Exam Network Appliance NS0-901 Material | Latest NS0-901 Exam Materials

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Network Appliance NS0-901 Exam Syllabus Topics:

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

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                                    Network Appliance NetApp Certified AI Expert Exam Sample Questions (Q66-Q71):

                                    NEW QUESTION # 66
                                    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 # 67
                                    An AI operations team is troubleshooting why their RAG-based chatbot is providing outdated information. They have confirmed that the vector database embedding process is functioning correctly, but suspect an issue with the initial data synchronization that moves the knowledge base from an on- premises ONTAP file share to a cloud staging bucket.
                                    They inspect the relevant BlueXP copy and sync job and find the following details:
                                    Service: BlueXP copy and sync
                                    Relationship_Name: KB_Sync_to_Vector_Staging
                                    Source: nfs://ontap-cluster-1/vol_kb/docs
                                    Destination: s3://vector-staging-bucket-89a3/latest/
                                    Last_Sync_Status: FAILED
                                    Last_Sync_Time: 2025-07-11T02:00:15Z
                                    Error_Message: "Authentication error:
                                    Unable to access source.
                                    Check export policy on 'vol_kb'."
                                    Based on this information, what is the most direct solution to fix the data pipeline?

                                    Answer: C


                                    NEW QUESTION # 68
                                    The data anonymization job, running in a Kubernetes pod, fails. The pod logs show a "Permission Denied" error when trying to access the source volume on the on-premises ASA. An administrator checks the export policy rule for the volume.
                                    The active rule is as follows:
                                    Rule_Index: 1
                                    Client_Match: 10.50.0.0/16
                                    Protocols: nfs4
                                    Read_Only_Access: sys
                                    Read_Write_Access: -
                                    Superuser_Access: none
                                    The Kubernetes pod that failed has an IP address of '10.60.5.10'.
                                    What is the cause of the "Permission Denied" error?

                                    Answer: D


                                    NEW QUESTION # 69
                                    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: B,C


                                    NEW QUESTION # 70
                                    A distributed training job running on the AIPod fails to start. The MLOps engineer inspects the events for one of the pending training pods and sees the following message:
                                    Events:
                                    Type Reason Age From Message
                                    - - - -
                                    Warning FailedScheduling 5m12s default-scheduler 0/4 nodes are available: 4 node(s) had no available volume zone.
                                    The PersistentVolumeClaim (PVC) for this pod specifies a StorageClass that uses the 'ontap-nas' Trident provisioner.
                                    he Trident logs show no errors.
                                    What is the most likely cause of this scheduling failure?

                                    Answer: C


                                    NEW QUESTION # 71
                                    ......

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