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Network Appliance NS0-901 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Security, Reliability, and Operations | 15% | - Monitoring, logging, and troubleshooting AI environments - High availability and data protection - Cost management and efficiency - Data security and access control for AI |
| Topic 2: AI Lifecycle | 27% | - Data preparation and management for AI - Model training, inference, and optimization - AI lifecycle stages: design, training, deployment, monitoring - AI governance, ethics, and compliance - Predictive vs generative AI |
| Topic 3: Cloud and Hybrid Cloud AI Deployment | 18% | - NetApp cloud data services for AI - Cloud-native AI solutions and integration - Hybrid and multi-cloud AI architectures - Data mobility and consistency across environments |
| Topic 4: AI Overview | 15% | - AI, machine learning, and deep learning concepts - Algorithm types: supervised, unsupervised, reinforcement learning - Convergence of AI, high-performance computing, and analytics - AI industry use cases and applications - AI deployment models: on-premises, cloud, edge |
| Topic 5: NetApp AI Solutions and Architecture | 25% | - NetApp AI-ready infrastructure components - ONTAP integration with AI frameworks - Storage architectures for AI workloads - Scalability and performance optimization for AI - Data management and data pipeline design |
Network Appliance NetApp Certified AI Expert Sample Questions:
1. What is the primary architectural benefit of using technologies like RDMA (Remote Direct Memory Access) and GPUDirect Storage in a high-performance AI training cluster?
A) They enable the use of lower-cost Ethernet switches in place of InfiniBand fabrics without any performance degradation.
B) They allow GPUs to communicate directly with each other and with NVMe storage, bypassing the host CPU and system memory, which significantly reduces I/O latency and CPU overhead.
C) They automatically encrypt data in-flight between the GPU servers and the storage system.
D) They reduce the power consumption of the storage array by offloading checksum calculations.
2. A data scientist needs to launch a Jupyter notebook as a pod in a Kubernetes cluster. The pod requires a 50 Gi persistent volume for storing datasets and notebooks. The cluster administrator has configured a default Trident StorageClass for general-purpose use. The data scientist has the following PersistentVolumeClaim (PVC) manifest:
apiVersion: v1
kind: PersistentVolumeClaim
metadata:
name: jupyter-pvc
spec:
accessModes:
- ReadWriteOnce
resources:
requests:
storage: 50Gi
When this PVC is applied to the cluster, what will be the result?
A) Trident will automatically provision a 50 Gi volume on its default backend and bind it to the PVC.
B) Trident will create a 1 Gi volume, as this is the default size for all PVCs.
C) The PVC will remain in a "Pending" state until a PersistentVolume is manually created.
D) The PVC will fail because a 'storageClassName' is not explicitly defined.
3. To meet HIPAA compliance, the first step in the data pipeline is to identify all medical scans that contain embedded PII. The solution must be automated and capable of scanning data in-place on the on-premises ASA system.
Which two technologies should be used to accomplish this identification and tagging task?
(Choose 2.)
A) NetApp BlueXP classification, configured to scan the on-premises ASA working environment.
B) NetApp SnapLock to make the source data immutable before scanning.
C) A manual review process where technicians visually inspect each scan for PII.
D) A custom Python script that uses regular expressions to search file contents.
E) A custom "PII" category within BlueXP classification to identify specific medical record number formats.
4. An online retail company's recommendation engine, which provides real-time product suggestions to users, is experiencing unacceptable latency. The inference application is running on a correctly-sized edge server, but user requests are taking over 500ms to process. An architect reviews the data access pattern and infrastructure diagram.
Application_Location: Edge Server (In-store)
Data_Source_Location: Core Data Center (On-premises ONTAP)
Data_Required_for_Inference: User profile data, product catalog vectors Network_Path: Edge -> WAN -> Core Data Center Observed_Latency: 550ms What is the most likely cause of the high inference latency?
A) Every inference request requires a high-latency round trip over the WAN to fetch data from the core data center.
B) The edge server has insufficient CPU resources to run the model.
C) The model is too large to fit into the edge server's memory.
D) The on-premises ONTAP system is not configured for high-throughput.
5. 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?
A) The application pod has incorrect mount options configured.
B) The ONTAP system's data LIF is offline.
C) A network firewall between the pod's subnet and the storage subnet is blocking the required NFS ports.
D) The AFF A-Series does not support the NFS protocol.
Solutions:
| Question # 1 Answer: B | Question # 2 Answer: A | Question # 3 Answer: A,E | Question # 4 Answer: A | Question # 5 Answer: C |
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