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Network Appliance NS0-901 Exam Syllabus Topics:

Network Appliance NetApp Certified AI Expert Sample Questions:

1. 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) The BlueXP GenAI Toolkit, which acts as a proxy to intercept user prompts and enrich them with data from a local vector database.
B) A direct VPN connection from each data scientist's laptop to the public LLM provider.
C) A private vector database hosted on an on-premises NetApp ONTAP system to store embeddings of the sensitive corporate data.
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.

2. The AI training jobs on the AIPod are performing below expectations. The NVIDIA DGX servers' GPUs show low utilization. A performance analysis reveals that the bottleneck is not the storage system itself, but the network path between the storage and the compute nodes.
The current network configuration is as follows:
Network_Fabric: 100GbE Standard Ethernet
Protocol: NFS over TCP/IP
Data_Path: Storage -> Host CPU -> GPU Memory
Which network architecture enhancement would provide the most significant performance improvement by reducing latency and CPU overhead?

A) Upgrade the network switches to a model with a larger packet buffer.
B) Isolate the storage traffic on a separate VLAN from the management traffic.
C) Add more 100GbE network ports to the storage controllers.
D) Implement RDMA over Converged Ethernet (RoCE) and configure GPUDirect Storage.

3. An AI platform is suffering from poor performance during distributed training jobs. The training data resides on a single, large NFS volume. Monitoring shows that while the overall network throughput to the storage system is high, individual GPU nodes experience significant I/O wait times, and the single ONTAP volume is becoming a performance bottleneck. The goal is to re- architect the storage layout to maximize read parallelism and throughput for the training cluster.
Which two actions should the architect take to address this performance bottleneck? (Choose 2.)

A) Enable QoS maximums on the training volume to limit its IOPS.
B) Implement a NetApp FlexGroup volume to spread the dataset across multiple constituent volumes and aggregates.
C) Replace the NFS protocol with iSCSI for all training data access.
D) Increase the number of network ports connected to the storage controller.
E) Use NetApp FlexCache to create a local cache of the training data on each compute node.

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

5. An AI architect is designing a solution for a legal firm. The primary goal is to allow lawyers to ask natural language questions about case law stored in a private, 50 TB document repository.
The key project constraints are as follows:
Project_Goal: Answer questions using proprietary, real-time legal documents.
Constraint_1: Must not alter the foundational LLM's weights due to compliance.
Constraint_2: Case law database is updated daily with new rulings.
Constraint_3: All generated answers must be traceable to a source document.
Which technology should the architect choose as the core of this solution?

A) A new LLM trained from scratch on the legal documents.
B) A predictive AI model to classify legal documents.
C) A fine-tuning pipeline to update the LLM daily.
D) A Retrieval-Augmented Generation (RAG) architecture.

Solutions: