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Network Appliance NS0-901 Exam Syllabus Topics:

Section	Weight	Objectives
AI Overview	15%	- 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 Industry Applications 1. Digital twins 2. Healthcare applications 3. Agents - AI Convergence with HPC and Analytics 1. Leveraging shared infrastructure for AI, HPC, and analytics - Training vs. Inferencing vs. Predictions 1. Distinguish between training and inference workloads - AI Deployment Models 1. Edge 2. Cloud 3. On-premises 4. Benefits and risks of each model
AI Hardware Architectures	18%	- Networking and Storage 1. Network protocols for AI workloads 2. Storage architectures for AI - NetApp Architectures 1. BasePod 2. OVX architectures 3. SuperPOD - Infrastructure Topologies 1. Data aggregation and compute topologies
AI Lifecycle	27%	- 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. Retrieval Augmented Generation (RAG) 2. Hallucinations 3. Fine-tuning - Data Preparation 1. NetApp BlueXP Classification 2. Data aggregation and cleansing 3. XCP and CopySync - Model Development 1. Fine-tuning workflows 2. Inferencing 3. Model building
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 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

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Network Appliance NetApp Certified AI Expert Exam Sample Questions (Q16-Q21):

NEW QUESTION # 16
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 automatically provision a 50 Gi volume on its default backend and bind it to the PVC.
B. Trident will create a 1 Gi volume, as this is the default size for all PVCs.
C. The PVC will fail because a 'storageClassName' is not explicitly defined.
D. The PVC will remain in a "Pending" state until a PersistentVolume is manually created.

Answer: A

NEW QUESTION # 17
An architect is designing a global infrastructure for a company that develops AI for autonomous vehicles.
The design must accommodate three distinct locations and functions:
1. Edge (Test Tracks): Fleets of test cars generate 100s of TBs of sensor data per day. This data must be ingested locally with high performance.
2. Core (Primary Data Center): The raw data from all edge sites must be aggregated here. This location houses the primary data lake and the main GPU cluster for large-scale model training.
3. Cloud (Public Cloud Provider): Data scientists want to use cloud-native tools for experimental data processing and model development. They also need a cost-effective location for long-term archiving of raw data.
Which combination of deployment locations and NetApp technologies creates the most logical and efficient end-to-end solution?

A. Deploy a single, global NetApp StorageGRID across all three locations to act as a unified data plane.
B. Use Cloud Volumes ONTAP at the edge, NetApp StorageGRID at the core, and on-premises ONTAP for cloud archive.
C. Use NetApp ONTAP systems at the edge and core, and Cloud Volumes ONTAP in the public cloud.
Use SnapMirror to replicate data from edge to core, and FabricPool to tier data from the core to the cloud.
D. Use NetApp E-Series at the edge, NetApp ASA at the core, and NetApp StorageGRID in the cloud.
Use SnapMirror to move data between all three tiers.

Answer: C

NEW QUESTION # 18
Which AI technology is used to generate new, never-before-seen content such as images or text?

A. Predictive AI
B. Supervised AI
C. Generative AI
D. Reinforcement AI

Answer: C

NEW QUESTION # 19
A data science team works primarily at a central data center but needs to run a short-term, burst- compute training job in the public cloud to take advantage of specialized GPUs that are not available on- premises. They need to efficiently and securely move a 20 TB dataset from their on- premises ONTAP cluster to a Cloud Volumes ONTAP instance for the duration of the job.
The data flow requirement is as follows:
Source: On-premises ONTAP cluster
Destination: Cloud Volumes ONTAP in AWS
Requirement: Efficient, secure, block-level data transfer.
Which NetApp technology is the most appropriate tool for this task?

A. NetApp FabricPool
B. NetApp XCP
C. NetApp FlexCache
D. NetApp SnapMirror

Answer: D

NEW QUESTION # 20
An MLOps engineer is troubleshooting a failed Kubeflow pipeline step. The step was designed to create a clone of a dataset for a training job using the NetApp DataOps Toolkit. The pod logs for the failed pipeline step show the following:
Traceback (most recent call last):
File "create_clone.py", line 15, in <module>
clone_pvc(source_pvc_name="training-data-v2", new_pvc_name="train-job-34a-data") NameError: name 'clone_pvc' is not defined The engineer reviews the Python script for the pipeline step:
# create_clone.py
import os
from netapp_dataops.k8s import create_pvc
# Other code
print("Cloning source dataset for training run...")
clone_pvc(
source_pvc_name="training-data-v2",
new_pvc_name="train-job-34a-data"
)
print("Clone created successfully.")
What is the cause of the error?

A. The NetApp DataOps Toolkit is not installed in the container image used for this pipeline step.
B. The Python script is attempting to use the 'clone_pvc' function, but it was not imported from the
'netapp_dataops.k8s' library.
C. The source PVC 'training-data-v2' does not exist.
D. The Kubernetes cluster is not running NetApp Trident.

Answer: B

NEW QUESTION # 21
......

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