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Last Updated: Jul 19, 2026

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Huawei H13-231 中文 Exam Syllabus Topics:

SectionObjectives
AI Model Training and Optimization- Distributed training techniques
  • 1. Data parallelism and model parallelism
    • 2. Performance optimization methods
      - Inference and deployment optimization
      • 1. Latency and throughput optimization
        • 2. Model inference acceleration
          Intelligent Computing Fundamentals- AI and intelligent computing system principles
          • 1. Overview of intelligent computing architecture
            • 2. AI workload characteristics and requirements
              Intelligent Computing Infrastructure- Storage and networking for AI systems
              • 1. High-performance storage systems
                • 2. Low-latency network design for AI clusters
                  - Compute and hardware architecture
                  • 1. CPU/GPU heterogeneous computing principles
                    • 2. AI server and accelerator architecture
                      AI Cluster Deployment and Management- Cluster deployment and configuration
                      • 1. Distributed computing environment setup
                        • 2. AI cluster planning and resource allocation
                          - Resource scheduling and orchestration
                          • 1. Workload scheduling strategies
                            • 2. Resource pooling and optimization
                              Operations, Maintenance and Troubleshooting- Maintenance and reliability
                              • 1. High availability and disaster recovery
                                • 2. System updates and lifecycle management
                                  - System monitoring and diagnostics
                                  • 1. Fault detection and root cause analysis
                                    • 2. Performance monitoring tools and metrics

                                      Huawei HCIE-Intelligent Computing V1.0 (H13-231中文版) Sample Questions:

                                      1. 下列關於人工智慧的說法錯誤的是?

                                      A) 人工智慧技術的發展趨勢是技術平台開放、專用智慧走向通用智慧、智慧感知走向智慧認知。
                                      B) 機器學習是人工智慧的核心。
                                      C) 人工智慧是基於人的智能。
                                      D) 人工智慧是利用數位電腦或數位電腦控制的機器模擬、延伸和擴展人的智能,感知環境,獲取知識並利用知識獲得最佳結果的理論、方法、技術和應用系統。


                                      2. GPU因其SIMD(單指令多資料)架構而廣泛應用於機器學習領域。FPGA 最近開始流行。以下關於 FPGA 和 GPU 的哪一種說法是正確的?

                                      A) GPU有時候是如果電腦要換的話可以重新組裝。
                                      B) FPGA可以根據特定應用進行軟體編程,其彈性比GPU好很多。
                                      C) GPU的執行單元很少,只能在單一執行緒處理指令。
                                      D) 就峰值效能而言,GPU 低於 FPGA。


                                      3. 2017年柯潔與AlphaGo 2.0人機大戰可以說吸引了許多人關注,而Google晶片採用的是一種叫做TPU的ASIC晶片。

                                      A) 正確
                                      B) 錯誤


                                      4. 華為Atlas800深度學習系統。降低人工智慧應用的技術門檻。讓客戶更專注於業務本身,讓能AI服務的快速開發與上線。下列哪些設備可用作其計算節點?

                                      A) FusionServer G2500
                                      B) 2288H V5
                                      C) FusionServer G5500
                                      D) Atlas500


                                      5. ARMv7架構之後,ARM的處理器架構名稱改為Cortex。華為自研手機處理器晶片採用哪一種ARM架構?

                                      A) 皮質-R
                                      B) 皮質-M
                                      C) 皮質-A


                                      Solutions:

                                      Question # 1
                                      Answer: C
                                      Question # 2
                                      Answer: B
                                      Question # 3
                                      Answer: A
                                      Question # 4
                                      Answer: C
                                      Question # 5
                                      Answer: C

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