Network Appliance NS0-901인기시험자료, NS0-901최신인증시험덤프데모

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Network Appliance NS0-901 Exam Syllabus Topics:

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

>> Network Appliance NS0-901인기시험자료 <<

Network Appliance NS0-901최신 인증시험 덤프데모, NS0-901최신 인증시험 공부자료

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최신 NetApp Certified AI Expert NS0-901 무료샘플문제 (Q21-Q26):

질문 # 21
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. Deploy a large NetApp ASA cluster with GPUDirect Storage enabled for the training environment.
B. Configure QoS minimums on the training volumes to ensure they do not impact inference performance.
C. Use NetApp StorageGRID as the primary storage for both the training and low-latency inference workloads.
D. Use a single, large Cloud Volumes ONTAP instance in a public cloud to handle both workloads to simplify management.
E. Implement NetApp FlexCache on smaller nodes at the network edge to serve the inference requests.

정답：A,E

질문 # 22
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 SnapMirror
B. NetApp XCP
C. NetApp FabricPool
D. NetApp FlexCache

정답：A

질문 # 23
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 SnapMirror
C. NetApp XCP
D. NetApp FabricPool

정답：A

질문 # 24
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 source PersistentVolumeClaim (PVC) named 'dataset-v1-pvc' does not exist or is in a different namespace.
B. The Python script is missing the necessary import statement for the toolkit.
C. The NetApp DataOps Toolkit does not support cloning volumes.
D. The Kubernetes cluster does not have NetApp Trident installed.

정답：A

질문 # 25
An AI platform administrator is trying to deploy a new GenAI toolkit using the BlueXP Workload Factory. The deployment fails, and the administrator examines the API response from BlueXP.
{
"jobId": "we-deploy-genai-987zy",
"status": "FAILED",
"statusCode": 403,
"message":
"Forbidden: The service principal or user account used by the Connector does not have the required permissions on the target subscription to create a new resource group.
Required permission: 'Microsoft.Resources/subscriptions/resourcegroups/write'." } Based on this API response, what is the root cause of the deployment failure?

A. The Azure service principal associated with the BlueXP Connector lacks the necessary IAM role to create resource groups.
B. The GenAI toolkit requires a specific license that has not been added to the BlueXP digital wallet.
C. The selected ONTAP version does not support the GenAI toolkit.
D. The BlueXP Connector is offline and cannot communicate with the cloud provider.

정답：A

질문 # 26
......

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