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Network Appliance NS0-901 Exam Syllabus Topics:

Network Appliance NetApp Certified AI Expert Sample Questions:

1. What is the primary reason for implementing a hybrid cloud architecture in this medical imaging scenario?

A) To exclusively use cloud-native AI services, eliminating the need for any on-premises hardware.
B) To leverage the powerful, elastic GPU compute resources available in the public cloud for model training while keeping sensitive source data on-premises to meet security and HIPAA compliance requirements.
C) To reduce the overall cost of storage by keeping all data in the public cloud.
D) To simplify network management by using a single, global virtual network spanning the on- premises and cloud environments.

2. A healthcare organization plans to use a large dataset of patient records to train a predictive model. Before training, they must identify and segregate all records containing Personally Identifiable Information (PII) to comply with privacy regulations. The data resides on an on- premises NetApp ONTAP cluster. The organization needs an automated tool to scan the data in- place and tag files containing PII without moving the data.
The project requirements are as follows:
Task: Identify PII in a large dataset.
Data_Location: On-premises ONTAP cluster.
Constraint: Data must not be moved from its source location for scanning.
Output: Tagged files containing PII.
Which NetApp tool is designed for this specific task?

A) NetApp SnapMirror
B) NetApp BlueXP classification
C) NetApp FlexCache
D) NetApp XCP

3. The firm decides to implement a disaster recovery (DR) site for the "Advisor Assistant" application in a secondary data center. The Recovery Point Objective (RPO) is 15 minutes, and the Recovery Time Objective (RTO) is 4 hours. The design must protect both the document data lake and the vector database.
The primary site contains:
- Data Lake: NetApp StorageGRID
- Vector DB: NetApp AFF A-Series
Which combination of technologies and processes provides a complete and robust DR solution?
(Select all that apply.)

A) Use NetApp FlexCache at the DR site to cache data from the primary site.
B) Use NetApp SnapMirror to create an asynchronous replication relationship for the AFF A-Series volume containing the vector database, with a schedule of 10 minutes.
C) Use BlueXP disaster recovery to orchestrate and automate the failover and failback workflows for the application and its data dependencies.
D) Use the NetApp DataOps Toolkit to manually script the failover process.
E) Use StorageGRID's built-in replication rules to replicate object data from the primary site's grid to a StorageGRID instance at the DR site.
F) Rely on tape backups to be shipped to the DR site in the event of a disaster.

4. An organization wants to provide its data science team with a secure, on-demand method for using a powerful generative AI model with their private, sensitive corporate data. The solution must ensure that the private data is never exposed to the public internet or the public LLM API endpoint.
The architect is designing a solution using BlueXP.
Which two components are essential for building this secure solution? (Choose 2.)

A) A private vector database hosted on an on-premises NetApp ONTAP system to store embeddings of the sensitive corporate data.
B) The BlueXP GenAI Toolkit, which acts as a proxy to intercept user prompts and enrich them with data from a local vector database.
C) A direct VPN connection from each data scientist's laptop to the public LLM provider.
D) A BlueXP Connector deployed in a public subnet with a public IP address to allow access to the LLM.
E) A policy in BlueXP classification to copy all sensitive data to a public cloud bucket for easier access.

5. 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?

A) Every inference request requires a high-latency round trip over the WAN to fetch data from the core data center.
B) The edge server has insufficient CPU resources to run the model.
C) The on-premises ONTAP system is not configured for high-throughput.
D) The model is too large to fit into the edge server's memory.

Solutions: