Network Appliance NS0-901 Exam Overview:
| Certification Vendor: | NetApp (Network Appliance) |
| Exam Name: | NetApp Certified AI Expert Exam |
| Exam Number: | NS0-901 |
| Real Exam Qty: | 60 |
| Passing Score: | 66% |
| Certificate Validity Period: | 2 years |
| Exam Format: | Multiple-choice, Scenario-based questions |
| Exam Duration: | 90 minutes |
| Available Languages: | English |
| Exam Price: | 250 USD |
| Recommended Training: | NetApp Learning Services - AI Expert Training |
| Exam Registration: | Pearson VUE Registration |
| Sample Questions: | Network Appliance NS0-901 Sample Questions |
| Exam Way: | Online proctored or onsite at Pearson VUE test centers |
| Pre Condition: | 6–12 months of technical experience with AI workloads; knowledge of NetApp ONTAP, AI frameworks, and data workflows |
| Official Syllabus URL: | https://www.netapp.com/support-and-training/netapp-learning-services/certifications/ai-expert/ |
Network Appliance NS0-901 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Security, Reliability, and Operations | 15% | - Data security and access control for AI - High availability and data protection - Cost management and efficiency - Monitoring, logging, and troubleshooting AI environments |
| Cloud and Hybrid Cloud AI Deployment | 18% | - Data mobility and consistency across environments - NetApp cloud data services for AI - Hybrid and multi-cloud AI architectures - Cloud-native AI solutions and integration |
| NetApp AI Solutions and Architecture | 25% | - ONTAP integration with AI frameworks - NetApp AI-ready infrastructure components - Storage architectures for AI workloads - Scalability and performance optimization for AI - Data management and data pipeline design |
| AI Overview | 15% | - AI industry use cases and applications - AI, machine learning, and deep learning concepts - Convergence of AI, high-performance computing, and analytics - AI deployment models: on-premises, cloud, edge - Algorithm types: supervised, unsupervised, reinforcement learning |
| AI Lifecycle | 27% | - AI governance, ethics, and compliance - Model training, inference, and optimization - Data preparation and management for AI - Predictive vs generative AI - AI lifecycle stages: design, training, deployment, monitoring |
Network Appliance NetApp Certified AI Expert Sample Questions:
1. An AI architect is designing a storage solution for a new training cluster. The primary workload consists of training large language models, which involves sequential reads of massive datasets.
The key requirement is to maximize GPU utilization by providing the highest possible data throughput. Cost is a secondary concern to performance.
Which NetApp storage system is the most appropriate choice for this workload?
A) NetApp StorageGRID
B) NetApp C-Series
C) NetApp ASA (All-SAN Array)
D) NetApp E-Series
2. 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 storage tier is too slow, causing the GPUs to wait for data.
B) The compute and storage infrastructure is sized for a large-scale training workload, not a lightweight inference workload.
C) The network latency is too high for an edge deployment.
D) The model was trained using supervised learning, which is inefficient for fraud detection.
3. A data scientist needs to test a new data normalization technique. To do this, they require an isolated, writable copy of a 50 TB curated simulation dataset that resides on the NetApp ASA system. The operation must be completed as quickly as possible and consume minimal additional storage space. Which NetApp technology is the most appropriate solution for this requirement?
A) NetApp SnapMirror
B) NetApp XCP
C) NetApp FlexClone
D) NetApp FabricPool
4. 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 SnapMirror
B) NetApp Snapshots
C) NetApp FlexClone
D) NetApp FabricPool
5. A research lab uses a fleet of autonomous drones to collect high-resolution aerial imagery for agricultural analysis. The drones land at a remote edge location and offload their data. The AI models for image analysis are trained at a central data center. The team is using NetApp SnapMirror to replicate the data from the edge to the core. However, the data scientists are complaining that the datasets arriving at the data center are often incomplete or corrupted.
An administrator reviews the SnapMirror configuration and status via the BlueXP API:
{
"source": { "workingEnvironmentId": "OnPrem-Edge-Filer-1", "volumeName": "drone_data_raw" },
"destination": { "workingEnvironmentId": "Core-Datacenter-A800", "volumeName":
"drone_data_replicated" },
"mirrorState": "broken-off",
"relationshipStatus": "idle",
"unhealthyReason": "Transfer failed. Destination volume is out of space.", "lastTransferInfo": {
"transferError": "No space left on device"
}
}
What is the direct cause of the incomplete datasets at the data center?
A) The source volume at the edge location has become corrupted.
B) The network connection between the edge and the core is unreliable.
C) The SnapMirror relationship is broken because the destination volume at the core data center has run out of capacity.
D) The BlueXP Connector does not have the correct permissions to manage the SnapMirror relationship.
Solutions:
| Question # 1 Answer: C | Question # 2 Answer: B | Question # 3 Answer: C | Question # 4 Answer: B | Question # 5 Answer: C |
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By Ellen

