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Juniper Mist AI, Associate (JNCIA-MistAI) Sample Questions (Q43-Q48):

NEW QUESTION # 43
Exhibit:

Referring to the exhibit, which Roaming Classifier is responsible for the sub-threshold SLEs?

A. Ethernet
B. Capacity
C. Signal Quality
D. WiFi Interference

Answer: B

Explanation:
In the Juniper Mist dashboard,Service Level Expectations (SLEs)are metrics that measure user experience in key areas such as connection, throughput, and roaming. Each SLE is composed ofclassifiers, which help identify the underlying cause of degraded performance or sub-threshold scores.
According to the Juniper Mist AI documentation, theRoaming SLEtracks client transitions between access points and evaluates the quality of those roaming events. The contributing classifiers typically includeSignal Quality,Wi-Fi Interference,Ethernet, andCapacity.
In this exhibit, the bar forCapacityis the longest under the "Roaming Classifiers" section, indicating that it has the most significant impact on theSub-ThresholdSLE value (10.6%). This means roaming performance is primarily being limited by insufficient capacity - often due to AP radio congestion or a high number of concurrent clients impacting handoff efficiency.
Hence, theCapacityclassifier is responsible for the sub-threshold SLEs.
References:- Juniper Mist AI Service Level Expectations (SLE) Overview- Juniper Mist Dashboard Analytics and SLE Classifiers Guide- Juniper Mist Wi-Fi Assurance Documentation

NEW QUESTION # 44
What are two ways that Marvis Minis validates network configurations? (Choose two.)

A. Marvis Minis uses supervised machine learning.
B. Marvis Minis requires additional hardware for validation.
C. Marvis Minis uses unsupervised machine learning.
D. Marvis Minis validates configurations automatically.

Answer: A,D

Explanation:
Marvis Minis validates network configurations automatically and uses supervised machine learning. As stated in the Juniper Mist AI documentation, "Marvis Minis runs validations automatically at regular intervals and can be triggered manually by administrators. Minis use network digital twin technology and supervised machine learning to simulate and validate real-world user connectivity events." The system automatically creates patterns, triggers tests, and correlates real user and simulated network behaviors with the learned models. The validation covers critical network services-such as DHCP, DNS, ARP, and application reachability-and does so across various scopes (site, AP, VLAN, and switch). There is no requirement for additional hardware, as the digital twin reuses existing network infrastructure and existing APs.
Reference:Marvis Minis Documentation - Marvis Minis Overview; Marvis Minis Configuration and Operation Manual

NEW QUESTION # 45
What is used in Wireless Assurance to evaluate information about possible network faults?

A. syslog analysis
B. machine learning
C. SNMP
D. tcpdump

Answer: B

Explanation:
Machine Learning in Wireless Assurance: Mist's Wireless Assurance uses machine learning to analyze telemetry data and identify potential network issues. This approach allows for proactive identification and resolution of faults, improving overall network reliability and performance.

NEW QUESTION # 46
What does the Predictive Analytics and Correlation Engine (PACE) of Mist AI use to understand the end-user experience?

A. Network monitoring tools
B. User feedback
C. Machine learning
D. Manual analysis

Answer: C

Explanation:
ThePredictive Analytics and Correlation Engine (PACE)is the core AI engine that powers theJuniper Mist Cloud. It is designed to continuously learn from real-time telemetry and user interactions across wireless, wired, and WAN networks to proactively identify anomalies and predict performance issues.
According to theJuniper Mist AI Cloud and PACE Architecture Documentation, PACE:
"Leverages machine learning and advanced analytics to correlate data across millions of network sessions, automatically identifying patterns that affect user experience." The engine continuously evaluates key Service Level Expectations (SLEs), such asTime to Connect, Throughput, andRoaming, to establish performance baselines and detect deviations before users notice them.
PACE doesnot rely on manual analysisoruser feedback, and it integrates telemetry rather than external monitoring tools to maintain a closed-loop feedback system for self-correction and optimization.
Therefore, PACE usesmachine learning (A)to understand, predict, and improve end-user experience across Juniper Mist-managed networks.
References:- Juniper Mist AI Cloud Architecture and PACE Overview- Juniper Mist AI Operations and Analytics Guide- Juniper Mist Assurance and Machine Learning Documentation

NEW QUESTION # 47
A Mist administrator may link their Mist login to which type of social account login?

A. Facebook
B. TikTok
C. LinkedIn
D. Google

Answer: D

Explanation:
Google Account Integration: Mist administrators can link their Mist login to a Google account. This facilitates easier access and management of Mist services using a single sign-on (SSO) mechanism provided by Google.
Benefits of SSO: Using Google SSO simplifies user management, reduces password fatigue, and enhances security through Google's robust authentication mechanisms.

NEW QUESTION # 48
......

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JN0-253 Valid Dumps Sheet & Test JN0-253 Question

Best Professional Juniper JN0-253 Valid Dumps Sheet - JN0-253 Free Download

Juniper Mist AI, Associate (JNCIA-MistAI) Sample Questions (Q43-Q48):

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