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Network Appliance NS0-901 Exam Syllabus Topics:

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

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Network Appliance NetApp Certified AI Expert Exam Sample Questions (Q51-Q56):

NEW QUESTION # 51
An architect is designing the storage and network infrastructure for a new, large-scale AI cluster dedicated to training foundational models. The primary design goal is to achieve the highest possible data throughput and the lowest latency to ensure multi-million dollar GPU resources are never idle. Which two technologies are essential to include in the design to achieve this goal?
(Choose 2.)

A. An InfiniBand or RoCE-capable Ethernet network fabric.
B. NetApp StorageGRID as the primary storage for the training datasets.
C. GPUDirect Storage support on the storage system.
D. A tiered storage architecture using NetApp FabricPool.
E. A 10GbE Ethernet network for all data traffic.

Answer: A,C

NEW QUESTION # 52
A data scientist needs to test a new data normalization technique. To do this, they require an isolated, writable copy of a 50 TB curated simulation dataset that resides on the NetApp ASA system. The operation must be completed as quickly as possible and consume minimal additional storage space. Which NetApp technology is the most appropriate solution for this requirement?

A. NetApp FlexClone
B. NetApp FabricPool
C. NetApp XCP
D. NetApp SnapMirror

Answer: A

NEW QUESTION # 53
An AI architect is planning the resource allocation for a new project. The primary task is to process millions of unlabeled customer reviews to identify naturally occurring groups or themes without any prior guidance.
The project requirements are summarized below:
Task: Discover hidden patterns in text data
Input_Data: 10 million unlabeled text reviews
Output: Clustered groups of related reviews
Supervision: None
Which type of machine learning algorithm is required for this task?

A. Unsupervised learning
B. Supervised learning
C. Predictive learning
D. Reinforcement learning

Answer: A

NEW QUESTION # 54
An AI platform team is investigating poor I/O performance for a specific workload that involves processing hundreds of thousands of small metadata files. The application is running on a Kubernetes cluster with storage provided by a NetApp ONTAP system over NFS. Performance metrics show acceptable network throughput but very high latency for metadata operations (e.g., open, stat, close).
The current storage configuration is as follows:
Storage_System: NetApp AFF A-Series
Protocol: NFSv4.1
Workload_Profile: Metadata-intensive, many small file lookups
Observed_Issue: High latency on metadata operations, slow job completion Which storage architecture would be better suited to handle this specific metadata-intensive workload?

A. A NetApp E-Series system running a parallel file system like BeeGFS.
B. A NetApp Cloud Volumes ONTAP instance in a different region.
C. A NetApp SnapLock-enabled volume for data protection.
D. A NetApp StorageGRID object storage system.

Answer: A

NEW QUESTION # 55
A research institute is designing an infrastructure to support its entire AI drug discovery pipeline.
The pipeline has two distinct workload requirements:
1. Training: A team of data scientists needs to train several large transformer models simultaneously using a 500 TB dataset of genomic sequences. This process requires maximum data throughput to keep the GPUs saturated.
2. Inference: Once trained, the models are deployed to an internal web portal where researchers submit individual protein sequences for analysis. These queries must return results with the lowest possible latency.
Which infrastructure design best satisfies both requirements? (Choose 2.)

A. Implement NetApp FlexCache on smaller nodes at the network edge to serve the inference requests.
B. Configure QoS minimums on the training volumes to ensure they do not impact inference performance.
C. Use NetApp StorageGRID as the primary storage for both the training and low-latency inference workloads.
D. Deploy a large NetApp ASA cluster with GPUDirect Storage enabled for the training environment.
E. Use a single, large Cloud Volumes ONTAP instance in a public cloud to handle both workloads to simplify management.

Answer: A,D

NEW QUESTION # 56
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