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Network Appliance NS0-901 Exam Syllabus Topics:

Network Appliance NetApp Certified AI Expert Sample Questions:

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

Question 2

The firm's data science team needs to run a high-priority, interactive model analysis job that requires immediate access to two GPUs. However, all GPUs in the cluster are currently allocated to long-running, lower-priority batch training jobs.
The MLOps platform, Run:AI, shows the following queue status:
JOB_ID | PROJECT | STATUS | PRIORITY | GPU_ALLOCATED
||--|-|
batch_job_1 | team_a | Running | Low | 2
batch_job_2 | team_a | Running | Low | 2
batch_job_3 | team_b | Running | Low | 4
interactive_1| team_c | Pending | High | 2 (requested)
How does the Run:AI platform address this resource contention to allow the high-priority job to run?

A. It sends an email notification to the administrator to manually reallocate the GPUs.
B. It keeps the high-priority job in a pending state until the low-priority jobs complete naturally.
C. It automatically pauses one of the low-priority jobs, saves its state, and allocates its GPUs to the high- priority job, placing the paused job back in the queue.
D. It automatically terminates all low-priority jobs to free up the entire cluster.

Question 3

An enterprise is planning a generative AI solution to power its internal support chatbot. The architect must choose between a RAG-based approach and fine-tuning a base model. The project stakeholders have provided a list of prioritized requirements.
| Requirement | Priority | Details
|
| | -- | |
| Factual Accuracy | Critical | Must use the latest product documentation, updated daily.
| | Brand Voice & Persona | High | Must respond in the company's specific, formal tone.
| | Development Cost | High | Limited budget for GPU compute hours for model training.
|
| Data Traceability | Critical | Must be able to cite the exact source document for each answer.
|
Which two recommendations should the architect make to best satisfy these requirements?
(Choose 2.)

A. Recommend training a new LLM from scratch to ensure both brand voice and factual accuracy are built-in.
B. Prioritize a RAG architecture to meet the critical requirements for factual accuracy and data traceability.
C. Use RAG exclusively, as prompt engineering alone can fully replicate a specific brand voice and persona.
D. Prioritize fine-tuning to embed the company's brand voice and persona into the model.
E. Propose a hybrid approach where a base model is first lightly fine-tuned for persona, then used within a RAG system for factual grounding.

Question 4

A distributed training job running on the AIPod fails to start. The MLOps engineer inspects the events for one of the pending training pods and sees the following message:
Events:
Type Reason Age From Message
- - - -
Warning FailedScheduling 5m12s default-scheduler 0/4 nodes are available: 4 node(s) had no available volume zone.
The PersistentVolumeClaim (PVC) for this pod specifies a StorageClass that uses the 'ontap-nas' Trident provisioner.
he Trident logs show no errors.
What is the most likely cause of this scheduling failure?

A. The 'requiredTopology' in the StorageClass is misconfigured, preventing Trident from finding a valid ONTAP backend in the same zone as the available compute nodes.
B. The Kubernetes nodes do not have the necessary NFS client utilities installed.
C. The NetApp ASA storage system is offline.
D. The training pod's container image does not exist in the registry.

Question 5

An AI infrastructure engineer is troubleshooting a poorly performing distributed training job. The job is running across multiple nodes, each equipped with powerful GPUs. The engineer observes that overall GPU utilization is unexpectedly low. System-level monitoring on the compute nodes provides the following metrics during a training run.
avg_gpu_utilization: 25%
avg_cpu_iowait_percent: 65%
avg_network_bandwidth_util: 95% (on a 10GbE network)
storage_array_latency: <1ms
Given these metrics, what is the most likely bottleneck causing the low GPU utilization?

A. The network connecting the compute nodes and storage is saturated and has become the primary bottleneck.
B. The CPU is underpowered and cannot preprocess the data fast enough for the GPUs.
C. The GPUs are faulty and cannot process data at their rated speed.
D. The storage array is too slow and cannot serve data quickly enough.

Solutions: