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Network Appliance NS0-901 Exam Syllabus Topics:

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

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Network Appliance NetApp Certified AI Expert Exam Sample Questions (Q78-Q83):

NEW QUESTION # 78
A research institute is designing an infrastructure to support its entire AI drug discovery pipeline.
The pipeline has two distinct workload requirements:
1. Training: A team of data scientists needs to train several large transformer models simultaneously using a 500 TB dataset of genomic sequences. This process requires maximum data throughput to keep the GPUs saturated.
2. Inference: Once trained, the models are deployed to an internal web portal where researchers submit individual protein sequences for analysis. These queries must return results with the lowest possible latency.
Which infrastructure design best satisfies both requirements? (Choose 2.)

A. Use NetApp StorageGRID as the primary storage for both the training and low-latency inference workloads.
B. Configure QoS minimums on the training volumes to ensure they do not impact inference performance.
C. Implement NetApp FlexCache on smaller nodes at the network edge to serve the inference requests.
D. Use a single, large Cloud Volumes ONTAP instance in a public cloud to handle both workloads to simplify management.
E. Deploy a large NetApp ASA cluster with GPUDirect Storage enabled for the training environment.

Answer: C,E

NEW QUESTION # 79
A data scientist needs to launch a Jupyter notebook as a pod in a Kubernetes cluster. The pod requires a 50 Gi persistent volume for storing datasets and notebooks. The cluster administrator has configured a default Trident StorageClass for general-purpose use. The data scientist has the following PersistentVolumeClaim (PVC) manifest:
apiVersion: v1
kind: PersistentVolumeClaim
metadata:
name: jupyter-pvc
spec:
accessModes:
- ReadWriteOnce
resources:
requests:
storage: 50Gi
When this PVC is applied to the cluster, what will be the result?

A. Trident will create a 1 Gi volume, as this is the default size for all PVCs.
B. The PVC will fail because a 'storageClassName' is not explicitly defined.
C. Trident will automatically provision a 50 Gi volume on its default backend and bind it to the PVC.
D. The PVC will remain in a "Pending" state until a PersistentVolume is manually created.

Answer: C

NEW QUESTION # 80
The data science team in Azure reports that training jobs are taking longer than expected. An analysis of the Cloud Volumes ONTAP instance in Azure shows that the instance type is undersized for the I/O demands of the training workload. The architect needs to change the Azure VM instance type for the Cloud Volumes ONTAP system to a more powerful one.
The current configuration is:
Cloud_Provider: Azure
ONTAP_System: Cloud Volumes ONTAP (Single Node)
Current_Instance_Type: Standard_DS3_v2
Target_Instance_Type: Standard_E8s_v4
What is the most direct method to perform this operation using NetApp's management tools?

A. From the BlueXP Canvas, select the Cloud Volumes ONTAP working environment and use the
"Change Instance Type" action in the Features panel.
B. Terminate the existing Cloud Volumes ONTAP instance and deploy a new one with the target instance type, then restore data from backup.
C. SSH to the Cloud Volumes ONTAP instance and run an ONTAP command to modify the underlying VM.
D. Use the Azure portal to manually change the VM instance type.

Answer: A

NEW QUESTION # 81
An architect is designing a data pipeline for a predictive AI model that will forecast retail sales.
The pipeline must be robust, version-controlled, and efficient.
The proposed data flow is as follows:
1. Ingest: Raw sales data is copied daily from multiple point-of-sale (POS) systems to a central staging area on an on-premises ONTAP cluster.
2. Prepare: The raw data is messy. A data engineering team needs a clean, isolated, and writable copy of the latest daily data to perform cleansing and feature engineering tasks without impacting the original raw data.
3. Train: Once prepared, the cleansed dataset is used to retrain the predictive model on a GPU cluster.
This step must be repeatable with the exact same dataset for compliance.
4. Deploy: The newly trained model is pushed to production inference servers.
Which combination of NetApp technologies best supports this entire predictive AI lifecycle?
(Select all
that apply.)

A. Use BlueXP backup and recovery to perform the initial data ingest from the POS systems.
B. Use a RAG architecture for the sales forecasting model.
C. Use NetApp XCP to efficiently aggregate the raw sales data from POS systems into the central staging area.
D. Use NetApp Snapshots on the prepared dataset volume just before training to create an immutable, point-in-time version for compliance and reproducibility.
E. Use NetApp StorageGRID as the primary storage for the high-performance training stage.
F. Use NetApp FlexClone to create an instantaneous, space-efficient, writable copy of the daily raw data for the data preparation stage.

Answer: C,D,F

NEW QUESTION # 82
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 FlexClone
B. NetApp FabricPool
C. NetApp XCP
D. NetApp SnapMirror

Answer: A

NEW QUESTION # 83
......

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