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Network Appliance NS0-901 Exam Syllabus Topics:

Section	Objectives
Topic 1: Industry Use Cases	- AI applications across industries 1. Autonomous systems and agents 2. Healthcare AI 3. Digital twins
Topic 2: AI Lifecycle and Deployment	- End-to-end AI lifecycle 1. Data ingestion and preparation 2. Model deployment and monitoring 3. Model training and validation - Operational challenges 1. Scalability and performance optimization 2. Data governance and management
Topic 3: AI Infrastructure and NetApp Solutions	- Converged workloads 1. Shared infrastructure design considerations 2. AI, HPC, and analytics convergence - AI-ready data infrastructure 1. Data pipeline optimization for AI/ML workloads 2. High-performance storage for AI workloads
Topic 4: AI and Machine Learning Fundamentals	- AI, ML, DL concepts 1. Differences between AI, machine learning, and deep learning 2. Training, inference, and prediction workflows - Algorithm types 1. Unsupervised learning 2. Reinforcement learning 3. Supervised learning

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Network Appliance NetApp Certified AI Expert Exam Sample Questions (Q11-Q16):

NEW QUESTION # 11
A university is building a shared AI research platform. They have two primary requirements:
1. Performance: A "hot" research area for active model training and development that requires the absolute lowest latency and highest throughput to support multiple, simultaneous GPU- intensive jobs.
The data in this area is around 50 TB.
2. Capacity & Cost: A "cold" data lake to store over 5 PB of raw, unstructured experimental data that is infrequently accessed but must be retained for compliance and future use. This tier must be as costeffective as possible.
Which combination of NetApp hardware and technologies should an architect select to build a complete, optimized, and cost-effective solution? (Select all that apply.)

A. Use NetApp E-Series systems for both the hot tier and the cold data lake to simplify management.
B. Use a NetApp All-SAN Array (ASA) system for the 50 TB high-performance "hot" research area.
C. Use NetApp StorageGRID to build the 5 PB cost-effective data lake.
D. Use a standard 10GbE network for all connectivity to reduce costs.
E. Implement NetApp FabricPool to automatically tier inactive data from the ASA system to the StorageGRID data lake.
F. Enable GPUDirect Storage on the ASA system to provide the lowest latency data path to the GPUs.

Answer: B,C,E,F

NEW QUESTION # 12
An architect is designing a global infrastructure for a company that develops AI for autonomous vehicles.
The design must accommodate three distinct locations and functions:
1. Edge (Test Tracks): Fleets of test cars generate 100s of TBs of sensor data per day. This data must be ingested locally with high performance.
2. Core (Primary Data Center): The raw data from all edge sites must be aggregated here. This location houses the primary data lake and the main GPU cluster for large-scale model training.
3. Cloud (Public Cloud Provider): Data scientists want to use cloud-native tools for experimental data processing and model development. They also need a cost-effective location for long-term archiving of raw data.
Which combination of deployment locations and NetApp technologies creates the most logical and efficient end-to-end solution?

A. Use Cloud Volumes ONTAP at the edge, NetApp StorageGRID at the core, and on-premises ONTAP for cloud archive.
B. Deploy a single, global NetApp StorageGRID across all three locations to act as a unified data plane.
C. Use NetApp ONTAP systems at the edge and core, and Cloud Volumes ONTAP in the public cloud.
Use SnapMirror to replicate data from edge to core, and FabricPool to tier data from the core to the cloud.
D. Use NetApp E-Series at the edge, NetApp ASA at the core, and NetApp StorageGRID in the cloud.
Use SnapMirror to move data between all three tiers.

Answer: C

NEW QUESTION # 13
An architect is designing a scalable, automated MLOps platform using Kubeflow on a Kubernetes cluster. The platform must support the entire AI lifecycle for multiple teams, with different storage requirements at each stage.
The key requirements are:
- Data Ingestion: A pipeline step needs a shared, read-write volume accessible by multiple pods to stage raw data.
- Experimentation: Data scientists need individual, isolated volumes for their Jupyter notebooks.
- Training: Distributed training jobs require a high-performance, parallel-access filesystem for reading training data.
- Automation: All storage must be provisioned automatically via Kubeflow pipeline definitions without manual intervention.
Which combination of technologies and configurations would create the most effective solution?

A. Configure multiple Trident backends (e.g., 'ontap-nas' for standard volumes, 'ontap-nas-flexgroup' for parallel access) and corresponding StorageClasses.
B. Create a single, large NFS volume and mount it to all pods using a static PersistentVolume.
C. Use the NetApp DataOps Toolkit for all storage provisioning, bypassing Trident and Kubernetes PVCs.
D. Rely on hostPath volumes for all storage to ensure the highest performance.
E. Use the NetApp DataOps Toolkit for Python within the Kubeflow pipeline components to dynamically create and manage Trident PVCs for each stage.

Answer: A,E

NEW QUESTION # 14
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 NetApp DataOps Toolkit is not installed in the container image used for this pipeline step.
B. The source PVC 'training-data-v2' does not exist.
C. The Kubernetes cluster is not running NetApp Trident.
D. The Python script is attempting to use the 'clone_pvc' function, but it was not imported from the
'netapp_dataops.k8s' library.

Answer: D

NEW QUESTION # 15
An architect is explaining different data storage paradigms to a new data engineering team. The team needs to understand where to store different types of data for their AI projects. Which statement accurately describes a key difference between a data lake and a data warehouse?

A. A data lake uses a schema-on-write approach, while a data warehouse uses a schema-on-read approach.
B. A data lake stores structured, processed data for fast querying, while a data warehouse stores raw, unstructured data in its native format.
C. A data lake is designed to store vast quantities of raw, multi-format data (structured, semi- structured, and unstructured), while a data warehouse is typically used for storing structured and filtered data that has already been processed for a specific purpose.
D. A data lake is only for on-premises deployments, while a data warehouse is only for cloud deployments.

Answer: C

NEW QUESTION # 16
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