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Network Appliance NS0-901 Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Security, Reliability, and Operations	15%	- Data security and access control for AI - Monitoring, logging, and troubleshooting AI environments - High availability and data protection - Cost management and efficiency
Topic 2: AI Lifecycle	27%	- Model training, inference, and optimization - AI governance, ethics, and compliance - Data preparation and management for AI - AI lifecycle stages: design, training, deployment, monitoring - Predictive vs generative AI
Topic 3: NetApp AI Solutions and Architecture	25%	- Storage architectures for AI workloads - ONTAP integration with AI frameworks - NetApp AI-ready infrastructure components - Data management and data pipeline design - Scalability and performance optimization for AI
Topic 4: AI Overview	15%	- AI industry use cases and applications - AI deployment models: on-premises, cloud, edge - Convergence of AI, high-performance computing, and analytics - AI, machine learning, and deep learning concepts - Algorithm types: supervised, unsupervised, reinforcement learning
Topic 5: Cloud and Hybrid Cloud AI Deployment	18%	- Cloud-native AI solutions and integration - Hybrid and multi-cloud AI architectures - Data mobility and consistency across environments - NetApp cloud data services for AI

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Network Appliance NetApp Certified AI Expert Exam NS0-901 Prüfungsfragen mit Lösungen (Q86-Q91):

86. Frage
An organization is planning to deploy a large AI infrastructure but wants to avoid a large, upfront capital expenditure. They prefer an operational expenditure (OpEx) model where they pay for storage and compute resources as they are consumed. They also need the flexibility to scale resources up or down based on project demands.
Which NetApp consumption model is specifically designed to meet these financial and operational requirements?

A. NetApp Keystone, which provides a subscription-based, pay-as-you-go service for on-premises and cloud storage.
B. A standard capital purchase of AFF and ASA systems.
C. A perpetual licensing model for all ONTAP software features.
D. NetApp Cloud Volumes ONTAP PAYGO licensing only.

Antwort: A

87. Frage
A training job on one of the NVIDIA DGX servers is running slowly. A performance engineer runs the 'dstat' command on the server and captures the following output during the job execution.
-total-cpu-usage- -dsk/total- -net/total- paging-- system-- usr sys idl wai hiq siq| read writ| recv send| in out | int csw 15 5 70 10 0 0| 1.2G 15.0M| 1.2G 12.0M| 0 0 | 15k 35k
14 6 69 11 0 0| 1.2G 14.3M| 1.2G 11.8M| 0 0 | 14k 33k
16 5 68 11 0 0| 1.2G 16.1M| 1.2G 13.1M| 0 0 | 16k 36k
The server is connected to the NetApp ASA via a 100GbE (12.5 GB/s) network.
What is the most likely performance bottleneck based on this data?

A. The server's network interface is saturated.
B. The server's CPU is overloaded.
C. The storage system (ASA) is unable to provide sufficient read throughput.
D. The application is experiencing excessive memory paging.

Antwort: A

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

Antwort: A,B,D,E

89. Frage
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 Run:AI scheduler is offline and cannot process new jobs.
B. The job is requesting a specific type of GPU that is not available in the cluster.
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 Kubernetes cluster has no available GPUs.

Antwort: C

90. Frage
A financial services company has deployed a real-time fraud detection model at the edge. The model is designed for low-latency inference. However, monitoring reports indicate that the infrastructure costs are excessively high, and GPU utilization is consistently low. The architect reviews the deployment configuration.
Instance_Type: NVIDIA DGX A100 (8 GPUs)
Storage_Tier: High-Performance All-Flash (NetApp ASA)
Network: 100GbE RoCE
GPU_Utilization_Avg: 5%
Monthly_Cost: $15,000
Workload_Profile: Low-volume, sporadic, real-time predictions
What is the most likely cause of the high costs and low utilization?

A. The model was trained using supervised learning, which is inefficient for fraud detection.
B. The network latency is too high for an edge deployment.
C. The storage tier is too slow, causing the GPUs to wait for data.
D. The compute and storage infrastructure is sized for a large-scale training workload, not a lightweight inference workload.

Antwort: D

91. Frage
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