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Network Appliance NS0-901 Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: AI Software Architectures	18%	- MLOps and LLMOps Ecosystems 1. Understanding the software tools and platforms enabling AI at scale - Development Tools 1. NetApp DataOps Toolkit 2. Jupyter notebooks vs. pipelines - Scaling and Orchestration 1. Leveraging BlueXP software tools 2. Scaling AI workloads with Kubernetes
Topic 2: AI Hardware Architectures	18%	- Infrastructure Topologies 1. Data aggregation and compute topologies - Networking and Storage 1. Storage architectures for AI 2. Network protocols for AI workloads - NetApp Architectures 1. BasePod 2. SuperPOD 3. OVX architectures
Topic 3: AI Overview	15%	- AI Deployment Models 1. Cloud 2. Edge 3. Benefits and risks of each model 4. On-premises - Algorithm Types 1. Supervised learning 2. Reinforcement learning 3. Unsupervised learning - AI Industry Applications 1. Healthcare applications 2. Agents 3. Digital twins - Machine Learning Fundamentals 1. Describe machine learning benefits 2. Understand the relationship between AI, machine learning, and deep learning - AI Convergence with HPC and Analytics 1. Leveraging shared infrastructure for AI, HPC, and analytics - Training vs. Inferencing vs. Predictions 1. Distinguish between training and inference workloads
Topic 4: AI Lifecycle	27%	- Generative AI Concepts 1. Fine-tuning 2. Retrieval Augmented Generation (RAG) 3. Hallucinations - Predictive AI vs. Generative AI 1. Impact of generative content (text, images, video, decision-making) 2. Distinction between predictive and generative AI 3. Large Language Models (LLMs) - Data Preparation 1. Data aggregation and cleansing 2. XCP and CopySync 3. NetApp BlueXP Classification - Model Development 1. Inferencing 2. Fine-tuning workflows 3. Model building
Topic 5: AI Common Challenges	22%	- Traceability and Optimization 1. Ensuring traceability for code, data, and models 2. Maximizing performance in demanding AI workloads 3. Optimizing data access and movement - Resource Management 1. Sizing storage and compute resources effectively 2. Controlling costs and securing storage

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Network Appliance NetApp Certified AI Expert Exam Sample Questions (Q17-Q22):

NEW QUESTION # 17
In the data pipeline, which NetApp technology is used to create an efficient, block-level, incremental replication of the anonymized datasets from the on-premises ASA to the Cloud Volumes ONTAP instance in Azure?

A. NetApp FabricPool
B. NetApp XCP
C. NetApp SnapMirror
D. NetApp BlueXP copy and sync

Answer: C

NEW QUESTION # 18
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 keeps the high-priority job in a pending state until the low-priority jobs complete naturally.
B. It sends an email notification to the administrator to manually reallocate the GPUs.
C. It automatically terminates all low-priority jobs to free up the entire cluster.
D. 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.

Answer: D

NEW QUESTION # 19
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 storage system cannot deliver data fast enough to the compute node.
B. The network is saturated, preventing the GPUs from receiving data.
C. The training script is CPU-bound, likely performing data augmentation or preprocessing on the CPU instead of offloading it to the GPUs.
D. The model is too simple and does not effectively utilize the GPU's parallel processing capabilities.

Answer: C

NEW QUESTION # 20
A junior administrator is attempting to delete an old, unused Snapshot copy from a production volume to reclaim space but receives an error. The administrator is certain they are using the correct command and have the appropriate 'vsadmin' role.
The command and error are shown below:
cluster-1::> volume snapshot delete -vserver svm_prod -volume app_data -snapshot nightly.2025- 06-15_0015 Error: command failed: This operation is not permitted. Deletion of Snapshot copy "nightly.2025-
06-15_0015" on volume "app_data" in Vserver "svm_prod" requires approval.
Use the "security multi-admin-verify approval show" command to view pending approvals.
What is the most likely reason the administrator cannot delete the Snapshot?

A. The volume is protected by SnapLock, and the Snapshot has not reached its expiry date.
B. The Snapshot is locked by a SnapMirror relationship.
C. The 'vsadmin' role does not have permission to delete Snapshots.
D. Multi-Admin Verification (MAV) is enabled, and the destructive operation requires approval from another designated administrator.

Answer: D

NEW QUESTION # 21
The HPC cluster generates simulation data at an extremely high rate, requiring a storage system that can handle massively parallel writes from hundreds of compute nodes simultaneously. Which storage system and file protocol combination is the most appropriate choice for the HPC cluster's high-performance scratch space?

A. A NetApp StorageGRID system accessed via the S3 protocol.
B. A NetApp ASA system serving a single, large NFS volume.
C. A Cloud Volumes ONTAP instance with a standard file system.
D. A NetApp E-Series system serving a BeeGFS parallel file system.

Answer: D

NEW QUESTION # 22
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