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Network Appliance NS0-901 Exam Syllabus Topics:

Network Appliance NetApp Certified AI Expert Sample Questions:

1. A research institute is designing an infrastructure to support its entire AI drug discovery pipeline.
The pipeline has two distinct workload requirements:
1. Training: A team of data scientists needs to train several large transformer models simultaneously using a 500 TB dataset of genomic sequences. This process requires maximum data throughput to keep the GPUs saturated.
2. Inference: Once trained, the models are deployed to an internal web portal where researchers submit individual protein sequences for analysis. These queries must return results with the lowest possible latency.
Which infrastructure design best satisfies both requirements? (Choose 2.)

A) Configure QoS minimums on the training volumes to ensure they do not impact inference performance.
B) Implement NetApp FlexCache on smaller nodes at the network edge to serve the inference requests.
C) Use a single, large Cloud Volumes ONTAP instance in a public cloud to handle both workloads to simplify management.
D) Use NetApp StorageGRID as the primary storage for both the training and low-latency inference workloads.
E) Deploy a large NetApp ASA cluster with GPUDirect Storage enabled for the training environment.

2. The firm has acquired a competitor, and the volume of proprietary documents for the "Advisor Assistant" is expected to triple, exceeding the capacity of the current StorageGRID data lake. The architect needs to expand the data lake's capacity non-disruptively.
The current StorageGRID status is:
System_Health: Nominal
Node_Count: 6 (3 Storage Nodes, 3 Admin/Gateway Nodes)
Usable_Capacity: 1.5 PB
Used_Capacity: 1.4 PB (93%)
What is the standard procedure for scaling the capacity of the on-premises NetApp StorageGRID system?

A) Provision a new, separate StorageGRID system and use BlueXP copy and sync to move the data.
B) Replace the disks in the existing storage nodes with higher-capacity drives.
C) Use FabricPool to tier the excess data from StorageGRID to a public cloud provider.
D) Add new storage nodes to the existing StorageGRID system and execute an expansion procedure.

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 Autonomous Ransomware Protection (ARP).
B) Use BlueXP backup and recovery to back up the volumes to the cloud more frequently.
C) Enable Multi-Admin Verification (MAV) on all volumes.
D) Increase the frequency of scheduled Snapshots.

4. A data science team works primarily at a central data center but needs to run a short-term, burst- compute training job in the public cloud to take advantage of specialized GPUs that are not available on- premises. They need to efficiently and securely move a 20 TB dataset from their on- premises ONTAP cluster to a Cloud Volumes ONTAP instance for the duration of the job.
The data flow requirement is as follows:
Source: On-premises ONTAP cluster
Destination: Cloud Volumes ONTAP in AWS
Requirement: Efficient, secure, block-level data transfer.
Which NetApp technology is the most appropriate tool for this task?

A) NetApp SnapMirror
B) NetApp FlexCache
C) NetApp FabricPool
D) NetApp XCP

5. To comply with the security mandate, the architect must design a process to prevent client PII from the portfolio database from ever being included in the context sent to the LLM. Which two actions are required to build a robust and automated solution for this? (Choose 2.)

A) Fine-tune the LLM on a sanitized dataset to teach it to ignore PII.
B) Implement a data preparation step in the RAG pipeline that programmatically masks or excludes any data fields tagged as PII before the context is constructed.
C) Rely on a disclaimer in the chatbot UI telling advisors not to enter queries related to specific clients.
D) Use BlueXP classification to perform an initial scan of the portfolio database to identify and tag all columns containing PII.
E) Move the entire client portfolio database to an encrypted, air-gapped system.

Solutions: