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Network Appliance NS0-901 Exam Syllabus Topics:

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

Network Appliance NetApp Certified AI Expert Sample Questions:

1. Which of the following techniques are used to maximize GPU utilization in AI workloads? (Choose two)

A) Balancing compute and storage workloads efficiently
B) Scheduling GPU-intensive tasks during off-peak hours
C) Using larger batch sizes
D) Utilizing CPU-based workloads for all tasks

2. An automotive company runs crash simulations on a dedicated High-Performance Computing (HPC) cluster and trains computer vision models on a separate AI cluster. Data scientists are complaining about the long delays required to move terabytes of simulation output data from the HPC storage to the AI cluster's storage before they can begin training.
The current data flow is as follows:
HPC Cluster -> --Manual Copy (NFS)--> -> AI Cluster
An architect has been asked to redesign the infrastructure to eliminate this data movement bottleneck.
Which architectural change would be most effective?

A) Use NetApp XCP to perform the data copy, as it is faster than a standard NFS copy.
B) Install faster CPUs in the AI cluster's storage controllers.
C) Upgrade the network connection between the two storage systems to 200GbE.
D) Implement a converged data infrastructure where both the HPC and AI clusters access a single, high- performance data lake built on NetApp storage.

3. 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 FabricPool
B) NetApp FlexClone
C) NetApp Snapshots
D) NetApp SnapMirror

4. Which storage protocols are commonly used for handling large-scale AI data? (Choose two)

A) Object-based storage
B) Parallel file systems
C) File-based systems
D) POSIX-based file systems

5. 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

Solutions:

Question # 1
Answer: A,B

Question # 2
Answer: D

Question # 3
Answer: C

Question # 4
Answer: A,B

Question # 5
Answer: B,C