NS0-901真実試験 & NS0-901日本語問題集

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Network Appliance NS0-901 Exam Syllabus Topics:

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

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Network Appliance NetApp Certified AI Expert Exam 認定 NS0-901 試験問題 (Q79-Q84):

質問 # 79
An automotive company runs crash simulations on a dedicated High-Performance Computing (HPC) cluster and trains computer vision models on a separate AI cluster. Data scientists are complaining about the long delays required to move terabytes of simulation output data from the HPC storage to the AI cluster's storage before they can begin training.
The current data flow is as follows:
HPC Cluster -> --Manual Copy (NFS)--> -> AI Cluster
An architect has been asked to redesign the infrastructure to eliminate this data movement bottleneck.
Which architectural change would be most effective?

A. Install faster CPUs in the AI cluster's storage controllers.
B. Upgrade the network connection between the two storage systems to 200GbE.
C. Implement a converged data infrastructure where both the HPC and AI clusters access a single, high- performance data lake built on NetApp storage.
D. Use NetApp XCP to perform the data copy, as it is faster than a standard NFS copy.

正解：C

質問 # 80
The data pipeline that updates the vector database from the StorageGRID data lake fails. An MLOps engineer uses the BlueXP API to check the status of the copy and sync job responsible for moving new documents to a staging area for processing.
The API returns the following JSON response:
{
"id": "rel_5f8g7h2k",
"volumeName": "product_docs_source",
"svmName": "svm_datalake",
"relationshipStatus": "FAILED",
"s3Location": "s3://vector-staging-bucket/latest/",
"progress": 87,
"failureMessage": "Sync failed.
Target credentials invalid. Check permissions for data broker 'db-gcp-1' on target bucket 'vector- staging-bucket'."
}
Based on this output, what is the most direct cause of the pipeline failure?

A. The BlueXP data broker service account or role lacks the necessary IAM permissions to write to the target S3 bucket.
B. The source StorageGRID system is offline.
C. The network connection between the data broker and the StorageGRID system is blocked by a firewall.
D. The vector database embedding process has crashed.

正解：A

質問 # 81
Which of the following describes the impact of generative AI in content creation?

A. Generative AI only processes pre-existing content.
B. Generative AI predicts customer preferences without creating new content.
C. Generative AI analyzes data without generating content.
D. Generative AI can create new content such as text, images, and videos.

正解：D

質問 # 82
An AI infrastructure architect is tasked with designing a solution to address two critical challenges in a large, multi-petabyte AI environment:
1. Cost: A significant portion of the data on the high-performance all-flash storage is inactive but must remain online. The cost of storing this cold data on the performance tier is prohibitive.
2. Traceability: Data scientists need a simple, space-efficient way to version their datasets at key points in their workflow to ensure reproducibility.
The environment consists of NetApp AFF A-Series and NetApp StorageGRID systems.
Which combination of NetApp technologies should the architect implement to solve both challenges simultaneously? (Select all that apply.)

A. Use BlueXP backup and recovery to create backups on StorageGRID, then delete the original volumes from the AFF systems.
B. Implement NetApp FlexClone to create full, writable copies of datasets for each experiment.
C. Use NetApp XCP to periodically move cold data from the AFF systems to StorageGRID.
D. Train data scientists to use NetApp Snapshots to create point-in-time, read-only versions of their data volumes.
E. Use NetApp SnapMirror to replicate volumes from the AFF systems to StorageGRID for archival.
F. Implement NetApp FabricPool to automatically tier cold data blocks from the AFF systems to StorageGRID.

正解：D、F

質問 # 83
An architect is explaining different data storage paradigms to a new data engineering team. The team needs to understand where to store different types of data for their AI projects. Which statement accurately describes a key difference between a data lake and a data warehouse?

A. A data lake is designed to store vast quantities of raw, multi-format data (structured, semi- structured, and unstructured), while a data warehouse is typically used for storing structured and filtered data that has already been processed for a specific purpose.
B. A data lake is only for on-premises deployments, while a data warehouse is only for cloud deployments.
C. A data lake stores structured, processed data for fast querying, while a data warehouse stores raw, unstructured data in its native format.
D. A data lake uses a schema-on-write approach, while a data warehouse uses a schema-on-read approach.

正解：A

質問 # 84
......

自宅にいても外にいても、NS0-901テストトレントを勉強できます。 NS0-901学習ツールの指導の下では、試験の準備に20〜30時間しかかからないため、他にやることがあるので、時間を心配する必要はありません。 NS0-901試験資料を使用して、独自に学習できます。毎日多くの時間を費やす必要はなく、試験に合格し、最終的には証明書を取得します。 NS0-901認定は、就職面接の重要なタグになる可能性があり、他の人よりも競争上の優位性があります。

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