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Network Appliance NS0-901 Exam Syllabus Topics:

SectionWeightObjectives
AI Software Architectures18%- 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. Scaling AI workloads with Kubernetes
  • 2. Leveraging BlueXP software tools
AI Lifecycle27%- Generative AI Concepts
  • 1. Fine-tuning
  • 2. Hallucinations
  • 3. Retrieval Augmented Generation (RAG)
- Predictive AI vs. Generative AI
  • 1. Impact of generative content (text, images, video, decision-making)
  • 2. Large Language Models (LLMs)
  • 3. Distinction between predictive and generative AI
- Data Preparation
  • 1. Data aggregation and cleansing
  • 2. NetApp BlueXP Classification
  • 3. XCP and CopySync
- Model Development
  • 1. Inferencing
  • 2. Model building
  • 3. Fine-tuning workflows
AI Overview15%- Machine Learning Fundamentals
  • 1. Understand the relationship between AI, machine learning, and deep learning
  • 2. Describe machine learning benefits
- AI Industry Applications
  • 1. Healthcare applications
  • 2. Agents
  • 3. Digital twins
- Training vs. Inferencing vs. Predictions
  • 1. Distinguish between training and inference workloads
- AI Deployment Models
  • 1. Benefits and risks of each model
  • 2. Edge
  • 3. On-premises
  • 4. Cloud
- Algorithm Types
  • 1. Supervised learning
  • 2. Reinforcement learning
  • 3. Unsupervised learning
- AI Convergence with HPC and Analytics
  • 1. Leveraging shared infrastructure for AI, HPC, and analytics
AI Hardware Architectures18%- Infrastructure Topologies
  • 1. Data aggregation and compute topologies
- NetApp Architectures
  • 1. SuperPOD
  • 2. OVX architectures
  • 3. BasePod
- Networking and Storage
  • 1. Network protocols for AI workloads
  • 2. Storage architectures for AI
AI Common Challenges22%- Traceability and Optimization
  • 1. Maximizing performance in demanding AI workloads
  • 2. Optimizing data access and movement
  • 3. Ensuring traceability for code, data, and models
- 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 (Q84-Q89):

NEW QUESTION # 84
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?

Answer: B


NEW QUESTION # 85
Which of the following describes the impact of generative AI in content creation?

Answer: C


NEW QUESTION # 86
An MLOps engineer is troubleshooting a failed Kubeflow pipeline step. The step was designed to create a clone of a dataset for a training job using the NetApp DataOps Toolkit. The pod logs for the failed pipeline step show the following:
Traceback (most recent call last):
File "create_clone.py", line 15, in <module>
clone_pvc(source_pvc_name="training-data-v2", new_pvc_name="train-job-34a-data") NameError: name 'clone_pvc' is not defined The engineer reviews the Python script for the pipeline step:
# create_clone.py
import os
from netapp_dataops.k8s import create_pvc
# Other code
print("Cloning source dataset for training run...")
clone_pvc(
source_pvc_name="training-data-v2",
new_pvc_name="train-job-34a-data"
)
print("Clone created successfully.")
What is the cause of the error?

Answer: D


NEW QUESTION # 87
A financial services company has deployed a real-time fraud detection model at the edge. The model is designed for low-latency inference. However, monitoring reports indicate that the infrastructure costs are excessively high, and GPU utilization is consistently low. The architect reviews the deployment configuration.
Instance_Type: NVIDIA DGX A100 (8 GPUs)
Storage_Tier: High-Performance All-Flash (NetApp ASA)
Network: 100GbE RoCE
GPU_Utilization_Avg: 5%
Monthly_Cost: $15,000
Workload_Profile: Low-volume, sporadic, real-time predictions
What is the most likely cause of the high costs and low utilization?

Answer: D


NEW QUESTION # 88
An AI architect needs to design a complete, end-to-end data pipeline for a new generative AI application at a financial services firm. The application will allow internal analysts to query a massive, 500 TB archive of historical market data and reports to generate summaries. The firm has the following environment and requirements:
Data_Sources: A mix of on-premises ONTAP filers and StorageGRID S3 buckets.
Requirement_1: All queries must be answered using only the private data archive.
Requirement_2: All generated summaries must provide citations to the source reports.
Requirement_3: All data containing client PII must be identified and excluded from the LLM context.
Requirement_4: The solution must be cost-effective for the large, mostly-read data archive.
Which set of actions and technologies constitutes the most robust and compliant solution?
(Select all that apply.)

Answer: C,D,E,F


NEW QUESTION # 89
......

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