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Network Appliance NS0-901 Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: 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. Controlling costs and securing storage
  • 2. Sizing storage and compute resources effectively
Topic 2: AI Software Architectures18%- 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
- Development Tools
  • 1. NetApp DataOps Toolkit
  • 2. Jupyter notebooks vs. pipelines
Topic 3: AI Overview15%- AI Convergence with HPC and Analytics
  • 1. Leveraging shared infrastructure for AI, HPC, and analytics
- AI Deployment Models
  • 1. Edge
  • 2. On-premises
  • 3. Cloud
  • 4. Benefits and risks of each model
- Machine Learning Fundamentals
  • 1. Understand the relationship between AI, machine learning, and deep learning
  • 2. Describe machine learning benefits
- Training vs. Inferencing vs. Predictions
  • 1. Distinguish between training and inference workloads
- AI Industry Applications
  • 1. Digital twins
  • 2. Healthcare applications
  • 3. Agents
- Algorithm Types
  • 1. Unsupervised learning
  • 2. Reinforcement learning
  • 3. Supervised learning
Topic 4: AI Hardware Architectures18%- Networking and Storage
  • 1. Storage architectures for AI
  • 2. Network protocols for AI workloads
- NetApp Architectures
  • 1. SuperPOD
  • 2. BasePod
  • 3. OVX architectures
- Infrastructure Topologies
  • 1. Data aggregation and compute topologies
Topic 5: AI Lifecycle27%- Generative AI Concepts
  • 1. Fine-tuning
  • 2. Hallucinations
  • 3. Retrieval Augmented Generation (RAG)
- Predictive AI vs. Generative AI
  • 1. Distinction between predictive and generative AI
  • 2. Impact of generative content (text, images, video, decision-making)
  • 3. Large Language Models (LLMs)
- Model Development
  • 1. Model building
  • 2. Fine-tuning workflows
  • 3. Inferencing
- Data Preparation
  • 1. NetApp BlueXP Classification
  • 2. XCP and CopySync
  • 3. Data aggregation and cleansing

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Network Appliance NetApp Certified AI Expert Exam Sample Questions (Q94-Q99):

NEW QUESTION # 94
An AI architect is reviewing the design for a new data lake. The primary requirement is to store petabytes of unstructured data (images, video, sensor logs) in a highly durable, scalable, and cost- effective manner. The data will be accessed via S3 API by various data processing and analytics applications.
The initial design proposes using a traditional Network Attached Storage (NAS) filer with a large number of disks. The architect reviews the proposal:
Proposed_System: Traditional NAS Filer
Protocol: NFSv4
Scalability_Model: Scale-up
Metadata_Handling: Centralized in filer head
Cost_per_GB: Moderate
Why is this proposed system a poor choice for a petabyte-scale data lake?

Answer: A


NEW QUESTION # 95
A development team is building a generative AI application that must answer questions based on a constantly changing internal knowledge base of company documents. They want to provide the model with up-to-date information without altering its core weights and capabilities.
Which approach is most suitable for this requirement?

Answer: B


NEW QUESTION # 96
An online retail company's recommendation engine, which provides real-time product suggestions to users, is experiencing unacceptable latency. The inference application is running on a correctly-sized edge server, but user requests are taking over 500ms to process. An architect reviews the data access pattern and infrastructure diagram.
Application_Location: Edge Server (In-store)
Data_Source_Location: Core Data Center (On-premises ONTAP)
Data_Required_for_Inference: User profile data, product catalog vectors Network_Path: Edge -> WAN -> Core Data Center Observed_Latency: 550ms What is the most likely cause of the high inference latency?

Answer: B


NEW QUESTION # 97
A data scientist on the team wants to run an experiment on a new vector indexing strategy. To do this, they need a temporary, writable copy of the main vector database. They use the NetApp DataOps Toolkit for Python to automate this process.
The following script is executed:
from netapp_dataops.k8s import clone_pvc
clone_pvc(
source_pvc_name="prod-vector-db-pvc",
new_pvc_name="exp-indexing-clone",
namespace="ds-prod"
)
Assuming the source PVC exists and the user has correct permissions, what is the primary benefit of using this method?

Answer: D


NEW QUESTION # 98
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?

Answer: D


NEW QUESTION # 99
......

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