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Network Appliance NS0-901 Exam Syllabus Topics:

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

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Network Appliance certification NS0-901 the latest exam questions and answers

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Network Appliance NetApp Certified AI Expert Exam Sample Questions (Q50-Q55):

NEW QUESTION # 50
An AI architect needs to design a complete, end-to-end data pipeline for a new generative AI application at a financial services firm. The application will allow internal analysts to query a massive, 500 TB archive of historical market data and reports to generate summaries. The firm has the following environment and requirements:
Data_Sources: A mix of on-premises ONTAP filers and StorageGRID S3 buckets.
Requirement_1: All queries must be answered using only the private data archive.
Requirement_2: All generated summaries must provide citations to the source reports.
Requirement_3: All data containing client PII must be identified and excluded from the LLM context.
Requirement_4: The solution must be cost-effective for the large, mostly-read data archive.
Which set of actions and technologies constitutes the most robust and compliant solution?
(Select all that apply.)

A. Use SnapMirror to replicate the StorageGRID data lake to a high-performance NetApp ASA system for faster query performance.
B. Deploy BlueXP classification to scan the entire StorageGRID data lake to identify and tag all files containing PII.
C. During the RAG retrieval step, filter out any documents tagged as containing PII by BlueXP classification before sending them to the LLM.
D. Implement a Retrieval-Augmented Generation (RAG) architecture to meet the requirements for private data usage and source citation.
E. Use NetApp XCP to perform a one-time migration of all data from the ONTAP filers to the StorageGRID data lake.
F. Fine-tune a foundation model on the entire 500 TB dataset to ensure it understands the financial context.

Answer: B,C,D,E

NEW QUESTION # 51
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. A network firewall between the pod's subnet and the storage subnet is blocking the required NFS ports.
B. The AFF A-Series does not support the NFS protocol.
C. The application pod has incorrect mount options configured.
D. The ONTAP system's data LIF is offline.

Answer: A

NEW QUESTION # 52
A new team of external auditors requires read-only access to the raw financial product documentation stored in the StorageGRID data lake. The data is in a bucket named 'prod-docs'.
The security team must ensure the auditors can list and read objects but cannot write, delete, or modify any data. Which is the most appropriate method to grant this specific access?

A. Configure an NFS export policy on the 'prod-docs' bucket and provide the auditors with the mount path.
B. Create a new group and user within StorageGRID, and attach an S3 group policy that explicitly allows only 's3:GetObject' and 's3:ListBucket' actions on the 'prod-docs' bucket.
C. Provide the auditors with the root access keys for the StorageGRID system.
D. Create a new StorageGRID tenant account for the auditors with full S3 access.

Answer: B

NEW QUESTION # 53
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 FabricPool
B. NetApp XCP
C. NetApp SnapMirror
D. NetApp FlexClone

Answer: D

NEW QUESTION # 54
The firm decides to expand the "Advisor Assistant" project to a new team in a different department. This team needs its own isolated environment. An MLOps engineer attempts to submit a new GPU- intensive job for the new team, but it remains pending. The engineer checks the Run:AI scheduler logs and finds the following entry:
time="2025-07-11T16:45:00Z" level=info msg="Job ds-new-team-job1 cannot be scheduled.
Project 'new-team-project' has exceeded its GPU quota. Quota: 0, Requested: 1, Used: 0." What is the root cause of the scheduling failure?

A. The Kubernetes cluster has no available GPUs.
B. A Run:AI project quota has been configured for the new team, but it has been set to zero, effectively blocking them from using any GPU resources.
C. The Run:AI scheduler is offline and cannot process new jobs.
D. The job is requesting a specific type of GPU that is not available in the cluster.

Answer: B

NEW QUESTION # 55
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