Network Appliance NS0-901 Exam | Valid NS0-901 Exam Labs - Assist you Clear NS0-901: NetApp Certified AI Expert Exam Exam

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Network Appliance NS0-901 Exam Syllabus Topics:

Section	Objectives
AI Infrastructure and NetApp Solutions	- Converged workloads 1. Shared infrastructure design considerations 2. AI, HPC, and analytics convergence - AI-ready data infrastructure 1. Data pipeline optimization for AI/ML workloads 2. High-performance storage for AI workloads
Industry Use Cases	- AI applications across industries 1. Healthcare AI 2. Digital twins 3. Autonomous systems and agents
AI and Machine Learning Fundamentals	- Algorithm types 1. Unsupervised learning 2. Reinforcement learning 3. Supervised learning - AI, ML, DL concepts 1. Differences between AI, machine learning, and deep learning 2. Training, inference, and prediction workflows
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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Network Appliance NetApp Certified AI Expert Exam Sample Questions (Q35-Q40):

NEW QUESTION # 35
A robotics company is developing a control system for an autonomous warehouse drone. The drone must learn to navigate complex environments to pick up packages. The development team has created a physics-based simulation where the drone can attempt the task millions of times.
The drone receives a positive reward for successfully retrieving a package and a negative penalty for collisions. Which type of machine learning algorithm is being used in this scenario?

A. Generative learning
B. Unsupervised learning
C. Supervised learning
D. Reinforcement learning

Answer: D

NEW QUESTION # 36
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 FabricPool
B. NetApp XCP
C. NetApp SnapMirror
D. NetApp FlexCache
E. NetApp StorageGRID

Answer: A,E

NEW QUESTION # 37
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. The PVC will remain in a "Pending" state until a PersistentVolume is manually created.
C. Trident will create a 1 Gi volume, as this is the default size for all PVCs.
D. The PVC will fail because a 'storageClassName' is not explicitly defined.

Answer: A

NEW QUESTION # 38
An AI architect is designing a solution for a legal firm. The primary goal is to allow lawyers to ask natural language questions about case law stored in a private, 50 TB document repository.
The key project constraints are as follows:
Project_Goal: Answer questions using proprietary, real-time legal documents.
Constraint_1: Must not alter the foundational LLM's weights due to compliance.
Constraint_2: Case law database is updated daily with new rulings.
Constraint_3: All generated answers must be traceable to a source document.
Which technology should the architect choose as the core of this solution?

A. A Retrieval-Augmented Generation (RAG) architecture.
B. A predictive AI model to classify legal documents.
C. A fine-tuning pipeline to update the LLM daily.
D. A new LLM trained from scratch on the legal documents.

Answer: A

NEW QUESTION # 39
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. Manually deploy all new application replicas to a single, new, very large "super-node".
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. Horizontally scale the cluster by adding new GPU-equipped nodes.
D. Vertically scale the existing nodes by adding more RAM and CPU cores to each one.
E. Replace all existing GPUs with the next-generation model to increase the performance of each node.

Answer: B,C

NEW QUESTION # 40
......

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