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Network Appliance NS0-901 Exam Syllabus Topics:

Section	Weight	Objectives
AI Overview	15%	- Algorithm types: supervised, unsupervised, reinforcement learning - AI industry use cases and applications - AI, machine learning, and deep learning concepts - Convergence of AI, high-performance computing, and analytics - AI deployment models: on-premises, cloud, edge
Security, Reliability, and Operations	15%	- Cost management and efficiency - Monitoring, logging, and troubleshooting AI environments - Data security and access control for AI - High availability and data protection
Cloud and Hybrid Cloud AI Deployment	18%	- Data mobility and consistency across environments - NetApp cloud data services for AI - Hybrid and multi-cloud AI architectures - Cloud-native AI solutions and integration
AI Lifecycle	27%	- AI lifecycle stages: design, training, deployment, monitoring - AI governance, ethics, and compliance - Predictive vs generative AI - Model training, inference, and optimization - Data preparation and management for AI
NetApp AI Solutions and Architecture	25%	- ONTAP integration with AI frameworks - Storage architectures for AI workloads - NetApp AI-ready infrastructure components - Data management and data pipeline design - Scalability and performance optimization for AI

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Network Appliance NetApp Certified AI Expert Exam Sample Questions (Q30-Q35):

NEW QUESTION # 30
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.

Answer: A

NEW QUESTION # 31
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 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.
B. Deploy a single, global NetApp StorageGRID across all three locations to act as a unified data plane.
C. 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.
D. Use Cloud Volumes ONTAP at the edge, NetApp StorageGRID at the core, and on-premises ONTAP for cloud archive.

Answer: A

NEW QUESTION # 32
Due to the success of the "Advisor Assistant," the number of concurrent users is expected to double in the next quarter. The existing Kubernetes cluster is running at 80% of its GPU capacity during peak hours. The architect must propose a plan to scale the compute infrastructure to handle the increased load.
Which two strategies represent the most effective and scalable solutions? (Choose 2.)

A. Vertically scale the existing nodes by adding more RAM and CPU cores to each one.
B. Horizontally scale the cluster by adding new GPU-equipped nodes.
C. Manually deploy all new application replicas to a single, new, very large "super-node".
D. Configure the Kubernetes Cluster Autoscaler to automatically add new nodes to the cluster from a predefined node pool when GPU demand exceeds current capacity.
E. Replace all existing GPUs with the next-generation model to increase the performance of each node.

Answer: B,D

NEW QUESTION # 33
The company's compliance officer mandates that all raw simulation data in the StorageGRID data lake must be protected from modification or deletion for a period of seven years. Which two StorageGRID features should be implemented to meet this strict, long-term data immutability requirement? (Choose 2.)

A. Encrypt all objects in the bucket using server-side encryption (SSE).
B. Configure an Information Lifecycle Management (ILM) rule that applies to the bucket and sets a retention period of seven years.
C. Create a new S3 bucket with Object Lock enabled in Compliance mode.
D. Use NetApp SnapMirror to replicate the data to a second StorageGRID system.
E. Enable Autonomous Ransomware Protection (ARP) on the StorageGRID system.

Answer: B,C

NEW QUESTION # 34
A financial services company has deployed a real-time fraud detection model at the edge. The model is designed for low-latency inference. However, monitoring reports indicate that the infrastructure costs are excessively high, and GPU utilization is consistently low. The architect reviews the deployment configuration.
Instance_Type: NVIDIA DGX A100 (8 GPUs)
Storage_Tier: High-Performance All-Flash (NetApp ASA)
Network: 100GbE RoCE
GPU_Utilization_Avg: 5%
Monthly_Cost: $15,000
Workload_Profile: Low-volume, sporadic, real-time predictions
What is the most likely cause of the high costs and low utilization?

A. The model was trained using supervised learning, which is inefficient for fraud detection.
B. The storage tier is too slow, causing the GPUs to wait for data.
C. The network latency is too high for an edge deployment.
D. The compute and storage infrastructure is sized for a large-scale training workload, not a lightweight inference workload.

Answer: D

NEW QUESTION # 35
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