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Network Appliance NS0-901 Exam Syllabus Topics:

Section	Objectives
AI Lifecycle and Deployment	- Operational challenges 1. Data governance and management 2. Scalability and performance optimization - End-to-end AI lifecycle 1. Model deployment and monitoring 2. Model training and validation 3. Data ingestion and preparation
AI and Machine Learning Fundamentals	- AI, ML, DL concepts 1. Training, inference, and prediction workflows 2. Differences between AI, machine learning, and deep learning - Algorithm types 1. Unsupervised learning 2. Supervised learning 3. Reinforcement learning
AI Infrastructure and NetApp Solutions	- Converged workloads 1. Shared infrastructure design considerations 2. AI, HPC, and analytics convergence - AI-ready data infrastructure 1. High-performance storage for AI workloads 2. Data pipeline optimization for AI/ML workloads
Industry Use Cases	- AI applications across industries 1. Digital twins 2. Autonomous systems and agents 3. Healthcare AI

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Network Appliance NetApp Certified AI Expert Exam Sample Questions (Q71-Q76):

NEW QUESTION # 71
A junior administrator is attempting to delete an old, unused Snapshot copy from a production volume to reclaim space but receives an error. The administrator is certain they are using the correct command and have the appropriate 'vsadmin' role.
The command and error are shown below:
cluster-1::> volume snapshot delete -vserver svm_prod -volume app_data -snapshot nightly.2025- 06-15_0015 Error: command failed: This operation is not permitted. Deletion of Snapshot copy "nightly.2025-
06-15_0015" on volume "app_data" in Vserver "svm_prod" requires approval.
Use the "security multi-admin-verify approval show" command to view pending approvals.
What is the most likely reason the administrator cannot delete the Snapshot?

A. The volume is protected by SnapLock, and the Snapshot has not reached its expiry date.
B. The 'vsadmin' role does not have permission to delete Snapshots.
C. The Snapshot is locked by a SnapMirror relationship.
D. Multi-Admin Verification (MAV) is enabled, and the destructive operation requires approval from another designated administrator.

Answer: D

NEW QUESTION # 72
A development team is building a generative AI application that must answer questions based on a constantly changing internal knowledge base of company documents. They want to provide the model with up-to-date information without altering its core weights and capabilities.
Which approach is most suitable for this requirement?

A. Predictive AI, which analyzes the documents to forecast future questions.
B. Fine-tuning, which retrains the model's last few layers on the new documents.
C. Unsupervised learning, which clusters the documents into topics for the model to browse.
D. Retrieval-Augmented Generation (RAG), which fetches relevant information from the documents at query time to inform the model's response.

Answer: D

NEW QUESTION # 73
A research institute is designing an infrastructure to support its entire AI drug discovery pipeline.
The pipeline has two distinct workload requirements:
1. Training: A team of data scientists needs to train several large transformer models simultaneously using a 500 TB dataset of genomic sequences. This process requires maximum data throughput to keep the GPUs saturated.
2. Inference: Once trained, the models are deployed to an internal web portal where researchers submit individual protein sequences for analysis. These queries must return results with the lowest possible latency.
Which infrastructure design best satisfies both requirements? (Choose 2.)

A. Use a single, large Cloud Volumes ONTAP instance in a public cloud to handle both workloads to simplify management.
B. Configure QoS minimums on the training volumes to ensure they do not impact inference performance.
C. Use NetApp StorageGRID as the primary storage for both the training and low-latency inference workloads.
D. Deploy a large NetApp ASA cluster with GPUDirect Storage enabled for the training environment.
E. Implement NetApp FlexCache on smaller nodes at the network edge to serve the inference requests.

Answer: D,E

NEW QUESTION # 74
To comply with the security mandate, the architect must design a process to prevent client PII from the portfolio database from ever being included in the context sent to the LLM. Which two actions are required to build a robust and automated solution for this? (Choose 2.)

A. Implement a data preparation step in the RAG pipeline that programmatically masks or excludes any data fields tagged as PII before the context is constructed.
B. Rely on a disclaimer in the chatbot UI telling advisors not to enter queries related to specific clients.
C. Use BlueXP classification to perform an initial scan of the portfolio database to identify and tag all columns containing PII.
D. Fine-tune the LLM on a sanitized dataset to teach it to ignore PII.
E. Move the entire client portfolio database to an encrypted, air-gapped system.

Answer: A,C

NEW QUESTION # 75
The data anonymization job, running in a Kubernetes pod, fails. The pod logs show a "Permission Denied" error when trying to access the source volume on the on-premises ASA. An administrator checks the export policy rule for the volume.
The active rule is as follows:
Rule_Index: 1
Client_Match: 10.50.0.0/16
Protocols: nfs4
Read_Only_Access: sys
Read_Write_Access: -
Superuser_Access: none
The Kubernetes pod that failed has an IP address of '10.60.5.10'.
What is the cause of the "Permission Denied" error?

A. The pod's IP address ('10.60.5.10') is not within the allowed client match range ('10.50.0.0/16').
B. The export policy does not grant read-write access, which is required by the anonymization job.
C. The export policy only allows access via the NFSv4 protocol.
D. The 'Superuser_Access' setting is too restrictive.

Answer: A

NEW QUESTION # 76
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