NS0-901日本語版テキスト内容、NS0-901認定資格試験

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Network Appliance NS0-901 Exam Syllabus Topics:

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

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Network Appliance NetApp Certified AI Expert Exam 認定 NS0-901 試験問題 (Q96-Q101):

質問 # 96
An AI architect is planning the resource allocation for a new project. The primary task is to process millions of unlabeled customer reviews to identify naturally occurring groups or themes without any prior guidance.
The project requirements are summarized below:
Task: Discover hidden patterns in text data
Input_Data: 10 million unlabeled text reviews
Output: Clustered groups of related reviews
Supervision: None
Which type of machine learning algorithm is required for this task?

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

正解：D

質問 # 97
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 a single Trident backend that points to both the ASA and StorageGRID systems simultaneously.
B. Configure the 'gold-tier' StorageClass to reference the ASA backend.
C. Create two distinct StorageClasses, 'gold-tier' and 'bronze-tier'.
D. Create a single StorageClass named 'multi-tier-storage' that references both backends.
E. Create two separate Trident backend configurations, one for the ASA ('backend-asa.yaml') and one for StorageGRID ('backend-sgrid.yaml').
F. Configure the 'bronze-tier' StorageClass to reference the StorageGRID backend.

正解：B、C、E、F

質問 # 98
A security administrator is reviewing the configuration of a production ONTAP cluster after a compliance audit. The audit requires that all volumes containing financial data must be protected against ransomware. The administrator runs a command to check the status of Autonomous Ransomware Protection (ARP) on a critical volume.
The command and its output are as follows:
cluster-1::> security ransomware anti-ransomware show -vserver svm_finance -volume finance_q1_data Vserver: svm_finance Volume: finance_q1_data Autonomous Ransomware Protection Status: disabled Learning Mode: normal Dry Run Mode: false Action on Detection: notify Based on this output, what is the primary security risk for the 'finance_q1_data' volume?

A. The learning mode is set to "normal," which may not detect zero-day attacks.
B. The volume is vulnerable to ransomware because Autonomous Ransomware Protection is not enabled.
C. The action on detection is set only to "notify," which will not block the attack.
D. The volume is not part of any Snapshot schedule.

正解：B

質問 # 99
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 E-Series 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 a single, large NetApp ASA system for both the object data lake and the vector database.
D. Use NetApp StorageGRID for the data lake and a NetApp ASA system for the vector database.

正解：D

質問 # 100
An AI platform team is investigating poor I/O performance for a specific workload that involves processing hundreds of thousands of small metadata files. The application is running on a Kubernetes cluster with storage provided by a NetApp ONTAP system over NFS. Performance metrics show acceptable network throughput but very high latency for metadata operations (e.g., open, stat, close).
The current storage configuration is as follows:
Storage_System: NetApp AFF A-Series
Protocol: NFSv4.1
Workload_Profile: Metadata-intensive, many small file lookups
Observed_Issue: High latency on metadata operations, slow job completion Which storage architecture would be better suited to handle this specific metadata-intensive workload?

A. A NetApp E-Series system running a parallel file system like BeeGFS.
B. A NetApp Cloud Volumes ONTAP instance in a different region.
C. A NetApp StorageGRID object storage system.
D. A NetApp SnapLock-enabled volume for data protection.

正解：A

質問 # 101
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NS0-901学習教材の練習試験や模擬試験はみんなにとって重要です。今のリビジョン条件はNS0-901試験に参加する良い機会です。したがって、レビュープランを調整するために、NS0-901の各練習問題を要約することが不可欠です。今、私たちはNS0-901実際試験を模擬するためにオンラインテストエンジンとWindowsソフトウェアを追加しました。

NS0-901認定資格試験: https://www.jpexam.com/NS0-901_exam.html

P.S.JpexamがGoogle Driveで共有している無料の2026 Network Appliance NS0-901ダンプ：https://drive.google.com/open?id=1aOCCWoWzpKXySrrHN-HCs_RILoxON-pt