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Network Appliance NS0-901 Exam Syllabus Topics:

Network Appliance NetApp Certified AI Expert Sample Questions:

1. 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) Implement NetApp FlexCache on smaller nodes at the network edge to serve the inference requests.
C) Use a single, large Cloud Volumes ONTAP instance in a public cloud to handle both workloads to simplify management.
D) Deploy a large NetApp ASA cluster with GPUDirect Storage enabled for the training environment.
E) Use NetApp StorageGRID as the primary storage for both the training and low-latency inference workloads.

2. The AI training jobs on the AIPod are performing below expectations. The NVIDIA DGX servers' GPUs show low utilization. A performance analysis reveals that the bottleneck is not the storage system itself, but the network path between the storage and the compute nodes.
The current network configuration is as follows:
Network_Fabric: 100GbE Standard Ethernet
Protocol: NFS over TCP/IP
Data_Path: Storage -> Host CPU -> GPU Memory
Which network architecture enhancement would provide the most significant performance improvement by reducing latency and CPU overhead?

A) Upgrade the network switches to a model with a larger packet buffer.
B) Implement RDMA over Converged Ethernet (RoCE) and configure GPUDirect Storage.
C) Add more 100GbE network ports to the storage controllers.
D) Isolate the storage traffic on a separate VLAN from the management traffic.

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

4. The architect is designing the complete, automated data pipeline from the on-premises data center to the Azure cloud for this medical imaging project. The design must prioritize security, efficiency, and reproducibility.
Which sequence of actions provides the most robust and automated solution?

A) Manually copy raw data to Azure, run a cloud-based script to anonymize it, then start training.
B) Use SnapMirror to replicate the raw, sensitive data to Azure first, then run BlueXP classification in the cloud to find PII.
C) Use BlueXP classification to scan the on-prem data; use a DataOps Toolkit script to trigger a FlexClone of the source volume for anonymization; use SnapMirror to replicate the anonymized clone to Cloud Volumes ONTAP; then begin training in Azure.
D) Use FabricPool to automatically tier all raw data to Azure Blob storage, then mount the blob container to the training VMs.

5. 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 model was trained using supervised learning, which is inefficient for fraud detection.
B) The storage tier is too slow, causing the GPUs to wait for data.
C) The network latency is too high for an edge deployment.
D) The compute and storage infrastructure is sized for a large-scale training workload, not a lightweight inference workload.

Solutions: