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Network Appliance NS0-901 Exam Syllabus Topics:
| Section | Weight | Objectives |
| NetApp AI Solutions and Architecture | 25% | - Scalability and performance optimization for AI
- Data management and data pipeline design
- ONTAP integration with AI frameworks
- Storage architectures for AI workloads
- NetApp AI-ready infrastructure components
|
| Security, Reliability, and Operations | 15% | - Cost management and efficiency
- Data security and access control for AI
- High availability and data protection
- Monitoring, logging, and troubleshooting AI environments
|
| Cloud and Hybrid Cloud AI Deployment | 18% | - Data mobility and consistency across environments
- Hybrid and multi-cloud AI architectures
- NetApp cloud data services for AI
- Cloud-native AI solutions and integration
|
| AI Lifecycle | 27% | - AI lifecycle stages: design, training, deployment, monitoring
- AI governance, ethics, and compliance
- Data preparation and management for AI
- Model training, inference, and optimization
- Predictive vs generative AI
|
| AI Overview | 15% | - Algorithm types: supervised, unsupervised, reinforcement learning
- AI industry use cases and applications
- AI, machine learning, and deep learning concepts
- Convergence of AI, high-performance computing, and analytics
- AI deployment models: on-premises, cloud, edge
|
Network Appliance NetApp Certified AI Expert Sample Questions:
1. Which of the following platforms can be used to manage containerized AI workloads on Kubernetes? (Choose two)
A) TensorFlow Extended
B) KubeFlow
C) RunAI
D) Google VertexAI
2. The architect needs to design an efficient data flow to move curated training sets from the central StorageGRID data lake to the high-performance NetApp ASA system used by the AI cluster. The process must be manageable from a single interface and should be automatable. Which two NetApp technologies should be used to implement this data pipeline stage? (Choose 2.)
A) A manual copy process using an S3 client on a bastion host.
B) NetApp FabricPool to automatically tier the data from StorageGRID to the ASA.
C) NetApp BlueXP copy and sync service to create and manage the data synchronization relationship between the S3 source and the NFS/CIFS destination.
D) A custom Python script using the NetApp DataOps Toolkit to trigger the data movement.
E) NetApp SnapMirror to replicate data from the StorageGRID object store to the ASA's file system.
3. 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) Enable Multi-Admin Verification (MAV) on all volumes.
B) Use BlueXP backup and recovery to back up the volumes to the cloud more frequently.
C) Enable Autonomous Ransomware Protection (ARP).
D) Increase the frequency of scheduled Snapshots.
4. Which of the following platforms provides tools for model training and deployment specifically for AI workloads?
A) Domino Data Labs
B) RunAI
C) Google VertexAI
D) All of the above
5. A data scientist is using the NetApp DataOps Toolkit for Python to automate the creation of a new, writable volume for an experiment. The script is intended to clone an existing dataset volume. When the script is executed, it fails with an error.
The relevant portion of the Python script is:
from netapp_dataops.k8s import clone_pvc
clone_pvc(
source_pvc_name="dataset-v1-pvc",
new_pvc_name="experiment-clone-pvc",
namespace="ds-team-1"
)
The script produces the following error in the terminal:
'Error: Failed to clone PVC. Source PVC 'dataset-v1-pvc' not found in namespace 'ds-team-1'.' What is the most likely cause of this error?
A) The NetApp DataOps Toolkit does not support cloning volumes.
B) The Python script is missing the necessary import statement for the toolkit.
C) The source PersistentVolumeClaim (PVC) named 'dataset-v1-pvc' does not exist or is in a different namespace.
D) The Kubernetes cluster does not have NetApp Trident installed.
Solutions:
Question # 1 Answer: B,C | Question # 2 Answer: C,D | Question # 3 Answer: C | Question # 4 Answer: D | Question # 5 Answer: C |