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Network Appliance NS0-901 Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: NetApp AI Solutions and Architecture25%- Storage architectures for AI workloads
- Data management and data pipeline design
- Scalability and performance optimization for AI
- NetApp AI-ready infrastructure components
- ONTAP integration with AI frameworks
Topic 2: Cloud and Hybrid Cloud AI Deployment18%- Cloud-native AI solutions and integration
- NetApp cloud data services for AI
- Data mobility and consistency across environments
- Hybrid and multi-cloud AI architectures
Topic 3: AI Lifecycle27%- Model training, inference, and optimization
- AI lifecycle stages: design, training, deployment, monitoring
- AI governance, ethics, and compliance
- Data preparation and management for AI
- Predictive vs generative AI
Topic 4: AI Overview15%- Convergence of AI, high-performance computing, and analytics
- Algorithm types: supervised, unsupervised, reinforcement learning
- AI deployment models: on-premises, cloud, edge
- AI industry use cases and applications
- AI, machine learning, and deep learning concepts
Topic 5: Security, Reliability, and Operations15%- Cost management and efficiency
- Data security and access control for AI
- Monitoring, logging, and troubleshooting AI environments
- High availability and data protection

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New Network Appliance NS0-901 Test Papers | Latest NS0-901 Exam Question

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Network Appliance NetApp Certified AI Expert Exam Sample Questions (Q71-Q76):

NEW QUESTION # 71
Which of the following techniques are used to maximize GPU utilization in AI workloads? (Choose two)

Answer: B,D


NEW QUESTION # 72
The firm decides to expand the "Advisor Assistant" project to a new team in a different department. This team needs its own isolated environment. An MLOps engineer attempts to submit a new GPU- intensive job for the new team, but it remains pending. The engineer checks the Run:AI scheduler logs and finds the following entry:
time="2025-07-11T16:45:00Z" level=info msg="Job ds-new-team-job1 cannot be scheduled.
Project 'new-team-project' has exceeded its GPU quota. Quota: 0, Requested: 1, Used: 0." What is the root cause of the scheduling failure?

Answer: B


NEW QUESTION # 73
The AI training jobs on the AIPod are performing below expectations. The NVIDIA DGX servers' GPUs show low utilization. A performance analysis reveals that the bottleneck is not the storage system itself, but the network path between the storage and the compute nodes.
The current network configuration is as follows:
Network_Fabric: 100GbE Standard Ethernet
Protocol: NFS over TCP/IP
Data_Path: Storage -> Host CPU -> GPU Memory
Which network architecture enhancement would provide the most significant performance improvement by reducing latency and CPU overhead?

Answer: C


NEW QUESTION # 74
An AI training job is running slower than expected. The infrastructure team suspects a compute bottleneck. The job involves processing high-resolution images with a complex convolutional neural network (CNN). They review the logs from the training script.
Epoch 1/100 - 3600s - loss: 1.253 - acc: 0.54
...
CPU_Utilization_During_Epoch: 99% (all cores)
GPU_0_Utilization_During_Epoch: 8%
GPU_1_Utilization_During_Epoch: 7%
...
Epoch 2/100 - 3610s - loss: 1.102 - acc: 0.61
What is the most likely cause of the poor training performance?

Answer: B


NEW QUESTION # 75
An organization recently suffered a ransomware attack that encrypted several volumes on their primary ONTAP storage system, including a critical volume containing curated training data. The security team needs to implement a solution that can proactively detect and block ransomware- like file I/O patterns and automatically create a secure Snapshot copy before any damage is done.
The current ONTAP configuration is as follows:
ONTAP_Version: 9.12.1
Security_Features: SnapLock (Compliance Mode) on archive volumes
Anti-Virus_Scan: Enabled (Vscan)
Ransomware_Detection: Not configured
Which ONTAP feature should be enabled to provide this proactive, automated protection?

Answer: B


NEW QUESTION # 76
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