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Network Appliance NS0-901 Exam Syllabus Topics:

Network Appliance NetApp Certified AI Expert Sample Questions:

1. An MLOps engineer is troubleshooting a failed Kubeflow pipeline step. The step was designed to create a clone of a dataset for a training job using the NetApp DataOps Toolkit. The pod logs for the failed pipeline step show the following:
Traceback (most recent call last):
File "create_clone.py", line 15, in <module>
clone_pvc(source_pvc_name="training-data-v2", new_pvc_name="train-job-34a-data") NameError: name 'clone_pvc' is not defined The engineer reviews the Python script for the pipeline step:
# create_clone.py
import os
from netapp_dataops.k8s import create_pvc
# Other code
print("Cloning source dataset for training run...")
clone_pvc(
source_pvc_name="training-data-v2",
new_pvc_name="train-job-34a-data"
)
print("Clone created successfully.")
What is the cause of the error?

A) The Python script is attempting to use the 'clone_pvc' function, but it was not imported from the
'netapp_dataops.k8s' library.
B) The Kubernetes cluster is not running NetApp Trident.
C) The source PVC 'training-data-v2' does not exist.
D) The NetApp DataOps Toolkit is not installed in the container image used for this pipeline step.

2. 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 policy in BlueXP classification to copy all sensitive data to a public cloud bucket for easier access.
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 private vector database hosted on an on-premises NetApp ONTAP system to store embeddings of the sensitive corporate data.

3. Which of the following platforms provides tools for model training and deployment specifically for AI workloads?

A) Domino Data Labs
B) RunAI
C) All of the above
D) Google VertexAI

4. An AI team is planning two separate projects. The architect needs to provision the appropriate infrastructure for each.
| | Project A | Project B|
| -- | | - |
| Goal | Build a novel image recognition model from scratch.
| Adapt an existing, pre- trained LLM to understand company-specific jargon. |
| Input Data | 10 million new, unlabeled images. | A 50 GB text corpus of internal documents. |
| Required Compute | Very High (Weeks of multi-GPU training) | Moderate (Hours of single-GPU training) | Which two statements accurately describe the infrastructure requirements for these projects?
(Choose two.)

A) Project B is a fine-tuning task, which leverages an existing model and requires less data and compute resources than building a model from scratch.
B) Both projects should use NetApp StorageGRID as the primary storage for training to ensure low latency.
C) Project A is a model building task and requires a significantly larger and more powerful compute and storage infrastructure than Project B.
D) Project A requires only a large amount of data, while Project B requires only a large amount of code.
E) Both projects are examples of fine-tuning and have similar infrastructure needs.

5. An AI infrastructure architect is tasked with designing a solution to address two critical challenges in a large, multi-petabyte AI environment:
1. Cost: A significant portion of the data on the high-performance all-flash storage is inactive but must remain online. The cost of storing this cold data on the performance tier is prohibitive.
2. Traceability: Data scientists need a simple, space-efficient way to version their datasets at key points in their workflow to ensure reproducibility.
The environment consists of NetApp AFF A-Series and NetApp StorageGRID systems.
Which combination of NetApp technologies should the architect implement to solve both challenges simultaneously? (Select all that apply.)

A) Implement NetApp FlexClone to create full, writable copies of datasets for each experiment.
B) Train data scientists to use NetApp Snapshots to create point-in-time, read-only versions of their data volumes.
C) Use NetApp SnapMirror to replicate volumes from the AFF systems to StorageGRID for archival.
D) Implement NetApp FabricPool to automatically tier cold data blocks from the AFF systems to StorageGRID.
E) Use BlueXP backup and recovery to create backups on StorageGRID, then delete the original volumes from the AFF systems.
F) Use NetApp XCP to periodically move cold data from the AFF systems to StorageGRID.

Solutions: