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Network Appliance NS0-901 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: AI Lifecycle | 27% | - AI lifecycle stages: design, training, deployment, monitoring - Predictive vs generative AI - AI governance, ethics, and compliance - Model training, inference, and optimization - Data preparation and management for AI |
| Topic 2: Security, Reliability, and Operations | 15% | - Monitoring, logging, and troubleshooting AI environments - Data security and access control for AI - Cost management and efficiency - High availability and data protection |
| Topic 3: Cloud and Hybrid Cloud AI Deployment | 18% | - Data mobility and consistency across environments - Hybrid and multi-cloud AI architectures - Cloud-native AI solutions and integration - NetApp cloud data services for AI |
| Topic 4: NetApp AI Solutions and Architecture | 25% | - Storage architectures for AI workloads - NetApp AI-ready infrastructure components - ONTAP integration with AI frameworks - Scalability and performance optimization for AI - Data management and data pipeline design |
| Topic 5: AI Overview | 15% | - Convergence of AI, high-performance computing, and analytics - AI, machine learning, and deep learning concepts - AI industry use cases and applications - Algorithm types: supervised, unsupervised, reinforcement learning - AI deployment models: on-premises, cloud, edge |
Network Appliance NetApp Certified AI Expert Sample Questions:
An AI architect is designing a solution for a legal firm. The primary goal is to allow lawyers to ask natural language questions about case law stored in a private, 50 TB document repository.
The key project constraints are as follows:
Project_Goal: Answer questions using proprietary, real-time legal documents.
Constraint_1: Must not alter the foundational LLM's weights due to compliance.
Constraint_2: Case law database is updated daily with new rulings.
Constraint_3: All generated answers must be traceable to a source document.
Which technology should the architect choose as the core of this solution?
- A. A Retrieval-Augmented Generation (RAG) architecture.
- B. A new LLM trained from scratch on the legal documents.
- C. A predictive AI model to classify legal documents.
- D. A fine-tuning pipeline to update the LLM daily.
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 BlueXP Connector is offline and cannot communicate with the cloud provider.
- C. The selected ONTAP version does not support the GenAI toolkit.
- D. The Azure service principal associated with the BlueXP Connector lacks the necessary IAM role to create resource groups.
The data scientists report that their Kubernetes-based data preparation jobs are failing. The pods are stuck in a 'Pending' state.
An MLOps engineer runs 'kubectl describe pvc data-prep-pvc-01' and sees the following event:
Type Reason Age From Message
- - - -
Warning ProvisioningFailed 2m15s trident-orchestrator-7b... failed to provision volume with StorageClass "bronze-tier": backend unavailable: no healthy backend with satisfying attributes for storage class "bronze-tier" The engineer checks the Trident backend configurations and finds no backend associated with the "bronze-tier" StorageClass.
What is the root cause of the failure?
- A. The Kubernetes scheduler is unable to find a node with enough resources for the pods.
- B. The data preparation pods do not have the correct security permissions.
- C. The PersistentVolumeClaim is referencing a StorageClass that has no configured or healthy Trident backend to provision storage from.
- D. The StorageGRID data lake is offline, preventing the creation of new volumes.
A data scientist is working on a new model and needs a flexible environment for interactive data exploration, code development, and quick visualizations. A DevOps engineer is responsible for deploying the finalized model into a production pipeline that must run automatically every night without manual intervention.
Which tools are best suited for each of these roles?
- A. The data scientist should use a Jupyter Notebook, and the DevOps engineer should use an automated production pipeline (e.g., Kubeflow Pipelines, Airflow).
- B. Both the data scientist and the DevOps engineer should use automated production pipelines.
- C. The data scientist should use a production pipeline, and the DevOps engineer should use a Jupyter Notebook.
- D. Both the data scientist and the DevOps engineer should use Jupyter Notebooks.
Which of the following techniques are used to maximize GPU utilization in AI workloads? (Choose two)
- A. Scheduling GPU-intensive tasks during off-peak hours
- B. Balancing compute and storage workloads efficiently
- C. Using larger batch sizes
- D. Utilizing CPU-based workloads for all tasks
