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Network Appliance NS0-901 Exam Syllabus Topics:

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

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Network Appliance NetApp Certified AI Expert Exam Sample Questions (Q77-Q82):

NEW QUESTION # 77
A data scientist needs to launch a Jupyter notebook as a pod in a Kubernetes cluster. The pod requires a 50 Gi persistent volume for storing datasets and notebooks. The cluster administrator has configured a default Trident StorageClass for general-purpose use. The data scientist has the following PersistentVolumeClaim (PVC) manifest:
apiVersion: v1
kind: PersistentVolumeClaim
metadata:
name: jupyter-pvc
spec:
accessModes:
- ReadWriteOnce
resources:
requests:
storage: 50Gi
When this PVC is applied to the cluster, what will be the result?

A. Trident will create a 1 Gi volume, as this is the default size for all PVCs.
B. Trident will automatically provision a 50 Gi volume on its default backend and bind it to the PVC.
C. The PVC will remain in a "Pending" state until a PersistentVolume is manually created.
D. The PVC will fail because a 'storageClassName' is not explicitly defined.

Answer: B

NEW QUESTION # 78
What is the primary difference between C-Series and A-Series storage for AI workloads?

A. A-Series is for smaller-scale AI workloads, while C-Series supports large-scale, high-performance AI applications.
B. C-Series is designed for high-performance workloads, while A-Series focuses on cost-efficiency for general storage.
C. C-Series supports both file and object-based storage, while A-Series is optimized for object storage.
D. A-Series provides GPU-based acceleration, while C-Series only supports CPU-based workloads.

Answer: B

NEW QUESTION # 79
An organization recently suffered a ransomware attack that encrypted several volumes on their primary ONTAP storage system, including a critical volume containing curated training data. The security team needs to implement a solution that can proactively detect and block ransomware- like file I/O patterns and automatically create a secure Snapshot copy before any damage is done.
The current ONTAP configuration is as follows:
ONTAP_Version: 9.12.1
Security_Features: SnapLock (Compliance Mode) on archive volumes
Anti-Virus_Scan: Enabled (Vscan)
Ransomware_Detection: Not configured
Which ONTAP feature should be enabled to provide this proactive, automated protection?

A. Use BlueXP backup and recovery to back up the volumes to the cloud more frequently.
B. Enable Autonomous Ransomware Protection (ARP).
C. Enable Multi-Admin Verification (MAV) on all volumes.
D. Increase the frequency of scheduled Snapshots.

Answer: B

NEW QUESTION # 80
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 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.
B. A data lake is only for on-premises deployments, while a data warehouse is only for cloud deployments.
C. A data lake uses a schema-on-write approach, while a data warehouse uses a schema-on-read approach.
D. A data lake stores structured, processed data for fast querying, while a data warehouse stores raw, unstructured data in its native format.

Answer: A

NEW QUESTION # 81
An MLOps team uses a variety of platforms to manage their AI workloads. They need to understand the primary function of each tool within their ecosystem. Which statement best describes the role of an MLOps/LLMOps platform like Kubeflow or Run:AI?

A. They are networking protocols designed to accelerate data transfer between GPUs.
B. They are orchestration and management platforms that automate and streamline the entire AI/ML lifecycle, from data preparation and model training to deployment and monitoring.
C. They are integrated development environments (IDEs) used exclusively for writing Python code.
D. They are specialized storage systems designed to hold large datasets for training.

Answer: B

NEW QUESTION # 82
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