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Network Appliance NS0-901 Exam Syllabus Topics:

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

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                                    Hot NS0-901 Training Pdf | High Pass-Rate Latest NS0-901 Test Labs: NetApp Certified AI Expert Exam

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                                    Network Appliance NetApp Certified AI Expert Exam Sample Questions (Q69-Q74):

                                    NEW QUESTION # 69
                                    The company's finance department mandates a cost-control strategy for the petabyte-scale StorageGRID data lake. The analysis shows that 80% of the simulation data is not accessed after
                                    90 days but must be retained for five years for regulatory compliance. Which StorageGRID feature should the architect use to automatically reduce the long-term storage costs for this inactive data?

                                    Answer: C


                                    NEW QUESTION # 70
                                    The "Advisor Assistant" application, running as a pod in Kubernetes, suddenly cannot access its data on the AFF A-Series. The application logs show "connection timed out" errors. The network team provides a firewall log snippet for the traffic between the application pod and the storage system's NFS data LIF.
                                    TIME | SRC_IP | DST_IP | PROTO | DST_PORT | ACTION
                                    -|--||-|-|-
                                    2025-07-11T16:01:10Z | 10.20.5.101 (Pod) | 10.20.10.55 (LIF) | TCP | 111 | BLOCKED 2025-07-
                                    11T16:01:12Z | 10.20.5.101 (Pod) | 10.20.10.55 (LIF) | TCP | 2049 | BLOCKED What is the most likely cause of the connectivity failure?

                                    Answer: C


                                    NEW QUESTION # 71
                                    An architect is designing a data pipeline for a predictive AI model that will forecast retail sales.
                                    The pipeline must be robust, version-controlled, and efficient.
                                    The proposed data flow is as follows:
                                    1. Ingest: Raw sales data is copied daily from multiple point-of-sale (POS) systems to a central staging area on an on-premises ONTAP cluster.
                                    2. Prepare: The raw data is messy. A data engineering team needs a clean, isolated, and writable copy of the latest daily data to perform cleansing and feature engineering tasks without impacting the original raw data.
                                    3. Train: Once prepared, the cleansed dataset is used to retrain the predictive model on a GPU cluster.
                                    This step must be repeatable with the exact same dataset for compliance.
                                    4. Deploy: The newly trained model is pushed to production inference servers.
                                    Which combination of NetApp technologies best supports this entire predictive AI lifecycle?
                                    (Select all
                                    that apply.)

                                    Answer: A,C,E


                                    NEW QUESTION # 72
                                    Due to the success of the "Advisor Assistant," the number of concurrent users is expected to double in the next quarter. The existing Kubernetes cluster is running at 80% of its GPU capacity during peak hours. The architect must propose a plan to scale the compute infrastructure to handle the increased load.
                                    Which two strategies represent the most effective and scalable solutions? (Choose 2.)

                                    Answer: B,C


                                    NEW QUESTION # 73
                                    A company is running its AI training workloads on a NetApp AFF A-Series system. To manage costs, they want to automatically move inactive training datasets and older model checkpoints from the high- performance all-flash tier to a lower-cost object storage tier, such as an on- premises StorageGRID or a public cloud bucket. The process must be transparent to the data scientists and not require changes to their scripts or file paths.
                                    Which two NetApp technologies should be combined to achieve this goal? (Choose 2.)

                                    Answer: C,D


                                    NEW QUESTION # 74
                                    ......

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