Network Appliance NS0-901 : NetApp Certified AI Expert Exam

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Network Appliance NetApp Certified AI Expert Sample Questions:

1. Which of the following techniques are used to maximize GPU utilization in AI workloads? (Choose two)

A) Using larger batch sizes
B) Scheduling GPU-intensive tasks during off-peak hours
C) Balancing compute and storage workloads efficiently
D) Utilizing CPU-based workloads for all tasks

2. An organization has a core data center with a large AI training cluster and several remote edge locations for data ingest and local inference. The edge locations frequently need access to the latest models trained in the core data center, but WAN bandwidth is limited and can be unreliable.
Users at the edge are reporting slow model loading times.
An architect reviews the data access logs from an edge site:
Timestamp: 2025-07-11T15:30:00Z
Event: Model_Load_Request
Model_Path: nfs://core-filer.example.com/vol/models/latest_model.pkl
Source_IP: 192.168.100.15 (Edge Server)
Destination_IP: 10.1.1.50 (Core Filer)
Status: SUCCESS
Duration: 3600s (60 minutes)
What is the most likely cause of the slow model loading times at the edge?

A) The NFS version used between the core and edge is outdated.
B) The core ONTAP filer is using slow, capacity-based disks.
C) The edge server does not have enough RAM to cache the model effectively.
D) The model file is being transferred over a slow, high-latency WAN link for every load request.

3. A national research laboratory is investing in a turnkey AI infrastructure solution. Their primary goal is to eliminate the complexity and risk of designing and integrating the compute, network, and storage components themselves. The solution must be pre-validated by the vendors to deliver predictable, linear performance as they scale from one to multiple compute nodes. Which two options represent this type of pre-validated, converged infrastructure solution for AI? (Choose two)

A) A Cisco and NetApp FlexPod for AI solution.
B) A custom-built server rack with components sourced from various different vendors.
C) A single NetApp FAS system connected directly to a single server.
D) NetApp AIPod, which integrates NVIDIA DGX servers with NetApp storage and networking.
E) A public cloud provider's general-purpose virtual machine instances.

4. An MLOps team uses a variety of platforms to manage their AI workloads. They need to understand the primary function of each tool within their ecosystem. Which statement best describes the role of an MLOps/LLMOps platform like Kubeflow or Run:AI?

A) They are specialized storage systems designed to hold large datasets for training.
B) They are integrated development environments (IDEs) used exclusively for writing Python code.
C) They are networking protocols designed to accelerate data transfer between GPUs.
D) They are orchestration and management platforms that automate and streamline the entire AI/ML lifecycle, from data preparation and model training to deployment and monitoring.

5. 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 policy in BlueXP classification to copy all sensitive data to a public cloud bucket for easier access.
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 direct VPN connection from each data scientist's laptop to the public LLM provider.

Solutions: