NS0-901トレーリングサンプル & NS0-901日本語学習内容

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Network Appliance NS0-901 Exam Syllabus Topics:

SectionObjectives
Industry Use Cases- AI applications across industries
  • 1. Healthcare AI
    • 2. Autonomous systems and agents
      • 3. Digital twins
        AI Lifecycle and Deployment- End-to-end AI lifecycle
        • 1. Data ingestion and preparation
          • 2. Model training and validation
            • 3. Model deployment and monitoring
              - Operational challenges
              • 1. Data governance and management
                • 2. Scalability and performance optimization
                  AI and Machine Learning Fundamentals- Algorithm types
                  • 1. Reinforcement learning
                    • 2. Unsupervised learning
                      • 3. Supervised learning
                        - AI, ML, DL concepts
                        • 1. Training, inference, and prediction workflows
                          • 2. Differences between AI, machine learning, and deep learning
                            AI Infrastructure and NetApp Solutions- Converged workloads
                            • 1. Shared infrastructure design considerations
                              • 2. AI, HPC, and analytics convergence
                                - AI-ready data infrastructure
                                • 1. High-performance storage for AI workloads
                                  • 2. Data pipeline optimization for AI/ML workloads

                                    >> NS0-901トレーリングサンプル <<

                                    NS0-901日本語学習内容、NS0-901認定資格試験

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                                    Network Appliance NetApp Certified AI Expert Exam 認定 NS0-901 試験問題 (Q100-Q105):

                                    質問 # 100
                                    An AI team is planning two separate projects. The architect needs to provision the appropriate infrastructure for each.
                                    | | Project A | Project B|
                                    | -- | | - |
                                    | Goal | Build a novel image recognition model from scratch.
                                    | Adapt an existing, pre- trained LLM to understand company-specific jargon. |
                                    | Input Data | 10 million new, unlabeled images. | A 50 GB text corpus of internal documents. |
                                    | Required Compute | Very High (Weeks of multi-GPU training) | Moderate (Hours of single-GPU training) | Which two statements accurately describe the infrastructure requirements for these projects?
                                    (Choose two.)

                                    正解:A、E


                                    質問 # 101
                                    An AI platform team is investigating poor I/O performance for a specific workload that involves processing hundreds of thousands of small metadata files. The application is running on a Kubernetes cluster with storage provided by a NetApp ONTAP system over NFS. Performance metrics show acceptable network throughput but very high latency for metadata operations (e.g., open, stat, close).
                                    The current storage configuration is as follows:
                                    Storage_System: NetApp AFF A-Series
                                    Protocol: NFSv4.1
                                    Workload_Profile: Metadata-intensive, many small file lookups
                                    Observed_Issue: High latency on metadata operations, slow job completion Which storage architecture would be better suited to handle this specific metadata-intensive workload?

                                    正解:D


                                    質問 # 102
                                    An AI architect is reviewing the design for a new data lake. The primary requirement is to store petabytes of unstructured data (images, video, sensor logs) in a highly durable, scalable, and cost- effective manner. The data will be accessed via S3 API by various data processing and analytics applications.
                                    The initial design proposes using a traditional Network Attached Storage (NAS) filer with a large number of disks. The architect reviews the proposal:
                                    Proposed_System: Traditional NAS Filer
                                    Protocol: NFSv4
                                    Scalability_Model: Scale-up
                                    Metadata_Handling: Centralized in filer head
                                    Cost_per_GB: Moderate
                                    Why is this proposed system a poor choice for a petabyte-scale data lake?

                                    正解:B


                                    質問 # 103
                                    A data scientist is working on a new model and needs a flexible environment for interactive data exploration, code development, and quick visualizations. A DevOps engineer is responsible for deploying the finalized model into a production pipeline that must run automatically every night without manual intervention.
                                    Which tools are best suited for each of these roles?

                                    正解:D


                                    質問 # 104
                                    Which of the following applications use AI in the healthcare industry? (Choose two)

                                    正解:B、D


                                    質問 # 105
                                    ......

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                                    NS0-901日本語学習内容: https://www.mogiexam.com/NS0-901-exam.html

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