiple cloud platforms. This supports interoperability, portability, and flexible integration across diverse environments.


                                                                질문 # 194
                                                                When setting up a multi-server, multi-GPU environment using NVLink switches, what is the primary consideration when planning the network topology for optimal performance?

                                                                정답：B

                                                                설명：
                                                                The primary goal is to reduce latency and maximize bandwidth. Minimizing hops between frequently communicating GPUs is essential for achieving this. Server placement for cooling is a secondary concern, and topology choice should prioritize performance over simplicity. Subnet configuration and server placement relative to the network edge are less critical for NVLink-specific performance.


                                                                질문 # 195
                                                                During NVLink Switch configuration, you encounter issues where certain GPUs are not being recognized by the system. Which of the following troubleshooting steps are most likely to resolve this problem?

                                                                정답：A,D,E

                                                                설명：
                                                                Explanation:Physical connection issues (A), BIOS configuration (B), and firmware incompatibility (C) are the most common causes of GPUs not being recognized. Reinstalling the operating system is a drastic measure that is unlikely to solve the problem. Checking the Power supply may also required to ensure the complete system have enough capacity and stability.


                                                                질문 # 196
                                                                ......

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