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Network Appliance NS0-901 Exam Syllabus Topics:

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

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

NEW QUESTION # 74
A financial services company has deployed a real-time fraud detection model at the edge. The model is designed for low-latency inference. However, monitoring reports indicate that the infrastructure costs are excessively high, and GPU utilization is consistently low. The architect reviews the deployment configuration.
Instance_Type: NVIDIA DGX A100 (8 GPUs)
Storage_Tier: High-Performance All-Flash (NetApp ASA)
Network: 100GbE RoCE
GPU_Utilization_Avg: 5%
Monthly_Cost: $15,000
Workload_Profile: Low-volume, sporadic, real-time predictions
What is the most likely cause of the high costs and low utilization?

Answer: D


NEW QUESTION # 75
The architect is designing the complete, automated data pipeline from the on-premises data center to the Azure cloud for this medical imaging project. The design must prioritize security, efficiency, and reproducibility.
Which sequence of actions provides the most robust and automated solution?

Answer: A


NEW QUESTION # 76
An AI architect is designing a storage solution for a new training cluster. The primary workload consists of training large language models, which involves sequential reads of massive datasets.
The key requirement is to maximize GPU utilization by providing the highest possible data throughput. Cost is a secondary concern to performance.
Which NetApp storage system is the most appropriate choice for this workload?

Answer: D


NEW QUESTION # 77
An AI platform is suffering from poor performance during distributed training jobs. The training data resides on a single, large NFS volume. Monitoring shows that while the overall network throughput to the storage system is high, individual GPU nodes experience significant I/O wait times, and the single ONTAP volume is becoming a performance bottleneck. The goal is to re- architect the storage layout to maximize read parallelism and throughput for the training cluster.
Which two actions should the architect take to address this performance bottleneck? (Choose 2.)

Answer: C,D


NEW QUESTION # 78
The data science team in Azure reports that training jobs are taking longer than expected. An analysis of the Cloud Volumes ONTAP instance in Azure shows that the instance type is undersized for the I/O demands of the training workload. The architect needs to change the Azure VM instance type for the Cloud Volumes ONTAP system to a more powerful one.
The current configuration is:
Cloud_Provider: Azure
ONTAP_System: Cloud Volumes ONTAP (Single Node)
Current_Instance_Type: Standard_DS3_v2
Target_Instance_Type: Standard_E8s_v4
What is the most direct method to perform this operation using NetApp's management tools?

Answer: A


NEW QUESTION # 79
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