NS0-901시험정보 & NS0-901인증덤프샘플다운로드

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Network Appliance NS0-901 Exam Syllabus Topics:

Section	Weight	Objectives
AI Lifecycle	27%	- Predictive vs generative AI - Data preparation and management for AI - AI governance, ethics, and compliance - AI lifecycle stages: design, training, deployment, monitoring - Model training, inference, and optimization
AI Overview	15%	- Convergence of AI, high-performance computing, and analytics - Algorithm types: supervised, unsupervised, reinforcement learning - AI industry use cases and applications - AI deployment models: on-premises, cloud, edge - AI, machine learning, and deep learning concepts
NetApp AI Solutions and Architecture	25%	- Data management and data pipeline design - ONTAP integration with AI frameworks - Storage architectures for AI workloads - NetApp AI-ready infrastructure components - Scalability and performance optimization for AI
Cloud and Hybrid Cloud AI Deployment	18%	- NetApp cloud data services for AI - Data mobility and consistency across environments - Hybrid and multi-cloud AI architectures - Cloud-native AI solutions and integration
Security, Reliability, and Operations	15%	- Data security and access control for AI - Cost management and efficiency - High availability and data protection - Monitoring, logging, and troubleshooting AI environments

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최신 NetApp Certified AI Expert NS0-901 무료샘플문제 (Q57-Q62):

질문 # 57
The company's compliance officer mandates that all raw simulation data in the StorageGRID data lake must be protected from modification or deletion for a period of seven years. Which two StorageGRID features should be implemented to meet this strict, long-term data immutability requirement? (Choose 2.)

A. Configure an Information Lifecycle Management (ILM) rule that applies to the bucket and sets a retention period of seven years.
B. Encrypt all objects in the bucket using server-side encryption (SSE).
C. Enable Autonomous Ransomware Protection (ARP) on the StorageGRID system.
D. Create a new S3 bucket with Object Lock enabled in Compliance mode.
E. Use NetApp SnapMirror to replicate the data to a second StorageGRID system.

정답：A,D

질문 # 58
Which of the following best describes the difference between data lakes, data warehouses, and lakehouses?

A. Data lakes store structured data, data warehouses store unstructured data, and lakehouses store only real-time data.
B. Data lakes store data in cloud storage, data warehouses store it in traditional databases, and lakehouses store it in external drives.
C. Data lakes store metadata, data warehouses store transaction data, and lakehouses store archival data.
D. Data lakes store raw, unstructured data, data warehouses store structured data, and lakehouses combine the features of both.

정답：D

질문 # 59
The firm decides to expand the "Advisor Assistant" project to a new team in a different department. This team needs its own isolated environment. An MLOps engineer attempts to submit a new GPU- intensive job for the new team, but it remains pending. The engineer checks the Run:AI scheduler logs and finds the following entry:
time="2025-07-11T16:45:00Z" level=info msg="Job ds-new-team-job1 cannot be scheduled.
Project 'new-team-project' has exceeded its GPU quota. Quota: 0, Requested: 1, Used: 0." What is the root cause of the scheduling failure?

A. The Kubernetes cluster has no available GPUs.
B. The Run:AI scheduler is offline and cannot process new jobs.
C. A Run:AI project quota has been configured for the new team, but it has been set to zero, effectively blocking them from using any GPU resources.
D. The job is requesting a specific type of GPU that is not available in the cluster.

정답：C

A. Prioritize fine-tuning to embed the company's brand voice and persona into the model.
B. Use RAG exclusively, as prompt engineering alone can fully replicate a specific brand voice and persona.
C. Propose a hybrid approach where a base model is first lightly fine-tuned for persona, then used within a RAG system for factual grounding.
D. Prioritize a RAG architecture to meet the critical requirements for factual accuracy and data traceability.
E. Recommend training a new LLM from scratch to ensure both brand voice and factual accuracy are built-in.

정답：C,D

질문 # 61
The firm's data science team needs to run a high-priority, interactive model analysis job that requires immediate access to two GPUs. However, all GPUs in the cluster are currently allocated to long-running, lower-priority batch training jobs.
The MLOps platform, Run:AI, shows the following queue status:
JOB_ID | PROJECT | STATUS | PRIORITY | GPU_ALLOCATED
||--|-|
batch_job_1 | team_a | Running | Low | 2
batch_job_2 | team_a | Running | Low | 2
batch_job_3 | team_b | Running | Low | 4
interactive_1| team_c | Pending | High | 2 (requested)
How does the Run:AI platform address this resource contention to allow the high-priority job to run?

A. It automatically terminates all low-priority jobs to free up the entire cluster.
B. It automatically pauses one of the low-priority jobs, saves its state, and allocates its GPUs to the high- priority job, placing the paused job back in the queue.
C. It keeps the high-priority job in a pending state until the low-priority jobs complete naturally.
D. It sends an email notification to the administrator to manually reallocate the GPUs.

정답：B

질문 # 62
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