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Network Appliance NS0-901 Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: AI Common Challenges	22%	- Traceability and Optimization 1. Ensuring traceability for code, data, and models 2. Optimizing data access and movement 3. Maximizing performance in demanding AI workloads - Resource Management 1. Sizing storage and compute resources effectively 2. Controlling costs and securing storage
Topic 2: AI Hardware Architectures	18%	- Infrastructure Topologies 1. Data aggregation and compute topologies - Networking and Storage 1. Storage architectures for AI 2. Network protocols for AI workloads - NetApp Architectures 1. SuperPOD 2. OVX architectures 3. BasePod
Topic 3: AI Overview	15%	- AI Deployment Models 1. Benefits and risks of each model 2. Edge 3. Cloud 4. On-premises - AI Convergence with HPC and Analytics 1. Leveraging shared infrastructure for AI, HPC, and analytics - AI Industry Applications 1. Healthcare applications 2. Digital twins 3. Agents - Algorithm Types 1. Reinforcement learning 2. Supervised learning 3. Unsupervised learning - Training vs. Inferencing vs. Predictions 1. Distinguish between training and inference workloads - Machine Learning Fundamentals 1. Understand the relationship between AI, machine learning, and deep learning 2. Describe machine learning benefits
Topic 4: AI Software Architectures	18%	- Development Tools 1. NetApp DataOps Toolkit 2. Jupyter notebooks vs. pipelines - MLOps and LLMOps Ecosystems 1. Understanding the software tools and platforms enabling AI at scale - Scaling and Orchestration 1. Scaling AI workloads with Kubernetes 2. Leveraging BlueXP software tools
Topic 5: AI Lifecycle	27%	- Data Preparation 1. XCP and CopySync 2. NetApp BlueXP Classification 3. Data aggregation and cleansing - Generative AI Concepts 1. Fine-tuning 2. Retrieval Augmented Generation (RAG) 3. Hallucinations - Model Development 1. Fine-tuning workflows 2. Inferencing 3. Model building - Predictive AI vs. Generative AI 1. Distinction between predictive and generative AI 2. Large Language Models (LLMs) 3. Impact of generative content (text, images, video, decision-making)

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Network Appliance NetApp Certified AI Expert Exam Sample Questions (Q52-Q57):

NEW QUESTION # 52
The architect needs to design an efficient data flow to move curated training sets from the central StorageGRID data lake to the high-performance NetApp ASA system used by the AI cluster. The process must be manageable from a single interface and should be automatable. Which two NetApp technologies should be used to implement this data pipeline stage? (Choose 2.)

A. NetApp FabricPool to automatically tier the data from StorageGRID to the ASA.
B. NetApp SnapMirror to replicate data from the StorageGRID object store to the ASA's file system.
C. A manual copy process using an S3 client on a bastion host.
D. NetApp BlueXP copy and sync service to create and manage the data synchronization relationship between the S3 source and the NFS/CIFS destination.
E. A custom Python script using the NetApp DataOps Toolkit to trigger the data movement.

Answer: D,E

NEW QUESTION # 53
A national research laboratory is investing in a turnkey AI infrastructure solution. Their primary goal is to eliminate the complexity and risk of designing and integrating the compute, network, and storage components themselves. The solution must be pre-validated by the vendors to deliver predictable, linear performance as they scale from one to multiple compute nodes. Which two options represent this type of pre-validated, converged infrastructure solution for AI? (Choose two)

A. A single NetApp FAS system connected directly to a single server.
B. A custom-built server rack with components sourced from various different vendors.
C. NetApp AIPod, which integrates NVIDIA DGX servers with NetApp storage and networking.
D. A Cisco and NetApp FlexPod for AI solution.
E. A public cloud provider's general-purpose virtual machine instances.

Answer: C,D

NEW QUESTION # 54
Which of the following platforms can be used to manage containerized AI workloads on Kubernetes? (Choose two)

A. Google VertexAI
B. TensorFlow Extended
C. RunAI
D. KubeFlow

Answer: C,D

NEW QUESTION # 55
What is the primary architectural advantage of using a NetApp AIPod with NVIDIA DGX servers for the AI training cluster, as described in the scenario?

A. It exclusively uses object storage, which simplifies access for data scientists using S3-native tools.
B. It is designed for small-scale, departmental AI projects and cannot be scaled.
C. It is a reference architecture that is pre-validated by NetApp and NVIDIA to eliminate design complexity and ensure predictable performance for AI workloads.
D. It prioritizes CPU performance over GPU performance for traditional machine learning algorithms.

Answer: C

NEW QUESTION # 56
A junior administrator is attempting to delete an old, unused Snapshot copy from a production volume to reclaim space but receives an error. The administrator is certain they are using the correct command and have the appropriate 'vsadmin' role.
The command and error are shown below:
cluster-1::> volume snapshot delete -vserver svm_prod -volume app_data -snapshot nightly.2025- 06-15_0015 Error: command failed: This operation is not permitted. Deletion of Snapshot copy "nightly.2025-
06-15_0015" on volume "app_data" in Vserver "svm_prod" requires approval.
Use the "security multi-admin-verify approval show" command to view pending approvals.
What is the most likely reason the administrator cannot delete the Snapshot?

A. Multi-Admin Verification (MAV) is enabled, and the destructive operation requires approval from another designated administrator.
B. The 'vsadmin' role does not have permission to delete Snapshots.
C. The Snapshot is locked by a SnapMirror relationship.
D. The volume is protected by SnapLock, and the Snapshot has not reached its expiry date.

Answer: A

NEW QUESTION # 57
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