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Network Appliance NS0-901 Exam Syllabus Topics:

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

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Network Appliance NetApp Certified AI Expert Exam Sample Questions (Q65-Q70):

NEW QUESTION # 65
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 BlueXP disaster recovery to orchestrate and automate the failover and failback workflows for the application and its data dependencies.
B. Use the NetApp DataOps Toolkit to manually script the failover process.
C. 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.
D. Rely on tape backups to be shipped to the DR site in the event of a disaster.
E. Use NetApp FlexCache at the DR site to cache data from the primary site.
F. 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.

Answer: A,C,F

NEW QUESTION # 66
A data scientist is working on a new model and needs a flexible environment for interactive data exploration, code development, and quick visualizations. A DevOps engineer is responsible for deploying the finalized model into a production pipeline that must run automatically every night without manual intervention.
Which tools are best suited for each of these roles?

A. The data scientist should use a Jupyter Notebook, and the DevOps engineer should use an automated production pipeline (e.g., Kubeflow Pipelines, Airflow).
B. Both the data scientist and the DevOps engineer should use automated production pipelines.
C. Both the data scientist and the DevOps engineer should use Jupyter Notebooks.
D. The data scientist should use a production pipeline, and the DevOps engineer should use a Jupyter Notebook.

Answer: A

NEW QUESTION # 67
A research lab uses a fleet of autonomous drones to collect high-resolution aerial imagery for agricultural analysis. The drones land at a remote edge location and offload their data. The AI models for image analysis are trained at a central data center. The team is using NetApp SnapMirror to replicate the data from the edge to the core. However, the data scientists are complaining that the datasets arriving at the data center are often incomplete or corrupted.
An administrator reviews the SnapMirror configuration and status via the BlueXP API:
{
"source": { "workingEnvironmentId": "OnPrem-Edge-Filer-1", "volumeName": "drone_data_raw" },
"destination": { "workingEnvironmentId": "Core-Datacenter-A800", "volumeName":
"drone_data_replicated" },
"mirrorState": "broken-off",
"relationshipStatus": "idle",
"unhealthyReason": "Transfer failed. Destination volume is out of space.", "lastTransferInfo": {
"transferError": "No space left on device"
}
}
What is the direct cause of the incomplete datasets at the data center?

A. The BlueXP Connector does not have the correct permissions to manage the SnapMirror relationship.
B. The source volume at the edge location has become corrupted.
C. The network connection between the edge and the core is unreliable.
D. The SnapMirror relationship is broken because the destination volume at the core data center has run out of capacity.

Answer: D

NEW QUESTION # 68
A data scientist is using the NetApp DataOps Toolkit for Python to automate the creation of a new, writable volume for an experiment. The script is intended to clone an existing dataset volume. When the script is executed, it fails with an error.
The relevant portion of the Python script is:
from netapp_dataops.k8s import clone_pvc
clone_pvc(
source_pvc_name="dataset-v1-pvc",
new_pvc_name="experiment-clone-pvc",
namespace="ds-team-1"
)
The script produces the following error in the terminal:
'Error: Failed to clone PVC. Source PVC 'dataset-v1-pvc' not found in namespace 'ds-team-1'.' What is the most likely cause of this error?

A. The Kubernetes cluster does not have NetApp Trident installed.
B. The source PersistentVolumeClaim (PVC) named 'dataset-v1-pvc' does not exist or is in a different namespace.
C. The NetApp DataOps Toolkit does not support cloning volumes.
D. The Python script is missing the necessary import statement for the toolkit.

Answer: B

NEW QUESTION # 69
In the data pipeline, which NetApp technology is used to create an efficient, block-level, incremental replication of the anonymized datasets from the on-premises ASA to the Cloud Volumes ONTAP instance in Azure?

A. NetApp SnapMirror
B. NetApp FabricPool
C. NetApp XCP
D. NetApp BlueXP copy and sync

Answer: A

NEW QUESTION # 70
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