Practice NS0-901 Questions | NS0-901 Dumps Cost

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Network Appliance NS0-901 Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: AI Lifecycle27%- Generative AI Concepts
  • 1. Retrieval Augmented Generation (RAG)
  • 2. Hallucinations
  • 3. Fine-tuning
- 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)
- Model Development
  • 1. Fine-tuning workflows
  • 2. Model building
  • 3. Inferencing
- Data Preparation
  • 1. NetApp BlueXP Classification
  • 2. Data aggregation and cleansing
  • 3. XCP and CopySync
Topic 2: AI Common Challenges22%- Resource Management
  • 1. Sizing storage and compute resources effectively
  • 2. Controlling costs and securing storage
- Traceability and Optimization
  • 1. Optimizing data access and movement
  • 2. Ensuring traceability for code, data, and models
  • 3. Maximizing performance in demanding AI workloads
Topic 3: AI Overview15%- AI Deployment Models
  • 1. On-premises
  • 2. Cloud
  • 3. Edge
  • 4. Benefits and risks of each model
- Machine Learning Fundamentals
  • 1. Describe machine learning benefits
  • 2. Understand the relationship between AI, machine learning, and deep learning
- Training vs. Inferencing vs. Predictions
  • 1. Distinguish between training and inference workloads
- AI Industry Applications
  • 1. Digital twins
  • 2. Healthcare applications
  • 3. Agents
- 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
Topic 4: AI Hardware Architectures18%- 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 5: AI Software Architectures18%- Scaling and Orchestration
  • 1. Scaling AI workloads with Kubernetes
  • 2. Leveraging BlueXP software tools
- Development Tools
  • 1. Jupyter notebooks vs. pipelines
  • 2. NetApp DataOps Toolkit
- MLOps and LLMOps Ecosystems
  • 1. Understanding the software tools and platforms enabling AI at scale

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Network Appliance NetApp Certified AI Expert Exam Sample Questions (Q74-Q79):

NEW QUESTION # 74
A data scientist needs to test a new data normalization technique. To do this, they require an isolated, writable copy of a 50 TB curated simulation dataset that resides on the NetApp ASA system. The operation must be completed as quickly as possible and consume minimal additional storage space. Which NetApp technology is the most appropriate solution for this requirement?

Answer: A


NEW QUESTION # 75
An AI platform administrator is trying to deploy a new GenAI toolkit using the BlueXP Workload Factory. The deployment fails, and the administrator examines the API response from BlueXP.
{
"jobId": "we-deploy-genai-987zy",
"status": "FAILED",
"statusCode": 403,
"message":
"Forbidden: The service principal or user account used by the Connector does not have the required permissions on the target subscription to create a new resource group.
Required permission: 'Microsoft.Resources/subscriptions/resourcegroups/write'." } Based on this API response, what is the root cause of the deployment failure?

Answer: B


NEW QUESTION # 76
An AI architect is designing a solution for a legal firm. The primary goal is to allow lawyers to ask natural language questions about case law stored in a private, 50 TB document repository.
The key project constraints are as follows:
Project_Goal: Answer questions using proprietary, real-time legal documents.
Constraint_1: Must not alter the foundational LLM's weights due to compliance.
Constraint_2: Case law database is updated daily with new rulings.
Constraint_3: All generated answers must be traceable to a source document.
Which technology should the architect choose as the core of this solution?

Answer: A


NEW QUESTION # 77
A healthcare organization plans to use a large dataset of patient records to train a predictive model. Before training, they must identify and segregate all records containing Personally Identifiable Information (PII) to comply with privacy regulations. The data resides on an on- premises NetApp ONTAP cluster. The organization needs an automated tool to scan the data in- place and tag files containing PII without moving the data.
The project requirements are as follows:
Task: Identify PII in a large dataset.
Data_Location: On-premises ONTAP cluster.
Constraint: Data must not be moved from its source location for scanning.
Output: Tagged files containing PII.
Which NetApp tool is designed for this specific task?

Answer: B


NEW QUESTION # 78
A distributed training job running on the AIPod fails to start. The MLOps engineer inspects the events for one of the pending training pods and sees the following message:
Events:
Type Reason Age From Message
- - - -
Warning FailedScheduling 5m12s default-scheduler 0/4 nodes are available: 4 node(s) had no available volume zone.
The PersistentVolumeClaim (PVC) for this pod specifies a StorageClass that uses the 'ontap-nas' Trident provisioner.
he Trident logs show no errors.
What is the most likely cause of this scheduling failure?

Answer: D


NEW QUESTION # 79
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