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Network Appliance NS0-901 Exam Syllabus Topics:

Section	Objectives
AI Lifecycle and Deployment	- Operational challenges 1. Scalability and performance optimization 2. Data governance and management - End-to-end AI lifecycle 1. Model deployment and monitoring 2. Data ingestion and preparation 3. Model training and validation
AI Infrastructure and NetApp Solutions	- Converged workloads 1. Shared infrastructure design considerations 2. AI, HPC, and analytics convergence - AI-ready data infrastructure 1. High-performance storage for AI workloads 2. Data pipeline optimization for AI/ML workloads
Industry Use Cases	- AI applications across industries 1. Healthcare AI 2. Autonomous systems and agents 3. Digital twins
AI and Machine Learning Fundamentals	- Algorithm types 1. Supervised learning 2. Unsupervised learning 3. Reinforcement 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 (Q31-Q36):

NEW QUESTION # 31
Which of the following describes the impact of generative AI in content creation?

A. Generative AI predicts customer preferences without creating new content.
B. Generative AI can create new content such as text, images, and videos.
C. Generative AI analyzes data without generating content.
D. Generative AI only processes pre-existing content.

Answer: B

NEW QUESTION # 32
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 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 storage tier is too slow, causing the GPUs to wait for data.

Answer: B

NEW QUESTION # 33
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. Deploy a large NetApp ASA cluster with GPUDirect Storage enabled for the training environment.
C. Configure QoS minimums on the training volumes to ensure they do not impact inference performance.
D. Use NetApp StorageGRID as the primary storage for both the training and low-latency inference workloads.
E. Use a single, large Cloud Volumes ONTAP instance in a public cloud to handle both workloads to simplify management.

Answer: A,B

NEW QUESTION # 34
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. A custom-built server rack with components sourced from various different vendors.
B. A single NetApp FAS system connected directly to a single server.
C. A Cisco and NetApp FlexPod for AI solution.
D. A public cloud provider's general-purpose virtual machine instances.
E. NetApp AIPod, which integrates NVIDIA DGX servers with NetApp storage and networking.

Answer: C,E

NEW QUESTION # 35
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 BlueXP Connector does not have the correct permissions to manage the SnapMirror relationship.
B. The source volume at the edge location has become corrupted.
C. The SnapMirror relationship is broken because the destination volume at the core data center has run out of capacity.
D. The network connection between the edge and the core is unreliable.

Answer: C

NEW QUESTION # 36
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