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Network Appliance NS0-901 Exam Syllabus Topics:

SectionObjectives
Topic 1: 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 2: 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
                    Topic 3: Industry Use Cases- AI applications across industries
                    • 1. Digital twins
                      • 2. Healthcare AI
                        • 3. Autonomous systems and agents
                          Topic 4: 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

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                                    Network Appliance NetApp Certified AI Expert Exam Sample Questions (Q74-Q79):

                                    NEW QUESTION # 74
                                    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?

                                    Answer: D


                                    NEW QUESTION # 75
                                    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: C


                                    NEW QUESTION # 76
                                    A data science team works primarily at a central data center but needs to run a short-term, burst- compute training job in the public cloud to take advantage of specialized GPUs that are not available on- premises. They need to efficiently and securely move a 20 TB dataset from their on- premises ONTAP cluster to a Cloud Volumes ONTAP instance for the duration of the job.
                                    The data flow requirement is as follows:
                                    Source: On-premises ONTAP cluster
                                    Destination: Cloud Volumes ONTAP in AWS
                                    Requirement: Efficient, secure, block-level data transfer.
                                    Which NetApp technology is the most appropriate tool for this task?

                                    Answer: A


                                    NEW QUESTION # 77
                                    An organization's AI platform team needs to provide two distinct tiers of storage for their data scientists on a single Kubernetes cluster:
                                    1. 'gold-tier': Extremely low-latency storage for active model training, using an all-flash NetApp ASA system.
                                    2. 'bronze-tier': Cost-effective, high-capacity storage for data staging and archiving, using a NetApp StorageGRID system.
                                    How should the platform team configure NetApp Trident to meet these requirements? (Select all that apply.)

                                    Answer: A,C,E,F


                                    NEW QUESTION # 78
                                    A university is building a shared AI research platform. They have two primary requirements:
                                    1. Performance: A "hot" research area for active model training and development that requires the absolute lowest latency and highest throughput to support multiple, simultaneous GPU- intensive jobs.
                                    The data in this area is around 50 TB.
                                    2. Capacity & Cost: A "cold" data lake to store over 5 PB of raw, unstructured experimental data that is infrequently accessed but must be retained for compliance and future use. This tier must be as costeffective as possible.
                                    Which combination of NetApp hardware and technologies should an architect select to build a complete, optimized, and cost-effective solution? (Select all that apply.)

                                    Answer: A,B,E,F


                                    NEW QUESTION # 79
                                    ......

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