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Network Appliance NS0-901 Exam Syllabus Topics:

Section	Weight	Objectives
AI Common Challenges	22%	- Resource Management 1. Sizing storage and compute resources effectively 2. Controlling costs and securing storage - Traceability and Optimization 1. Maximizing performance in demanding AI workloads 2. Ensuring traceability for code, data, and models 3. Optimizing data access and movement
AI Software Architectures	18%	- Scaling and Orchestration 1. Scaling AI workloads with Kubernetes 2. Leveraging BlueXP software tools - MLOps and LLMOps Ecosystems 1. Understanding the software tools and platforms enabling AI at scale - Development Tools 1. Jupyter notebooks vs. pipelines 2. NetApp DataOps Toolkit
AI Overview	15%	- 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 - AI Industry Applications 1. Agents 2. Healthcare applications 3. Digital twins - 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 Deployment Models 1. Edge 2. Cloud 3. Benefits and risks of each model 4. On-premises
AI Hardware Architectures	18%	- Infrastructure Topologies 1. Data aggregation and compute topologies - NetApp Architectures 1. BasePod 2. OVX architectures 3. SuperPOD - Networking and Storage 1. Network protocols for AI workloads 2. Storage architectures for AI
AI Lifecycle	27%	- 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) - Generative AI Concepts 1. Retrieval Augmented Generation (RAG) 2. Hallucinations 3. Fine-tuning - Model Development 1. Model building 2. Fine-tuning workflows 3. Inferencing - Data Preparation 1. Data aggregation and cleansing 2. NetApp BlueXP Classification 3. XCP and CopySync

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Network Appliance NetApp Certified AI Expert Exam Sample Questions (Q46-Q51):

A. Prioritize fine-tuning to embed the company's brand voice and persona into the model.
B. Propose a hybrid approach where a base model is first lightly fine-tuned for persona, then used within a RAG system for factual grounding.
C. Use RAG exclusively, as prompt engineering alone can fully replicate a specific brand voice and persona.
D. Prioritize a RAG architecture to meet the critical requirements for factual accuracy and data traceability.
E. Recommend training a new LLM from scratch to ensure both brand voice and factual accuracy are built-in.

Answer: B,D

NEW QUESTION # 47
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 keeps the high-priority job in a pending state until the low-priority jobs complete naturally.
C. 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.
D. It sends an email notification to the administrator to manually reallocate the GPUs.

Answer: C

NEW QUESTION # 48
An architect is explaining different data storage paradigms to a new data engineering team. The team needs to understand where to store different types of data for their AI projects. Which statement accurately describes a key difference between a data lake and a data warehouse?

A. A data lake uses a schema-on-write approach, while a data warehouse uses a schema-on-read approach.
B. A data lake is only for on-premises deployments, while a data warehouse is only for cloud deployments.
C. A data lake stores structured, processed data for fast querying, while a data warehouse stores raw, unstructured data in its native format.
D. A data lake is designed to store vast quantities of raw, multi-format data (structured, semi- structured, and unstructured), while a data warehouse is typically used for storing structured and filtered data that has already been processed for a specific purpose.

Answer: D

NEW QUESTION # 49
A financial services company is required by regulators to be able to trace any version of their deployed fraud detection model back to the exact dataset and source code commit used to train it.
The current MLOps workflow is as follows:
Code_Repository: Git (commit hash: a1b2c3d4)
Dataset_Location: /vol/prod_data/fraud_dataset_v3
Storage_System: NetApp ONTAP 9
Model_Output: /vol/models/fraud_model_v3.2
Which NetApp technology should be used to create an immutable, point-in-time, and space- efficient copy of the dataset that can be linked to the specific code commit and model version?

A. NetApp Snapshots
B. NetApp FabricPool
C. NetApp FlexClone
D. NetApp SnapMirror

Answer: A

NEW QUESTION # 50
Which of the following are typical requirements for inferencing in AI models? (Choose two)

A. Loading the model into memory
B. Extensive training data
C. Cloud-based inference only
D. Pre-trained models

Answer: A,D

NEW QUESTION # 51
......

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