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[Hardware] To Get Brilliant Success Juniper JN0-253 Questions

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【Hardware】 To Get Brilliant Success Juniper JN0-253 Questions

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Juniper JN0-253 Exam Syllabus Topics:
TopicDetails
Topic 1
  • Juniper Mist Monitoring and Analytics: This domain focuses on monitoring tools including service-level expectations, packet captures, AI-driven insights, automated alerts, and audit logs for comprehensive network visibility.
Topic 2
  • Marvis Virtual Network Assistant AI: This domain introduces Marvis, an AI-powered assistant providing automated troubleshooting through intelligent actions, natural language queries, and specialized analytical tools for proactive issue resolution.
Topic 3
  • Location-based Services: This domain presents virtual Bluetooth Low Energy capabilities for asset tracking, visibility, and location-aware experiences that extend networking into physical space management.

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Juniper Mist AI, Associate (JNCIA-MistAI) Sample Questions (Q32-Q37):NEW QUESTION # 32
Which type of machine learning does Radio Resource Management (RRM) use?
  • A. Supervised learning
  • B. Reinforcement learning
  • C. Cognitive learning
  • D. Unsupervised learning
Answer: B
Explanation:
TheAI-driven Radio Resource Management (RRM)system inJuniper Mist Wireless Assuranceemploys reinforcement learningto continuously optimize wireless radio parameters such as channel selection, transmit power, and channel width.
According to theJuniper Mist Wireless Assurance and AI-Driven RRM Guide:
"The RRM system leverages reinforcement learning techniques to dynamically adjust radio configurations based on environmental conditions, user density, and interference patterns." Reinforcement learning enables the Mist AI system to make decisions by continuously evaluating the outcome of past configurations and improving future adjustments. This ensures that Mist RRM can autonomously optimize RF conditions for coverage and capacity without manual intervention.
Therefore, the correct answer isC. Reinforcement learning.
References:- Juniper Mist Wireless Assurance and AI-Driven RRM Guide- Juniper Mist AI and Machine Learning Architecture Documentation- Juniper Mist Cloud Operations and Optimization Overview

NEW QUESTION # 33
You are asked to create a real-time visualization dashboard which displays clients on a map.
Which two Juniper Mist functions would you use in this scenario? (Choose two.)
  • A. WebSocket
  • B. RESTful API
  • C. Live View
  • D. Webhooks
Answer: A,C
Explanation:
When developing areal-time visualization dashboardthat displays client locations on a map, Juniper Mist offers specific APIs and data streaming methods to support dynamic updates.
According to the Juniper Mist Developer Documentation, theWebSocketinterface enables continuous,real- time streaming of client location and telemetry datadirectly from the Mist Cloud. This mechanism is ideal for live dashboards, as it eliminates the need for repeated REST API polling. WebSocket connections provide instant updates whenever a device moves, connects, or disconnects, ensuring the displayed map remains accurate in real time.
TheLive Viewfeature complements this functionality within the Mist Cloud and third-party integrations. It allows administrators and developers toview live location movements of Wi-Fi clients, BLE beacons, and IoT deviceswithin a site's floor plan. It uses telemetry directly from access points, offering second-by-second updates.
In contrast,RESTful APIsandWebhooksare designed for event-based automation and configuration management rather than live visualization. REST APIs are best for historical or static data retrieval, while Webhooks are used for triggering external actions based on events.
Therefore, the correct functions for real-time map visualization are:
* WebSocket (C)- for continuous live data streaming
* Live View (D)- for direct map-based visualization of client activity
References:- Juniper Mist Developer API and WebSocket Guide- Juniper Mist Location Services and Live View Documentation- Juniper Mist Cloud Architecture Overview

NEW QUESTION # 34
Which language is used to execute Marvis queries in the Mist UI?
  • A. Natural Language
  • B. Structured Query Language (SQL)
  • C. C++
  • D. Python
Answer: A
Explanation:
Understanding Marvis Queries:
Marvis is the AI-driven virtual network assistant in Mist that helps with queries and troubleshooting.
Language Used for Queries:
Natural Language: Marvis is designed to understand and respond to natural language queries, making it user-friendly.
Python, C++, SQL: These are programming and query languages not used directly for Marvis queries.

NEW QUESTION # 35
In the Latest Updates section of Marvis Actions, an issue might be classified as one of which three states? (Choose three.)
  • A. Reoccurring Issue
  • B. Intermittent Issue
  • C. Resolved
  • D. AI Validated
  • E. Active
Answer: A,C,D

NEW QUESTION # 36
Which two statements describe SLEs? (Choose two.)
  • A. The metrics analyzed to meet specific SLE goals are categorized into classifiers.
  • B. SLEs use machine learning to provide a proactive approach to understanding the end-user experience.
  • C. SLEs use data science to troubleshoot network issues on an ad hoc basis.
  • D. SLEs display a detailed list of Wi-Fi clients who have connected to the network.
Answer: A,B
Explanation:
InJuniper Mist AI,Service Level Expectations (SLEs)form the foundation ofWireless AssuranceandWired Assurance. They provide adata-driven, proactive methodto measure and maintain the quality of user experience, going beyond traditional device-centric monitoring.
According to theJuniper Mist AI Operations GuideandWireless Assurance Documentation, SLEs:
"Leverage machine learning and data science to deliver a proactive understanding of end-user experience and identify root causes of performance issues." Each SLE (such asTime to Connect,Roaming,Throughput,Coverage, andCapacity) is composed of classifiers, which break down performance metrics into measurable, root-cause categories. Examples include DHCP,DNS,Authentication, andSignal Qualityclassifiers.
This classifier-based structure enables Mist AI to automatically correlate problems and highlight the most probable cause of degradation-eliminating the need for manual troubleshooting or reactive analysis.
OptionsBandDare incorrect because SLEs arenot ad hoc toolsfor manual troubleshooting and donot simply list connected clients. Instead, they provide intelligent, AI-driven insights into user experience across the network.
Therefore, the correct statements are:
* A. SLEs use machine learning to provide a proactive approach to understanding the end-user experience.
* C. The metrics analyzed to meet specific SLE goals are categorized into classifiers.
References:- Juniper Mist Wireless Assurance and SLE Overview- Juniper Mist AI Operations and Analytics Guide- Juniper Mist Cloud Monitoring and SLE Classifier Documentation

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