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Network Appliance NS0-901 Exam Syllabus Topics:

SectionObjectives
Topic 1: 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. Scalability and performance optimization
          • 2. Data governance and management
            Topic 2: Industry Use Cases- AI applications across industries
            • 1. Digital twins
              • 2. Autonomous systems and agents
                • 3. Healthcare AI
                  Topic 3: AI Infrastructure and NetApp Solutions- AI-ready data infrastructure
                  • 1. High-performance storage for AI workloads
                    • 2. Data pipeline optimization for AI/ML workloads
                      - Converged workloads
                      • 1. Shared infrastructure design considerations
                        • 2. AI, HPC, and analytics convergence
                          Topic 4: AI and Machine Learning Fundamentals- AI, ML, DL concepts
                          • 1. Training, inference, and prediction workflows
                            • 2. Differences between AI, machine learning, and deep learning
                              - Algorithm types
                              • 1. Supervised learning
                                • 2. Unsupervised learning
                                  • 3. Reinforcement learning

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                                    Network Appliance NetApp Certified AI Expert Exam Sample Questions (Q21-Q26):

                                    NEW QUESTION # 21
                                    In the data pipeline, which NetApp technology is used to create an efficient, block-level, incremental replication of the anonymized datasets from the on-premises ASA to the Cloud Volumes ONTAP instance in Azure?

                                    Answer: B


                                    NEW QUESTION # 22
                                    What is the primary architectural benefit of using technologies like RDMA (Remote Direct Memory Access) and GPUDirect Storage in a high-performance AI training cluster?

                                    Answer: B


                                    NEW QUESTION # 23
                                    An MLOps team is using the BlueXP Workload Factory to automate the deployment of a Jupyter notebook environment for a new data scientist. The deployment job fails. The team reviews the job details in the BlueXP UI.
                                    Job ID: 78a1c-2b3d-4e5f
                                    Service: BlueXP Workload Factory
                                    Action: Deploy JupyterHub on Kubernetes
                                    Status: FAILED
                                    Error Message: "Failed to create PersistentVolumeClaim 'jupyter-pvc-user01'.
                                    User 'service-account- bluexp' does not have permission to 'create' resource
                                    'persistentvolumeclaims' in cluster 'k8s-prod- cluster-01'."
                                    What is the most direct way to resolve this issue?

                                    Answer: B


                                    NEW QUESTION # 24
                                    An organization wants to provide its data science team with a secure, on-demand method for using a powerful generative AI model with their private, sensitive corporate data. The solution must ensure that the private data is never exposed to the public internet or the public LLM API endpoint.
                                    The architect is designing a solution using BlueXP.
                                    Which two components are essential for building this secure solution? (Choose 2.)

                                    Answer: C,D


                                    NEW QUESTION # 25
                                    An AI research team is experiencing slow model training times. Their performance monitoring indicates that the GPUs are frequently idle, waiting for data. They want to implement a single technology change to create a more direct data path between their storage and GPUs.
                                    Their current setup is as follows:
                                    Compute: Server with NVIDIA A100 GPUs
                                    Storage: NetApp AFF A-Series (All-Flash)
                                    Network: 100GbE Ethernet
                                    Data_Path: Storage -> Host CPU/Memory -> GPU Memory
                                    Which technology should the architect recommend to specifically address this data path inefficiency?

                                    Answer: D


                                    NEW QUESTION # 26
                                    ......

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