NS0-901 Prüfungsaufgaben, NS0-901 PDF Demo

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Network Appliance NS0-901 Exam Syllabus Topics:

Section	Weight	Objectives
AI Lifecycle	27%	- Generative AI Concepts 1. Retrieval Augmented Generation (RAG) 2. Fine-tuning 3. Hallucinations - Data Preparation 1. XCP and CopySync 2. NetApp BlueXP Classification 3. Data aggregation and cleansing - Model Development 1. Inferencing 2. Model building 3. Fine-tuning workflows - Predictive AI vs. Generative AI 1. Large Language Models (LLMs) 2. Impact of generative content (text, images, video, decision-making) 3. Distinction between predictive and generative AI
AI Software Architectures	18%	- Scaling and Orchestration 1. Scaling AI workloads with Kubernetes 2. Leveraging BlueXP software tools - MLOps and LLMOps Ecosystems 1. Understanding the software tools and platforms enabling AI at scale - Development Tools 1. Jupyter notebooks vs. pipelines 2. NetApp DataOps Toolkit
AI Hardware Architectures	18%	- Infrastructure Topologies 1. Data aggregation and compute topologies - NetApp Architectures 1. OVX architectures 2. SuperPOD 3. BasePod - Networking and Storage 1. Storage architectures for AI 2. Network protocols for AI workloads
AI Overview	15%	- Machine Learning Fundamentals 1. Describe machine learning benefits 2. Understand the relationship between AI, machine learning, and deep learning - AI Deployment Models 1. Edge 2. Benefits and risks of each model 3. On-premises 4. Cloud - Algorithm Types 1. Unsupervised learning 2. Reinforcement learning 3. Supervised learning - AI Industry Applications 1. Agents 2. Healthcare applications 3. Digital twins - AI Convergence with HPC and Analytics 1. Leveraging shared infrastructure for AI, HPC, and analytics - Training vs. Inferencing vs. Predictions 1. Distinguish between training and inference workloads
AI Common Challenges	22%	- Resource Management 1. Controlling costs and securing storage 2. Sizing storage and compute resources effectively - Traceability and Optimization 1. Maximizing performance in demanding AI workloads 2. Ensuring traceability for code, data, and models 3. Optimizing data access and movement

>> NS0-901 Prüfungsaufgaben <<

Network Appliance NS0-901 PDF Demo, NS0-901 Antworten

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Network Appliance NetApp Certified AI Expert Exam NS0-901 Prüfungsfragen mit Lösungen (Q71-Q76):

71. Frage
The company's finance department mandates a cost-control strategy for the petabyte-scale StorageGRID data lake. The analysis shows that 80% of the simulation data is not accessed after
90 days but must be retained for five years for regulatory compliance. Which StorageGRID feature should the architect use to automatically reduce the long-term storage costs for this inactive data?

A. Configure an Information Lifecycle Management (ILM) policy that automatically moves objects older than 90 days to a lower-cost cloud archive tier, such as Amazon S3 Glacier Deep Archive.
B. Use NetApp SnapMirror to replicate the cold data to a different, lower-cost StorageGRID cluster.
C. Implement a FabricPool policy to tier the data to a NetApp ASA system.
D. Enable deduplication and compression on all StorageGRID storage nodes.

Antwort: A

72. Frage
The data anonymization job, running in a Kubernetes pod, fails. The pod logs show a "Permission Denied" error when trying to access the source volume on the on-premises ASA. An administrator checks the export policy rule for the volume.
The active rule is as follows:
Rule_Index: 1
Client_Match: 10.50.0.0/16
Protocols: nfs4
Read_Only_Access: sys
Read_Write_Access: -
Superuser_Access: none
The Kubernetes pod that failed has an IP address of '10.60.5.10'.
What is the cause of the "Permission Denied" error?

A. The 'Superuser_Access' setting is too restrictive.
B. The export policy only allows access via the NFSv4 protocol.
C. The pod's IP address ('10.60.5.10') is not within the allowed client match range ('10.50.0.0/16').
D. The export policy does not grant read-write access, which is required by the anonymization job.

Antwort: C

73. Frage
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 source PVC 'training-data-v2' does not exist.
C. The Kubernetes cluster is not running NetApp Trident.
D. The NetApp DataOps Toolkit is not installed in the container image used for this pipeline step.

Antwort: A

74. Frage
Which of the following best describes the difference between data lakes, data warehouses, and lakehouses?

A. Data lakes store data in cloud storage, data warehouses store it in traditional databases, and lakehouses store it in external drives.
B. Data lakes store metadata, data warehouses store transaction data, and lakehouses store archival data.
C. Data lakes store raw, unstructured data, data warehouses store structured data, and lakehouses combine the features of both.
D. Data lakes store structured data, data warehouses store unstructured data, and lakehouses store only real-time data.

Antwort: C

75. Frage
The firm's CFO is concerned about the rising costs of the on-premises AI infrastructure. A storage utilization report shows that of the 200 TB of data on the high-performance AFF A-Series, 150 TB consists of inactive, older versions of product documents that are rarely accessed but must be kept online for regulatory reasons.
The current storage landscape is:
- Performance Tier: NetApp AFF A-Series (200 TB used)
- Capacity Tier: NetApp StorageGRID (1.5 PB used)
What is the most effective and automated solution to reduce the storage cost of the performance tier without impacting data accessibility?

A. Purchase an additional, larger AFF A-Series system to gain better storage efficiency through deduplication.
B. Implement NetApp FabricPool to automatically tier the inactive data blocks from the AFF A-Series to the StorageGRID system.
C. Use NetApp SnapMirror to replicate the entire 200 TB volume to the StorageGRID system and then delete the source.
D. Manually identify and delete the 150 TB of inactive data from the AFF A-Series.

Antwort: B

76. Frage
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