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Network Appliance NS0-901 Exam Syllabus Topics:

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

>> Valid NS0-901 Exam Objectives <<

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Network Appliance NetApp Certified AI Expert Exam Sample Questions (Q46-Q51):

NEW QUESTION # 46
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. Deploy BlueXP classification to scan the entire StorageGRID data lake to identify and tag all files containing PII.
B. Fine-tune a foundation model on the entire 500 TB dataset to ensure it understands the financial context.
C. Use SnapMirror to replicate the StorageGRID data lake to a high-performance NetApp ASA system for faster query performance.
D. Use NetApp XCP to perform a one-time migration of all data from the ONTAP filers to the StorageGRID data lake.
E. During the RAG retrieval step, filter out any documents tagged as containing PII by BlueXP classification before sending them to the LLM.
F. Implement a Retrieval-Augmented Generation (RAG) architecture to meet the requirements for private data usage and source citation.

Answer: A,D,E,F

A. Prioritize a RAG architecture to meet the critical requirements for factual accuracy and data traceability.
B. Use RAG exclusively, as prompt engineering alone can fully replicate a specific brand voice and persona.
C. Propose a hybrid approach where a base model is first lightly fine-tuned for persona, then used within a RAG system for factual grounding.
D. Recommend training a new LLM from scratch to ensure both brand voice and factual accuracy are built-in.
E. Prioritize fine-tuning to embed the company's brand voice and persona into the model.

Answer: A,C

NEW QUESTION # 48
A training job on one of the NVIDIA DGX servers is running slowly. A performance engineer runs the 'dstat' command on the server and captures the following output during the job execution.
-total-cpu-usage- -dsk/total- -net/total- paging-- system-- usr sys idl wai hiq siq| read writ| recv send| in out | int csw 15 5 70 10 0 0| 1.2G 15.0M| 1.2G 12.0M| 0 0 | 15k 35k
14 6 69 11 0 0| 1.2G 14.3M| 1.2G 11.8M| 0 0 | 14k 33k
16 5 68 11 0 0| 1.2G 16.1M| 1.2G 13.1M| 0 0 | 16k 36k
The server is connected to the NetApp ASA via a 100GbE (12.5 GB/s) network.
What is the most likely performance bottleneck based on this data?

A. The server's network interface is saturated.
B. The server's CPU is overloaded.
C. The application is experiencing excessive memory paging.
D. The storage system (ASA) is unable to provide sufficient read throughput.

Answer: A

NEW QUESTION # 49
An online retail company's recommendation engine, which provides real-time product suggestions to users, is experiencing unacceptable latency. The inference application is running on a correctly-sized edge server, but user requests are taking over 500ms to process. An architect reviews the data access pattern and infrastructure diagram.
Application_Location: Edge Server (In-store)
Data_Source_Location: Core Data Center (On-premises ONTAP)
Data_Required_for_Inference: User profile data, product catalog vectors Network_Path: Edge -> WAN -> Core Data Center Observed_Latency: 550ms What is the most likely cause of the high inference latency?

A. The model is too large to fit into the edge server's memory.
B. The edge server has insufficient CPU resources to run the model.
C. Every inference request requires a high-latency round trip over the WAN to fetch data from the core data center.
D. The on-premises ONTAP system is not configured for high-throughput.

Answer: C

NEW QUESTION # 50
Which of the following applications use AI in the healthcare industry? (Choose two)

A. Managing hospital supply chains
B. Diagnosing diseases
C. Predicting patient outcomes
D. Automating financial reporting

Answer: B,C

NEW QUESTION # 51
......

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