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Network Appliance NS0-901 Exam Syllabus Topics:

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

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Network Appliance NetApp Certified AI Expert Exam Sample Questions (Q59-Q64):

NEW QUESTION # 59
The company's finance department mandates a cost-control strategy for the petabyte-scale StorageGRID data lake. The analysis shows that 80% of the simulation data is not accessed after
90 days but must be retained for five years for regulatory compliance. Which StorageGRID feature should the architect use to automatically reduce the long-term storage costs for this inactive data?

A. Configure an Information Lifecycle Management (ILM) policy that automatically moves objects older than 90 days to a lower-cost cloud archive tier, such as Amazon S3 Glacier Deep Archive.
B. Implement a FabricPool policy to tier the data to a NetApp ASA system.
C. Enable deduplication and compression on all StorageGRID storage nodes.
D. Use NetApp SnapMirror to replicate the cold data to a different, lower-cost StorageGRID cluster.

Answer: A

NEW QUESTION # 60
An organization's AI platform team needs to provide two distinct tiers of storage for their data scientists on a single Kubernetes cluster:
1. 'gold-tier': Extremely low-latency storage for active model training, using an all-flash NetApp ASA system.
2. 'bronze-tier': Cost-effective, high-capacity storage for data staging and archiving, using a NetApp StorageGRID system.
How should the platform team configure NetApp Trident to meet these requirements? (Select all that apply.)

A. Configure the 'gold-tier' StorageClass to reference the ASA backend.
B. Create a single Trident backend that points to both the ASA and StorageGRID systems simultaneously.
C. Create two separate Trident backend configurations, one for the ASA ('backend-asa.yaml') and one for StorageGRID ('backend-sgrid.yaml').
D. Configure the 'bronze-tier' StorageClass to reference the StorageGRID backend.
E. Create two distinct StorageClasses, 'gold-tier' and 'bronze-tier'.
F. Create a single StorageClass named 'multi-tier-storage' that references both backends.

Answer: A,C,D,E

NEW QUESTION # 61
An AI architect is designing a solution for a legal firm. The primary goal is to allow lawyers to ask natural language questions about case law stored in a private, 50 TB document repository.
The key project constraints are as follows:
Project_Goal: Answer questions using proprietary, real-time legal documents.
Constraint_1: Must not alter the foundational LLM's weights due to compliance.
Constraint_2: Case law database is updated daily with new rulings.
Constraint_3: All generated answers must be traceable to a source document.
Which technology should the architect choose as the core of this solution?

A. A new LLM trained from scratch on the legal documents.
B. A predictive AI model to classify legal documents.
C. A fine-tuning pipeline to update the LLM daily.
D. A Retrieval-Augmented Generation (RAG) architecture.

Answer: D

NEW QUESTION # 62
What is the primary architectural benefit of using technologies like RDMA (Remote Direct Memory Access) and GPUDirect Storage in a high-performance AI training cluster?

A. They reduce the power consumption of the storage array by offloading checksum calculations.
B. They enable the use of lower-cost Ethernet switches in place of InfiniBand fabrics without any performance degradation.
C. They allow GPUs to communicate directly with each other and with NVMe storage, bypassing the host CPU and system memory, which significantly reduces I/O latency and CPU overhead.
D. They automatically encrypt data in-flight between the GPU servers and the storage system.

Answer: C

NEW QUESTION # 63
The "Advisor Assistant" application, running as a pod in Kubernetes, suddenly cannot access its data on the AFF A-Series. The application logs show "connection timed out" errors. The network team provides a firewall log snippet for the traffic between the application pod and the storage system's NFS data LIF.
TIME | SRC_IP | DST_IP | PROTO | DST_PORT | ACTION
-|--||-|-|-
2025-07-11T16:01:10Z | 10.20.5.101 (Pod) | 10.20.10.55 (LIF) | TCP | 111 | BLOCKED 2025-07-
11T16:01:12Z | 10.20.5.101 (Pod) | 10.20.10.55 (LIF) | TCP | 2049 | BLOCKED What is the most likely cause of the connectivity failure?

A. The ONTAP system's data LIF is offline.
B. The AFF A-Series does not support the NFS protocol.
C. A network firewall between the pod's subnet and the storage subnet is blocking the required NFS ports.
D. The application pod has incorrect mount options configured.

Answer: C

NEW QUESTION # 64
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