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Network Appliance NS0-901 Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: AI Overview	15%	- Algorithm types: supervised, unsupervised, reinforcement learning - Convergence of AI, high-performance computing, and analytics - AI, machine learning, and deep learning concepts - AI deployment models: on-premises, cloud, edge - AI industry use cases and applications
Topic 2: Cloud and Hybrid Cloud AI Deployment	18%	- Data mobility and consistency across environments - Cloud-native AI solutions and integration - NetApp cloud data services for AI - Hybrid and multi-cloud AI architectures
Topic 3: Security, Reliability, and Operations	15%	- Monitoring, logging, and troubleshooting AI environments - Data security and access control for AI - Cost management and efficiency - High availability and data protection
Topic 4: NetApp AI Solutions and Architecture	25%	- NetApp AI-ready infrastructure components - ONTAP integration with AI frameworks - Scalability and performance optimization for AI - Storage architectures for AI workloads - Data management and data pipeline design
Topic 5: AI Lifecycle	27%	- Data preparation and management for AI - AI lifecycle stages: design, training, deployment, monitoring - AI governance, ethics, and compliance - Predictive vs generative AI - Model training, inference, and optimization

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Network Appliance NetApp Certified AI Expert Exam Sample Questions (Q77-Q82):

NEW QUESTION # 77
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 storage system cannot deliver data fast enough to the compute node.
D. The training script is CPU-bound, likely performing data augmentation or preprocessing on the CPU instead of offloading it to the GPUs.

Answer: D

NEW QUESTION # 78
A security administrator is reviewing the configuration of a production ONTAP cluster after a compliance audit. The audit requires that all volumes containing financial data must be protected against ransomware. The administrator runs a command to check the status of Autonomous Ransomware Protection (ARP) on a critical volume.
The command and its output are as follows:
cluster-1::> security ransomware anti-ransomware show -vserver svm_finance -volume finance_q1_data Vserver: svm_finance Volume: finance_q1_data Autonomous Ransomware Protection Status: disabled Learning Mode: normal Dry Run Mode: false Action on Detection: notify Based on this output, what is the primary security risk for the 'finance_q1_data' volume?

A. The action on detection is set only to "notify," which will not block the attack.
B. The volume is not part of any Snapshot schedule.
C. The volume is vulnerable to ransomware because Autonomous Ransomware Protection is not enabled.
D. The learning mode is set to "normal," which may not detect zero-day attacks.

Answer: C

NEW QUESTION # 79
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?

A. NetApp ASA (All-SAN Array)
B. NetApp C-Series
C. NetApp StorageGRID
D. NetApp E-Series

Answer: A

NEW QUESTION # 80
What is the primary difference between C-Series and A-Series storage for AI workloads?

A. C-Series is designed for high-performance workloads, while A-Series focuses on cost-efficiency for general storage.
B. C-Series supports both file and object-based storage, while A-Series is optimized for object storage.
C. A-Series provides GPU-based acceleration, while C-Series only supports CPU-based workloads.
D. A-Series is for smaller-scale AI workloads, while C-Series supports large-scale, high-performance AI applications.

Answer: A

NEW QUESTION # 81
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?

A. The storage tier is too slow, causing the GPUs to wait for data.
B. The network latency is too high for an edge deployment.
C. The compute and storage infrastructure is sized for a large-scale training workload, not a lightweight inference workload.
D. The model was trained using supervised learning, which is inefficient for fraud detection.

Answer: C

NEW QUESTION # 82
......

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