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Network Appliance NS0-901 Exam Syllabus Topics:

Section	Objectives
Topic 1: AI Infrastructure and NetApp Solutions	- AI-ready data infrastructure 1. High-performance storage for AI workloads 2. Data pipeline optimization for AI/ML workloads - Converged workloads 1. Shared infrastructure design considerations 2. AI, HPC, and analytics convergence
Topic 2: AI Lifecycle and Deployment	- Operational challenges 1. Scalability and performance optimization 2. Data governance and management - End-to-end AI lifecycle 1. Model training and validation 2. Data ingestion and preparation 3. Model deployment and monitoring
Topic 3: Industry Use Cases	- AI applications across industries 1. Digital twins 2. Healthcare AI 3. Autonomous systems and agents
Topic 4: AI and Machine Learning Fundamentals	- Algorithm types 1. Supervised learning 2. Reinforcement learning 3. Unsupervised learning - AI, ML, DL concepts 1. Training, inference, and prediction workflows 2. Differences between AI, machine learning, and deep learning

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Network Appliance NetApp Certified AI Expert Exam Sample Questions (Q87-Q92):

NEW QUESTION # 87
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. 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. Deploy a large NetApp ASA cluster with GPUDirect Storage enabled for the training environment.
E. Implement NetApp FlexCache on smaller nodes at the network edge to serve the inference requests.

Answer: D,E

NEW QUESTION # 88
An architect is designing a fully automated, end-to-end MLOps pipeline on Kubernetes for a computer vision use case. The pipeline must handle everything from data versioning to model deployment.
The required pipeline stages are:
1. Data Versioning: Create a new, immutable version of the master dataset for the pipeline run.
2. Data Preparation: Launch a pod to run a preprocessing script on the versioned data.
3. Model Training: Launch a distributed training job that reads the prepared data from a highperformance volume.
4. Model Deployment: Push the trained model to a production inference service.
Which combination of NetApp and Kubernetes technologies provides the most effective and automated solution for this entire pipeline?

A. Use the NetApp DataOps Toolkit to create a Snapshot of the source data volume (for versioning), then create a FlexClone PVC from the snapshot for the preparation stage, and finally create a FlexGroup PVC for the training stage.
B. Use NetApp SnapMirror for data versioning and manually create hostPath volumes for each pipeline stage.
C. Use a single, large ReadWriteMany PVC for all stages to simplify the pipeline configuration.
D. Manually create a NetApp Snapshot via System Manager before each pipeline run, and use the NetApp DataOps Toolkit only for the training stage.
E. Use NetApp XCP to copy the data for each stage and configure static PersistentVolumes for each pod.

Answer: A

NEW QUESTION # 89
An AI team is embarking on a project to train a new, large-scale computer vision model from scratch. The lead architect emphasizes that the success of the project depends on four fundamental inputs that must be available and managed throughout the training process. Which of the following are the four essential requirements for model generation?

A. A data lake, a data warehouse, a data pipeline, and a data mart.
B. A pre-trained model, a validation set, an inference engine, and a cloud provider.
C. Data, code, compute, and time.
D. A project manager, a data scientist, a software engineer, and a budget.

Answer: C

NEW QUESTION # 90
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 GPUs are faulty and cannot process data at their rated speed.
B. The storage array is too slow and cannot serve data quickly enough.
C. The network connecting the compute nodes and storage is saturated and has become the primary bottleneck.
D. The CPU is underpowered and cannot preprocess the data fast enough for the GPUs.

Answer: C

NEW QUESTION # 91
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 compute and storage infrastructure is sized for a large-scale training workload, not a lightweight inference workload.
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 model was trained using supervised learning, which is inefficient for fraud detection.

Answer: A

NEW QUESTION # 92
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