New Study Network Appliance NS0-901 Questions, NS0-901 Braindumps

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Network Appliance NS0-901 Exam Syllabus Topics:

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

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                                    NS0-901 Braindumps | NS0-901 Formal Test

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                                    Network Appliance NetApp Certified AI Expert Exam Sample Questions (Q39-Q44):

                                    NEW QUESTION # 39
                                    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.)

                                    Answer: C,E


                                    NEW QUESTION # 40
                                    Given the company's goal of combining physics-based simulations with AI-driven analytics on a shared data foundation, which industry trend does this project best represent?

                                    Answer: D


                                    NEW QUESTION # 41
                                    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: A,D


                                    NEW QUESTION # 42
                                    An automotive company runs crash simulations on a dedicated High-Performance Computing (HPC) cluster and trains computer vision models on a separate AI cluster. Data scientists are complaining about the long delays required to move terabytes of simulation output data from the HPC storage to the AI cluster's storage before they can begin training.
                                    The current data flow is as follows:
                                    HPC Cluster -> --Manual Copy (NFS)--> -> AI Cluster
                                    An architect has been asked to redesign the infrastructure to eliminate this data movement bottleneck.
                                    Which architectural change would be most effective?

                                    Answer: D


                                    NEW QUESTION # 43
                                    An MLOps team uses a variety of platforms to manage their AI workloads. They need to understand the primary function of each tool within their ecosystem. Which statement best describes the role of an MLOps/LLMOps platform like Kubeflow or Run:AI?

                                    Answer: C


                                    NEW QUESTION # 44
                                    ......

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