NS0-901 Exam Sample, NS0-901 Vce Files

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Network Appliance NS0-901 Exam Syllabus Topics:

Section	Objectives
Topic 1: AI Infrastructure and NetApp Solutions	- AI-ready data infrastructure 1. Data pipeline optimization for AI/ML workloads 2. High-performance storage for AI workloads - Converged workloads 1. AI, HPC, and analytics convergence 2. Shared infrastructure design considerations
Topic 2: Industry Use Cases	- AI applications across industries 1. Healthcare AI 2. Autonomous systems and agents 3. Digital twins
Topic 3: AI and Machine Learning Fundamentals	- Algorithm types 1. Supervised learning 2. Reinforcement learning 3. Unsupervised learning - AI, ML, DL concepts 1. Training, inference, and prediction workflows 2. Differences between AI, machine learning, and deep learning
Topic 4: AI Lifecycle and Deployment	- Operational challenges 1. Scalability and performance optimization 2. Data governance and management - End-to-end AI lifecycle 1. Model training and validation 2. Model deployment and monitoring 3. Data ingestion and preparation

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NS0-901 Vce Files & NS0-901 Pdf Format

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Network Appliance NetApp Certified AI Expert Exam Sample Questions (Q105-Q110):

NEW QUESTION # 105
A company is running its AI training workloads on a NetApp AFF A-Series system. To manage costs, they want to automatically move inactive training datasets and older model checkpoints from the high- performance all-flash tier to a lower-cost object storage tier, such as an on- premises StorageGRID or a public cloud bucket. The process must be transparent to the data scientists and not require changes to their scripts or file paths.
Which two NetApp technologies should be combined to achieve this goal? (Choose 2.)

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

Answer: A,E

NEW QUESTION # 106
An organization's AI platform team needs to provide two distinct tiers of storage for their data scientists on a single Kubernetes cluster:
1. 'gold-tier': Extremely low-latency storage for active model training, using an all-flash NetApp ASA system.
2. 'bronze-tier': Cost-effective, high-capacity storage for data staging and archiving, using a NetApp StorageGRID system.
How should the platform team configure NetApp Trident to meet these requirements? (Select all that apply.)

A. Create two separate Trident backend configurations, one for the ASA ('backend-asa.yaml') and one for StorageGRID ('backend-sgrid.yaml').
B. Create a single Trident backend that points to both the ASA and StorageGRID systems simultaneously.
C. Configure the 'bronze-tier' StorageClass to reference the StorageGRID backend.
D. Create two distinct StorageClasses, 'gold-tier' and 'bronze-tier'.
E. Create a single StorageClass named 'multi-tier-storage' that references both backends.
F. Configure the 'gold-tier' StorageClass to reference the ASA backend.

Answer: A,C,D,F

NEW QUESTION # 107
A media company is building a new generative AI service. The project has two main components:
1. Data Lake & Fine-Tuning: A 300 TB repository of unstructured data (videos, images, text) stored as objects will be used to fine-tune a foundational model. This process requires a scalable, cost-effective storage solution that can integrate with cloud-native data processing tools like Apache Spark.
2. Inference & RAG: The fine-tuned model will be used in a customer-facing application that leverages Retrieval-Augmented Generation (RAG). To ensure low-latency responses, the RAG component requires extremely fast lookups from a 10 TB vector database.
The company needs a solution that optimizes both cost and performance for this entire lifecycle.
Which combination of NetApp technologies provides the most appropriate solution for this scenario?

A. Use NetApp StorageGRID for the data lake and a NetApp ASA system for the vector database.
B. Use NetApp Cloud Volumes ONTAP for the data lake and NetApp StorageGRID for the vector database.
C. Use NetApp E-Series for the data lake and a NetApp ASA system for the vector database.
D. Use a single, large NetApp ASA system for both the object data lake and the vector database.

Answer: A

NEW QUESTION # 108
Due to the success of the "Advisor Assistant," the number of concurrent users is expected to double in the next quarter. The existing Kubernetes cluster is running at 80% of its GPU capacity during peak hours. The architect must propose a plan to scale the compute infrastructure to handle the increased load.
Which two strategies represent the most effective and scalable solutions? (Choose 2.)

A. Vertically scale the existing nodes by adding more RAM and CPU cores to each one.
B. Configure the Kubernetes Cluster Autoscaler to automatically add new nodes to the cluster from a predefined node pool when GPU demand exceeds current capacity.
C. Manually deploy all new application replicas to a single, new, very large "super-node".
D. Horizontally scale the cluster by adding new GPU-equipped nodes.
E. Replace all existing GPUs with the next-generation model to increase the performance of each node.

Answer: B,D

NEW QUESTION # 109
A data scientist is using the NetApp DataOps Toolkit for Python to automate the creation of a new, writable volume for an experiment. The script is intended to clone an existing dataset volume. When the script is executed, it fails with an error.
The relevant portion of the Python script is:
from netapp_dataops.k8s import clone_pvc
clone_pvc(
source_pvc_name="dataset-v1-pvc",
new_pvc_name="experiment-clone-pvc",
namespace="ds-team-1"
)
The script produces the following error in the terminal:
'Error: Failed to clone PVC. Source PVC 'dataset-v1-pvc' not found in namespace 'ds-team-1'.' What is the most likely cause of this error?

A. The Kubernetes cluster does not have NetApp Trident installed.
B. The Python script is missing the necessary import statement for the toolkit.
C. The source PersistentVolumeClaim (PVC) named 'dataset-v1-pvc' does not exist or is in a different namespace.
D. The NetApp DataOps Toolkit does not support cloning volumes.

Answer: C

NEW QUESTION # 110
......

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