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Network Appliance NS0-901 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| AI Software Architectures | 18% | - MLOps and LLMOps Ecosystems
|
| AI Common Challenges | 22% | - Traceability and Optimization
|
| AI Overview | 15% | - AI Industry Applications
|
| AI Lifecycle | 27% | - Generative AI Concepts
|
| AI Hardware Architectures | 18% | - Infrastructure Topologies
|
Network Appliance NetApp Certified AI Expert Sample Questions:
1. 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 Multi-Admin Verification (MAV) on all volumes.
C) Enable Autonomous Ransomware Protection (ARP).
D) Increase the frequency of scheduled Snapshots.
2. The data anonymization job, running in a Kubernetes pod, fails. The pod logs show a "Permission Denied" error when trying to access the source volume on the on-premises ASA. An administrator checks the export policy rule for the volume.
The active rule is as follows:
Rule_Index: 1
Client_Match: 10.50.0.0/16
Protocols: nfs4
Read_Only_Access: sys
Read_Write_Access: -
Superuser_Access: none
The Kubernetes pod that failed has an IP address of '10.60.5.10'.
What is the cause of the "Permission Denied" error?
A) The pod's IP address ('10.60.5.10') is not within the allowed client match range ('10.50.0.0/16').
B) The export policy only allows access via the NFSv4 protocol.
C) The 'Superuser_Access' setting is too restrictive.
D) The export policy does not grant read-write access, which is required by the anonymization job.
3. A data scientist is working on a new model and needs a flexible environment for interactive data exploration, code development, and quick visualizations. A DevOps engineer is responsible for deploying the finalized model into a production pipeline that must run automatically every night without manual intervention.
Which tools are best suited for each of these roles?
A) Both the data scientist and the DevOps engineer should use Jupyter Notebooks.
B) The data scientist should use a Jupyter Notebook, and the DevOps engineer should use an automated production pipeline (e.g., Kubeflow Pipelines, Airflow).
C) The data scientist should use a production pipeline, and the DevOps engineer should use a Jupyter Notebook.
D) Both the data scientist and the DevOps engineer should use automated production pipelines.
4. Due to the success of the "Advisor Assistant," the number of concurrent users is expected to double in the next quarter. The existing Kubernetes cluster is running at 80% of its GPU capacity during peak hours. The architect must propose a plan to scale the compute infrastructure to handle the increased load.
Which two strategies represent the most effective and scalable solutions? (Choose 2.)
A) Replace all existing GPUs with the next-generation model to increase the performance of each node.
B) Configure the Kubernetes Cluster Autoscaler to automatically add new nodes to the cluster from a predefined node pool when GPU demand exceeds current capacity.
C) Vertically scale the existing nodes by adding more RAM and CPU cores to each one.
D) Manually deploy all new application replicas to a single, new, very large "super-node".
E) Horizontally scale the cluster by adding new GPU-equipped nodes.
5. An AI architect is designing a storage solution for a new training cluster. The primary workload consists of training large language models, which involves sequential reads of massive datasets.
The key requirement is to maximize GPU utilization by providing the highest possible data throughput. Cost is a secondary concern to performance.
Which NetApp storage system is the most appropriate choice for this workload?
A) NetApp C-Series
B) NetApp StorageGRID
C) NetApp E-Series
D) NetApp ASA (All-SAN Array)
Solutions:
| Question # 1 Answer: C | Question # 2 Answer: A | Question # 3 Answer: B | Question # 4 Answer: B,E | Question # 5 Answer: D |

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