NS0-901資格トレーニング、NS0-901関連日本語内容

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Network Appliance NS0-901 Exam Syllabus Topics:

Section	Objectives
Industry Use Cases	- AI applications across industries 1. Healthcare AI 2. Autonomous systems and agents 3. Digital twins
AI Lifecycle and Deployment	- Operational challenges 1. Scalability and performance optimization 2. Data governance and management - End-to-end AI lifecycle 1. Model deployment and monitoring 2. Model training and validation 3. Data ingestion and preparation
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
AI and Machine Learning Fundamentals	- Algorithm types 1. Unsupervised learning 2. Reinforcement learning 3. Supervised learning - AI, ML, DL concepts 1. Training, inference, and prediction workflows 2. Differences between AI, machine learning, and deep learning

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Network Appliance NetApp Certified AI Expert Exam 認定 NS0-901 試験問題 (Q98-Q103):

質問 # 98
An AI architect needs to design a complete, end-to-end data pipeline for a new generative AI application at a financial services firm. The application will allow internal analysts to query a massive, 500 TB archive of historical market data and reports to generate summaries. The firm has the following environment and requirements:
Data_Sources: A mix of on-premises ONTAP filers and StorageGRID S3 buckets.
Requirement_1: All queries must be answered using only the private data archive.
Requirement_2: All generated summaries must provide citations to the source reports.
Requirement_3: All data containing client PII must be identified and excluded from the LLM context.
Requirement_4: The solution must be cost-effective for the large, mostly-read data archive.
Which set of actions and technologies constitutes the most robust and compliant solution?
(Select all that apply.)

A. Use NetApp XCP to perform a one-time migration of all data from the ONTAP filers to the StorageGRID data lake.
B. During the RAG retrieval step, filter out any documents tagged as containing PII by BlueXP classification before sending them to the LLM.
C. Use SnapMirror to replicate the StorageGRID data lake to a high-performance NetApp ASA system for faster query performance.
D. Deploy BlueXP classification to scan the entire StorageGRID data lake to identify and tag all files containing PII.
E. Fine-tune a foundation model on the entire 500 TB dataset to ensure it understands the financial context.
F. Implement a Retrieval-Augmented Generation (RAG) architecture to meet the requirements for private data usage and source citation.

正解：A、B、D、F

質問 # 99
Given the company's goal of combining physics-based simulations with AI-driven analytics on a shared data foundation, which industry trend does this project best represent?

A. The exclusive use of public cloud resources for all computational tasks.
B. The separation of AI and HPC into dedicated, air-gapped environments.
C. The replacement of all physical testing with digital simulations.
D. The convergence of AI, High-Performance Computing (HPC), and analytics on a unified data infrastructure.

正解：D

質問 # 100
In the data pipeline, which NetApp technology is used to create an efficient, block-level, incremental replication of the anonymized datasets from the on-premises ASA to the Cloud Volumes ONTAP instance in Azure?

A. NetApp XCP
B. NetApp SnapMirror
C. NetApp FabricPool
D. NetApp BlueXP copy and sync

正解：B

質問 # 101
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. Use Cloud Volumes ONTAP at the edge, NetApp StorageGRID at the core, and on-premises ONTAP for cloud archive.
B. 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.
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. Deploy a single, global NetApp StorageGRID across all three locations to act as a unified data plane.

正解：C

質問 # 102
Which of the following applications use AI in the healthcare industry? (Choose two)

A. Managing hospital supply chains
B. Automating financial reporting
C. Diagnosing diseases
D. Predicting patient outcomes

正解：C、D

質問 # 103
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