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Network Appliance NS0-901 Exam Syllabus Topics:

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

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Network Appliance NetApp Certified AI Expert Exam Sample Questions (Q76-Q81):

NEW QUESTION # 76
An MLOps team is using the BlueXP Workload Factory to automate the deployment of a Jupyter notebook environment for a new data scientist. The deployment job fails. The team reviews the job details in the BlueXP UI.
Job ID: 78a1c-2b3d-4e5f
Service: BlueXP Workload Factory
Action: Deploy JupyterHub on Kubernetes
Status: FAILED
Error Message: "Failed to create PersistentVolumeClaim 'jupyter-pvc-user01'.
User 'service-account- bluexp' does not have permission to 'create' resource
'persistentvolumeclaims' in cluster 'k8s-prod- cluster-01'."
What is the most direct way to resolve this issue?

A. Manually create the PersistentVolumeClaim using 'kubectl'.
B. Modify the Kubernetes RBAC (Role-Based Access Control) configuration to grant the 'service- account-bluexp' the necessary permissions to create PVCs in the target cluster.
C. Restart the BlueXP Connector to refresh its permissions.
D. Increase the storage quota for the 'jupyter-pvc-user01' PVC.

Answer: B

NEW QUESTION # 77
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 application is experiencing excessive memory paging.
B. The storage system (ASA) is unable to provide sufficient read throughput.
C. The server's network interface is saturated.
D. The server's CPU is overloaded.

Answer: C

NEW QUESTION # 78
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. Retrieval-Augmented Generation (RAG), which fetches relevant information from the documents at query time to inform the model's response.
D. Unsupervised learning, which clusters the documents into topics for the model to browse.

Answer: C

NEW QUESTION # 79
A financial services company is required by regulators to be able to trace any version of their deployed fraud detection model back to the exact dataset and source code commit used to train it.
The current MLOps workflow is as follows:
Code_Repository: Git (commit hash: a1b2c3d4)
Dataset_Location: /vol/prod_data/fraud_dataset_v3
Storage_System: NetApp ONTAP 9
Model_Output: /vol/models/fraud_model_v3.2
Which NetApp technology should be used to create an immutable, point-in-time, and space- efficient copy of the dataset that can be linked to the specific code commit and model version?

A. NetApp FabricPool
B. NetApp SnapMirror
C. NetApp FlexClone
D. NetApp Snapshots

Answer: D

NEW QUESTION # 80
A pharmaceutical company is creating a "digital twin" of its manufacturing process. This involves running complex simulations (an HPC workload) that generate massive datasets.
The company wants to use this data immediately for two other purposes:
1. Analytics: Business analysts need to run complex queries on the simulation output using tools like Spark.
2. AI Training: Data scientists need to use the same output as a training set for a predictive maintenance model.
The company wants to avoid creating separate data silos for each workload.
Which two NetApp technologies are best suited for building a unified data lake that can efficiently serve all three workloads (HPC, Analytics, AI)? (Choose 2.)

A. NetApp StorageGRID to provide a scalable, S3-native object store that integrates directly with modern analytics platforms like Spark.
B. NetApp ONTAP with FlexGroup volumes to provide high-throughput, parallel NFS access for the HPC and AI training workloads.
C. NetApp Keystone to provide a flexible, pay-as-you-go consumption model for the infrastructure.
D. NetApp Autonomous Ransomware Protection to secure the data from modification.
E. NetApp SnapCenter to create application-consistent backups of the data.

Answer: A,B

NEW QUESTION # 81
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