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Network Appliance NS0-901 Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: 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. Fine-tuning workflows 2. Model building 3. Inferencing - Generative AI Concepts 1. Hallucinations 2. Retrieval Augmented Generation (RAG) 3. Fine-tuning - Data Preparation 1. Data aggregation and cleansing 2. NetApp BlueXP Classification 3. XCP and CopySync
Topic 2: AI Overview	15%	- Machine Learning Fundamentals 1. Understand the relationship between AI, machine learning, and deep learning 2. Describe machine learning benefits - AI Industry Applications 1. Agents 2. Healthcare applications 3. Digital twins - AI Convergence with HPC and Analytics 1. Leveraging shared infrastructure for AI, HPC, and analytics - Algorithm Types 1. Supervised learning 2. Unsupervised learning 3. Reinforcement learning - Training vs. Inferencing vs. Predictions 1. Distinguish between training and inference workloads - AI Deployment Models 1. On-premises 2. Benefits and risks of each model 3. Cloud 4. Edge
Topic 3: 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
Topic 4: 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 5: AI Software Architectures	18%	- Scaling and Orchestration 1. Leveraging BlueXP software tools 2. Scaling AI workloads with Kubernetes - Development Tools 1. Jupyter notebooks vs. pipelines 2. NetApp DataOps Toolkit - MLOps and LLMOps Ecosystems 1. Understanding the software tools and platforms enabling AI at scale

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Network Appliance NetApp Certified AI Expert Exam Sample Questions (Q74-Q79):

NEW QUESTION # 74
Given the firm's requirements for using a private, constantly updated knowledge base and the strict mandate for data traceability, which AI architecture is the most appropriate foundation for the "Advisor Assistant" chatbot?

A. A standalone LLM deployed in an air-gapped environment with no access to the document repository.
B. A predictive AI model trained to forecast market trends.
C. A fine-tuning architecture where a base LLM is continuously retrained on the entire document repository.
D. A Retrieval-Augmented Generation (RAG) architecture that retrieves relevant context from a local vector database to enrich prompts sent to the LLM.

Answer: D

NEW QUESTION # 75
The HPC cluster generates simulation data at an extremely high rate, requiring a storage system that can handle massively parallel writes from hundreds of compute nodes simultaneously. Which storage system and file protocol combination is the most appropriate choice for the HPC cluster's high-performance scratch space?

A. A NetApp StorageGRID system accessed via the S3 protocol.
B. A NetApp ASA system serving a single, large NFS volume.
C. A Cloud Volumes ONTAP instance with a standard file system.
D. A NetApp E-Series system serving a BeeGFS parallel file system.

Answer: D

NEW QUESTION # 76
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. During the RAG retrieval step, filter out any documents tagged as containing PII by BlueXP classification before sending them to the LLM.
B. Use NetApp XCP to perform a one-time migration of all data from the ONTAP filers to the StorageGRID data lake.
C. Implement a Retrieval-Augmented Generation (RAG) architecture to meet the requirements for private data usage and source citation.
D. Fine-tune a foundation model on the entire 500 TB dataset to ensure it understands the financial context.
E. Use SnapMirror to replicate the StorageGRID data lake to a high-performance NetApp ASA system for faster query performance.
F. Deploy BlueXP classification to scan the entire StorageGRID data lake to identify and tag all files containing PII.

Answer: A,B,C,F

NEW QUESTION # 77
An AI platform is suffering from poor performance during distributed training jobs. The training data resides on a single, large NFS volume. Monitoring shows that while the overall network throughput to the storage system is high, individual GPU nodes experience significant I/O wait times, and the single ONTAP volume is becoming a performance bottleneck. The goal is to re- architect the storage layout to maximize read parallelism and throughput for the training cluster.
Which two actions should the architect take to address this performance bottleneck? (Choose 2.)

A. Enable QoS maximums on the training volume to limit its IOPS.
B. Increase the number of network ports connected to the storage controller.
C. Use NetApp FlexCache to create a local cache of the training data on each compute node.
D. Implement a NetApp FlexGroup volume to spread the dataset across multiple constituent volumes and aggregates.
E. Replace the NFS protocol with iSCSI for all training data access.

Answer: C,D

NEW QUESTION # 78
A financial services company is required by regulators to be able to trace any version of their deployed fraud detection model back to the exact dataset and source code commit used to train it.
The current MLOps workflow is as follows:
Code_Repository: Git (commit hash: a1b2c3d4)
Dataset_Location: /vol/prod_data/fraud_dataset_v3
Storage_System: NetApp ONTAP 9
Model_Output: /vol/models/fraud_model_v3.2
Which NetApp technology should be used to create an immutable, point-in-time, and space- efficient copy of the dataset that can be linked to the specific code commit and model version?

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

Answer: D

NEW QUESTION # 79
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