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Network Appliance NS0-901 Exam Syllabus Topics:

Section	Weight	Objectives
NetApp AI Solutions and Architecture	25%	- Storage architectures for AI workloads - ONTAP integration with AI frameworks - Data management and data pipeline design - NetApp AI-ready infrastructure components - Scalability and performance optimization for AI
AI Lifecycle	27%	- AI lifecycle stages: design, training, deployment, monitoring - Data preparation and management for AI - Predictive vs generative AI - Model training, inference, and optimization - AI governance, ethics, and compliance
AI Overview	15%	- AI, machine learning, and deep learning concepts - Convergence of AI, high-performance computing, and analytics - AI industry use cases and applications - AI deployment models: on-premises, cloud, edge - Algorithm types: supervised, unsupervised, reinforcement learning
Security, Reliability, and Operations	15%	- Monitoring, logging, and troubleshooting AI environments - Data security and access control for AI - High availability and data protection - Cost management and efficiency
Cloud and Hybrid Cloud AI Deployment	18%	- Cloud-native AI solutions and integration - Hybrid and multi-cloud AI architectures - NetApp cloud data services for AI - Data mobility and consistency across environments

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Network Appliance NetApp Certified AI Expert Exam Sample Questions (Q81-Q86):

NEW QUESTION # 81
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?

A. Install faster CPUs in the AI cluster's storage controllers.
B. Implement a converged data infrastructure where both the HPC and AI clusters access a single, high- performance data lake built on NetApp storage.
C. Upgrade the network connection between the two storage systems to 200GbE.
D. Use NetApp XCP to perform the data copy, as it is faster than a standard NFS copy.

Answer: B

NEW QUESTION # 82
A network administrator, attempting to harden the security of the data center, modifies a firewall access control list (ACL). Immediately afterward, the "Advisor Assistant" application pods can no longer mount their required NFS volumes from the AFF A-Series. The MLOps team confirms the pods are stuck in a 'ContainerCreating' state with a 'MountVolume.SetUp failed... connection timed out' error. The administrator provides the new, active firewall rule:
RULE | ACTION | PROTOCOL | SOURCE_IP_RANGE | DEST_IP_RANGE | DEST_PORT --|--|-
|--||--51 | ALLOW | TCP | 10.20.5.0/24 | 10.20.10.0/24 | 2049
What is the most likely reason for the mount failures?

A. The firewall rule is blocking the NFS lock manager (NLM) protocol, which is required for file locking.
B. The firewall rule is blocking the Portmapper/RPCbind service (TCP/UDP port 111), which is necessary for the initial NFS mount negotiation.
C. The firewall rule is blocking the NFSv4 protocol, which requires TCP port 2050.
D. The 'SOURCE_IP_RANGE' is incorrect and does not include the Kubernetes pod IP addresses.

Answer: B

NEW QUESTION # 83
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?

A. The model is too simple and does not effectively utilize the GPU's parallel processing capabilities.
B. The network is saturated, preventing the GPUs from receiving data.
C. The training script is CPU-bound, likely performing data augmentation or preprocessing on the CPU instead of offloading it to the GPUs.
D. The storage system cannot deliver data fast enough to the compute node.

Answer: C

NEW QUESTION # 84
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.)

A. Enable GPUDirect Storage on the ASA system to provide the lowest latency data path to the GPUs.
B. Use a standard 10GbE network for all connectivity to reduce costs.
C. Implement NetApp FabricPool to automatically tier inactive data from the ASA system to the StorageGRID data lake.
D. Use a NetApp All-SAN Array (ASA) system for the 50 TB high-performance "hot" research area.
E. Use NetApp StorageGRID to build the 5 PB cost-effective data lake.
F. Use NetApp E-Series systems for both the hot tier and the cold data lake to simplify management.

Answer: A,C,D,E

NEW QUESTION # 85
Which of the following are typical requirements for inferencing in AI models? (Choose two)

A. Loading the model into memory
B. Cloud-based inference only
C. Extensive training data
D. Pre-trained models

Answer: A,D

NEW QUESTION # 86
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