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Network Appliance NS0-901 Exam Syllabus Topics:

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

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Network Appliance NetApp Certified AI Expert Exam NS0-901 Prüfungsfragen mit Lösungen (Q17-Q22):

17. Frage
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 NetApp SnapMirror for data versioning and manually create hostPath volumes for each pipeline stage.
B. Manually create a NetApp Snapshot via System Manager before each pipeline run, and use the NetApp DataOps Toolkit only for the training stage.
C. Use a single, large ReadWriteMany PVC for all stages to simplify the pipeline configuration.
D. Use NetApp XCP to copy the data for each stage and configure static PersistentVolumes for each pod.
E. 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.

Antwort: E

18. Frage
The Chief Information Security Officer (CISO) is concerned about the risk of a ransomware attack encrypting the critical vector database hosted on the NetApp AFF A-Series. The CISO wants a solution that can proactively detect and block a live attack in real-time, not just recover from a backup after the fact.
Which NetApp security feature should the architect enable on the vector database volume to meet this requirement?

A. NetApp SnapLock (Compliance Mode)
B. Autonomous Ransomware Protection (ARP)
C. Multi-Admin Verification (MAV)
D. NetApp Volume Encryption (NVE)

Antwort: B

19. Frage
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 storage array is too slow and cannot serve data quickly enough.
C. The CPU is underpowered and cannot preprocess the data fast enough for the GPUs.
D. The GPUs are faulty and cannot process data at their rated speed.

Antwort: A

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

Antwort: B

21. Frage
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 only processes pre-existing content.
C. Generative AI analyzes data without generating content.
D. Generative AI can create new content such as text, images, and videos.

Antwort: D

22. Frage
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