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Network Appliance NS0-901 Exam Syllabus Topics:

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 C-Series
B) NetApp ASA (All-SAN Array)
C) NetApp StorageGRID
D) NetApp E-Series

2. To meet HIPAA compliance, the first step in the data pipeline is to identify all medical scans that contain embedded PII. The solution must be automated and capable of scanning data in-place on the on-premises ASA system.
Which two technologies should be used to accomplish this identification and tagging task?
(Choose 2.)

A) A custom Python script that uses regular expressions to search file contents.
B) NetApp SnapLock to make the source data immutable before scanning.
C) A custom "PII" category within BlueXP classification to identify specific medical record number formats.
D) A manual review process where technicians visually inspect each scan for PII.
E) NetApp BlueXP classification, configured to scan the on-premises ASA working environment.

3. Due to the success of the "Advisor Assistant," the number of concurrent users is expected to double in the next quarter. The existing Kubernetes cluster is running at 80% of its GPU capacity during peak hours. The architect must propose a plan to scale the compute infrastructure to handle the increased load.
Which two strategies represent the most effective and scalable solutions? (Choose 2.)

A) Vertically scale the existing nodes by adding more RAM and CPU cores to each one.
B) Horizontally scale the cluster by adding new GPU-equipped nodes.
C) Configure the Kubernetes Cluster Autoscaler to automatically add new nodes to the cluster from a predefined node pool when GPU demand exceeds current capacity.
D) Replace all existing GPUs with the next-generation model to increase the performance of each node.
E) Manually deploy all new application replicas to a single, new, very large "super-node".

4. A national research laboratory is investing in a turnkey AI infrastructure solution. Their primary goal is to eliminate the complexity and risk of designing and integrating the compute, network, and storage components themselves. The solution must be pre-validated by the vendors to deliver predictable, linear performance as they scale from one to multiple compute nodes. Which two options represent this type of pre-validated, converged infrastructure solution for AI? (Choose two)

A) NetApp AIPod, which integrates NVIDIA DGX servers with NetApp storage and networking.
B) A Cisco and NetApp FlexPod for AI solution.
C) A single NetApp FAS system connected directly to a single server.
D) A custom-built server rack with components sourced from various different vendors.
E) A public cloud provider's general-purpose virtual machine instances.

5. An AI training job is running slower than expected. The infrastructure team suspects a compute bottleneck. The job involves processing high-resolution images with a complex convolutional neural network (CNN). They review the logs from the training script.
Epoch 1/100 - 3600s - loss: 1.253 - acc: 0.54
...
CPU_Utilization_During_Epoch: 99% (all cores)
GPU_0_Utilization_During_Epoch: 8%
GPU_1_Utilization_During_Epoch: 7%
...
Epoch 2/100 - 3610s - loss: 1.102 - acc: 0.61
What is the most likely cause of the poor training performance?

A) The model is too simple and does not effectively utilize the GPU's parallel processing capabilities.
B) The storage system cannot deliver data fast enough to the compute node.
C) The network is saturated, preventing the GPUs from receiving data.
D) The training script is CPU-bound, likely performing data augmentation or preprocessing on the CPU instead of offloading it to the GPUs.

Solutions: