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Network Appliance NS0-901 Exam Syllabus Topics:

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

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                                    Network Appliance NetApp Certified AI Expert Exam Sample Questions (Q25-Q30):

                                    NEW QUESTION # 25
                                    A data scientist needs to test a new data normalization technique. To do this, they require an isolated, writable copy of a 50 TB curated simulation dataset that resides on the NetApp ASA system. The operation must be completed as quickly as possible and consume minimal additional storage space. Which NetApp technology is the most appropriate solution for this requirement?

                                    Answer: D


                                    NEW QUESTION # 26
                                    A research institute is designing an infrastructure to support its entire AI drug discovery pipeline.
                                    The pipeline has two distinct workload requirements:
                                    1. Training: A team of data scientists needs to train several large transformer models simultaneously using a 500 TB dataset of genomic sequences. This process requires maximum data throughput to keep the GPUs saturated.
                                    2. Inference: Once trained, the models are deployed to an internal web portal where researchers submit individual protein sequences for analysis. These queries must return results with the lowest possible latency.
                                    Which infrastructure design best satisfies both requirements? (Choose 2.)

                                    Answer: C,E


                                    NEW QUESTION # 27
                                    Which AI technology is used to generate new, never-before-seen content such as images or text?

                                    Answer: A


                                    NEW QUESTION # 28
                                    A data scientist on the team wants to run an experiment on a new vector indexing strategy. To do this, they need a temporary, writable copy of the main vector database. They use the NetApp DataOps Toolkit for Python to automate this process.
                                    The following script is executed:
                                    from netapp_dataops.k8s import clone_pvc
                                    clone_pvc(
                                    source_pvc_name="prod-vector-db-pvc",
                                    new_pvc_name="exp-indexing-clone",
                                    namespace="ds-prod"
                                    )
                                    Assuming the source PVC exists and the user has correct permissions, what is the primary benefit of using this method?

                                    Answer: A


                                    NEW QUESTION # 29
                                    An AI infrastructure team is selecting a storage protocol for a new, high-performance computing (HPC) cluster that will be used for a weather modeling AI application. The application requires extremely high-throughput, parallel access from hundreds of compute nodes to a shared dataset.
                                    The workload characteristics are as follows:
                                    Access_Pattern: Massively parallel reads from many clients.
                                    File_Type: Large, shared data files.
                                    Latency_Sensitivity: High (for metadata operations).
                                    Primary_Requirement: Maximum aggregate throughput.
                                    Which storage protocol is best suited for this workload?

                                    Answer: A


                                    NEW QUESTION # 30
                                    ......

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