Events and Insights JN0-452 Practice Questions (Page 2)

Questions 6–10

1. 6application · medium

   A network admin is curious about how Mist generates an insight that says 'The AP in the lobby has a coverage gap in the northeast corner.' The admin understands that Mist uses telemetry and machine learning. Which statement best describes how this insight is generated?

   • AMist analyzes client signal strength data collected from the AP and uses machine learning models to identify areas with consistently weak signals.
   • BMist relies on the administrator to manually define coverage areas and then monitors for violations.
   • CMist sends a test packet from the AP to a mobile device to measure signal strength at various locations.
   • DMist uses a pre-configured map of the building and calculates coverage based on the AP's transmit power.
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2. 7application · medium

   A network administrator notices that several clients in the marketing department are experiencing intermittent connectivity. While reviewing the Mist dashboard, the admin sees a series of events from the same access point: a 'client disconnect' event, followed by a 'radio channel change' event, and then a 'client reconnect' event, all within a 30-second window. The admin wants to understand the sequence and determine if the channel change caused the disconnects. What is the most efficient way to analyze this in the Mist portal?

   • ACheck the site-level dashboard for any alerts that might summarize the issue.
   • BUse the event search with filters for the specific AP and time range, then examine the events in chronological order to correlate the sequence.
   • CExport all events from the last hour to a CSV file and manually sort them in a spreadsheet.
   • DOpen the client's device details page and look at the client's event history only.
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4. 8expert · medium

   A network engineer is troubleshooting an issue where users in the east wing are experiencing intermittent connectivity. The engineer sees the following events in the Mist portal: a 'channel utilization' event on AP-East-1 showing high utilization, followed by a 'client disconnect' event for several clients, and then a 'roam' event for those clients to AP-East-2. The engineer wants to determine if the high utilization on AP-East-1 caused the disconnects. What is the best way to confirm this correlation?

   • AIgnore the high utilization event because it is a normal occurrence.
   • BAssume the high utilization caused the disconnects because it is the only event before them.
   • CLook at the client's perspective to see if they had low SNR before the disconnect.
   • DCheck if the high utilization event occurred before the disconnects and if the disconnects are clustered around that time.
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5. 9application · medium

   A network engineer is troubleshooting a user's complaint of slow Wi-Fi. The engineer sees the following events in the Mist portal for the user's device: a 'roam' event from AP-1 to AP-2, followed by a 'low SNR' event on AP-2, and then a 'throughput' event showing a significant drop. The engineer wants to determine the root cause of the slow performance. What is the most likely conclusion from correlating these events?

   • AThe user roamed to AP-2, which has a weak signal, causing the low SNR and subsequent throughput drop.
   • BThe user's authentication failed, causing the throughput drop.
   • CThe user's device is faulty and needs to be replaced.
   • DThe network is congested because too many users are on AP-2.
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6. 10expert · medium

   A network admin is explaining to a colleague how Mist generates insights. The admin wants to emphasize that insights are based on machine learning and telemetry. Which statement is the most accurate?

   • AMist generates insights based on predefined thresholds set by the administrator.
   • BMist generates insights by comparing current data to historical data from the same site.
   • CMist generates insights by analyzing packet captures from the network.
   • DMist uses telemetry data from APs and clients, such as signal strength and data rates, and applies machine learning models to detect patterns and anomalies.
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