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Network Appliance NS0-901 Exam Syllabus Topics:

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

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Network Appliance NetApp Certified AI Expert Exam Sample Questions (Q34-Q39):

NEW QUESTION # 34
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. Use NetApp StorageGRID as the primary storage for both the training and low-latency inference workloads.
B. Configure QoS minimums on the training volumes to ensure they do not impact inference performance.
C. Implement NetApp FlexCache on smaller nodes at the network edge to serve the inference requests.
D. Deploy a large NetApp ASA cluster with GPUDirect Storage enabled for the training environment.
E. Use a single, large Cloud Volumes ONTAP instance in a public cloud to handle both workloads to simplify management.

Answer: C,D

NEW QUESTION # 35
What is the primary architectural benefit of using technologies like RDMA (Remote Direct Memory Access) and GPUDirect Storage in a high-performance AI training cluster?

A. They reduce the power consumption of the storage array by offloading checksum calculations.
B. They automatically encrypt data in-flight between the GPU servers and the storage system.
C. They allow GPUs to communicate directly with each other and with NVMe storage, bypassing the host CPU and system memory, which significantly reduces I/O latency and CPU overhead.
D. They enable the use of lower-cost Ethernet switches in place of InfiniBand fabrics without any performance degradation.

Answer: C

NEW QUESTION # 36
Which of the following describes the impact of generative AI in content creation?

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

Answer: A

NEW QUESTION # 37
An MLOps team is using the BlueXP Workload Factory to automate the deployment of a Jupyter notebook environment for a new data scientist. The deployment job fails. The team reviews the job details in the BlueXP UI.
Job ID: 78a1c-2b3d-4e5f
Service: BlueXP Workload Factory
Action: Deploy JupyterHub on Kubernetes
Status: FAILED
Error Message: "Failed to create PersistentVolumeClaim 'jupyter-pvc-user01'.
User 'service-account- bluexp' does not have permission to 'create' resource
'persistentvolumeclaims' in cluster 'k8s-prod- cluster-01'."
What is the most direct way to resolve this issue?

A. Restart the BlueXP Connector to refresh its permissions.
B. Increase the storage quota for the 'jupyter-pvc-user01' PVC.
C. Modify the Kubernetes RBAC (Role-Based Access Control) configuration to grant the 'service- account-bluexp' the necessary permissions to create PVCs in the target cluster.
D. Manually create the PersistentVolumeClaim using 'kubectl'.

Answer: C

NEW QUESTION # 38
The firm decides to expand the "Advisor Assistant" project to a new team in a different department. This team needs its own isolated environment. An MLOps engineer attempts to submit a new GPU- intensive job for the new team, but it remains pending. The engineer checks the Run:AI scheduler logs and finds the following entry:
time="2025-07-11T16:45:00Z" level=info msg="Job ds-new-team-job1 cannot be scheduled.
Project 'new-team-project' has exceeded its GPU quota. Quota: 0, Requested: 1, Used: 0." What is the root cause of the scheduling failure?

A. The job is requesting a specific type of GPU that is not available in the cluster.
B. The Run:AI scheduler is offline and cannot process new jobs.
C. The Kubernetes cluster has no available GPUs.
D. A Run:AI project quota has been configured for the new team, but it has been set to zero, effectively blocking them from using any GPU resources.

Answer: D

NEW QUESTION # 39
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