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Network Appliance NS0-901 Exam Syllabus Topics:

Section	Objectives
Topic 1: AI Lifecycle and Deployment	- End-to-end AI lifecycle 1. Model training and validation 2. Data ingestion and preparation 3. Model deployment and monitoring - Operational challenges 1. Data governance and management 2. Scalability and performance optimization
Topic 2: Industry Use Cases	- AI applications across industries 1. Digital twins 2. Autonomous systems and agents 3. Healthcare AI
Topic 3: 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
Topic 4: AI and Machine Learning Fundamentals	- Algorithm types 1. Supervised learning 2. Reinforcement learning 3. Unsupervised learning - AI, ML, DL concepts 1. Training, inference, and prediction workflows 2. Differences between AI, machine learning, and deep learning

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Network Appliance NetApp Certified AI Expert Exam Sample Questions (Q68-Q73):

NEW QUESTION # 68
An AI platform administrator is trying to deploy a new GenAI toolkit using the BlueXP Workload Factory. The deployment fails, and the administrator examines the API response from BlueXP.
{
"jobId": "we-deploy-genai-987zy",
"status": "FAILED",
"statusCode": 403,
"message":
"Forbidden: The service principal or user account used by the Connector does not have the required permissions on the target subscription to create a new resource group.
Required permission: 'Microsoft.Resources/subscriptions/resourcegroups/write'." } Based on this API response, what is the root cause of the deployment failure?

A. The GenAI toolkit requires a specific license that has not been added to the BlueXP digital wallet.
B. The Azure service principal associated with the BlueXP Connector lacks the necessary IAM role to create resource groups.
C. The BlueXP Connector is offline and cannot communicate with the cloud provider.
D. The selected ONTAP version does not support the GenAI toolkit.

Answer: B

NEW QUESTION # 69
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. Configure QoS minimums on the training volumes to ensure they do not impact inference performance.
B. Use NetApp StorageGRID as the primary storage for both the training and low-latency inference workloads.
C. Implement NetApp FlexCache on smaller nodes at the network edge to serve the inference requests.
D. Deploy a large NetApp ASA cluster with GPUDirect Storage enabled for the training environment.
E. Use a single, large Cloud Volumes ONTAP instance in a public cloud to handle both workloads to simplify management.

Answer: C,D

NEW QUESTION # 70
An architect is designing a scalable, automated MLOps platform using Kubeflow on a Kubernetes cluster. The platform must support the entire AI lifecycle for multiple teams, with different storage requirements at each stage.
The key requirements are:
- Data Ingestion: A pipeline step needs a shared, read-write volume accessible by multiple pods to stage raw data.
- Experimentation: Data scientists need individual, isolated volumes for their Jupyter notebooks.
- Training: Distributed training jobs require a high-performance, parallel-access filesystem for reading training data.
- Automation: All storage must be provisioned automatically via Kubeflow pipeline definitions without manual intervention.
Which combination of technologies and configurations would create the most effective solution?

A. Rely on hostPath volumes for all storage to ensure the highest performance.
B. Use the NetApp DataOps Toolkit for all storage provisioning, bypassing Trident and Kubernetes PVCs.
C. Create a single, large NFS volume and mount it to all pods using a static PersistentVolume.
D. Configure multiple Trident backends (e.g., 'ontap-nas' for standard volumes, 'ontap-nas-flexgroup' for parallel access) and corresponding StorageClasses.
E. Use the NetApp DataOps Toolkit for Python within the Kubeflow pipeline components to dynamically create and manage Trident PVCs for each stage.

Answer: D,E

NEW QUESTION # 71
What is the primary role of NetApp Trident in a Kubernetes environment designed for AI workloads?

A. To directly accelerate GPU computations using specialized drivers.
B. To act as a dynamic storage orchestrator, provisioning persistent storage from NetApp backends on- demand for containerized applications.
C. To provide a web-based IDE, like Jupyter, for data scientists to develop models.
D. To function as a container runtime interface for executing AI models.

Answer: B

NEW QUESTION # 72
In the data pipeline, which NetApp technology is used to create an efficient, block-level, incremental replication of the anonymized datasets from the on-premises ASA to the Cloud Volumes ONTAP instance in Azure?

A. NetApp BlueXP copy and sync
B. NetApp FabricPool
C. NetApp SnapMirror
D. NetApp XCP

Answer: C

NEW QUESTION # 73
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