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That mechanism explains why the alternative action—analyze application insights traces using kql filters for environment, app id, version and time—helps in another case. It does not justify the proposed fix here; custom feature telemetry records application-specific behavior that generic infrastructure events may not reveal. The distinguishing requirement is: A custom discount capability needs a measurable signal when users discover, activate and successfully use it.

That mechanism explains why the alternative action—use timing and performance telemetry to locate the measured slow operation before optimizing—helps in another case. It does not justify the proposed fix here; custom feature telemetry records application-specific behavior that generic infrastructure events may not reveal. The distinguishing requirement is: A custom discount capability needs a measurable signal when users discover, activate and successfully use it.

That mechanism explains why the alternative action—enable and verify environment-level telemetry to the intended application insights resource—helps in another case. It does not justify the proposed fix here; custom feature telemetry records application-specific behavior that generic infrastructure events may not reveal. The distinguishing requirement is: A custom discount capability needs a measurable signal when users discover, activate and successfully use it.

This would verify analyze application insights traces using kql filters for environment, app id, version and time, which addresses a different engineering need. It does not demonstrate that the current issue is resolved: A custom discount capability needs a measurable signal when users discover, activate and successfully use it. The decisive local proof is sample business actions produce identifiable feature events with useful non-sensitive dimensions.

This would verify use timing and performance telemetry to locate the measured slow operation before optimizing, which addresses a different engineering need. It does not demonstrate that the current issue is resolved: A custom discount capability needs a measurable signal when users discover, activate and successfully use it. The decisive local proof is sample business actions produce identifiable feature events with useful non-sensitive dimensions.

This would verify enable and verify environment-level telemetry to the intended application insights resource, which addresses a different engineering need. It does not demonstrate that the current issue is resolved: A custom discount capability needs a measurable signal when users discover, activate and successfully use it. The decisive local proof is sample business actions produce identifiable feature events with useful non-sensitive dimensions.

This correction is for the separate condition: A regional operations team needs to compare latency for two app versions using structured telemetry fields. It would not directly restore the intended behavior in this situation: A custom discount capability needs a measurable signal when users discover, activate and successfully use it. Here, instrument the missing business paths with appropriate feature telemetry and exclude confidential field values, because custom feature telemetry records application-specific behavior that generic infrastructure events may not reveal.

This correction is for the separate condition: A warehouse application sometimes times out and engineers guess which database operation is expensive. It would not directly restore the intended behavior in this situation: A custom discount capability needs a measurable signal when users discover, activate and successfully use it. Here, instrument the missing business paths with appropriate feature telemetry and exclude confidential field values, because custom feature telemetry records application-specific behavior that generic infrastructure events may not reveal.

This correction is for the separate condition: A support team wants error and latency signals from an on-premises Business Central instance into Azure Monitor. It would not directly restore the intended behavior in this situation: A custom discount capability needs a measurable signal when users discover, activate and successfully use it. Here, instrument the missing business paths with appropriate feature telemetry and exclude confidential field values, because custom feature telemetry records application-specific behavior that generic infrastructure events may not reveal.

This choice primarily fits a different requirement: A support engineer sees frequent exception events but cannot isolate whether they came from the latest extension deployment. There, structured telemetry dimensions let investigators narrow events to a deployment and correlate performance or errors. In the current situation, An invoice approval extension needs explicit business-level counters beyond generic page-open telemetry. Custom feature telemetry records application-specific behavior that generic infrastructure events may not reveal. Acceptance requires sample business actions produce identifiable feature events with useful non-sensitive dimensions.

This choice primarily fits a different requirement: A large extension's posting route is slow but the team optimizes UI rendering without evidence of the bottleneck. There, performance work should target observed long-running calls rather than speculative components. In the current situation, An invoice approval extension needs explicit business-level counters beyond generic page-open telemetry. Custom feature telemetry records application-specific behavior that generic infrastructure events may not reveal. Acceptance requires sample business actions produce identifiable feature events with useful non-sensitive dimensions.

This choice primarily fits a different requirement: An administrator needs diagnostic telemetry from a Business Central online production environment that currently has no Application Insights connection. There, environment-level telemetry sends service operation events to azure application insights for monitoring and troubleshooting. In the current situation, An invoice approval extension needs explicit business-level counters beyond generic page-open telemetry. Custom feature telemetry records application-specific behavior that generic infrastructure events may not reveal. Acceptance requires sample business actions produce identifiable feature events with useful non-sensitive dimensions.

That mechanism explains why the alternative action—analyze application insights traces using kql filters for environment, app id, version and time—helps in another case. It does not justify the proposed fix here; custom feature telemetry records application-specific behavior that generic infrastructure events may not reveal. The distinguishing requirement is: An invoice approval extension needs explicit business-level counters beyond generic page-open telemetry.

That mechanism explains why the alternative action—use timing and performance telemetry to locate the measured slow operation before optimizing—helps in another case. It does not justify the proposed fix here; custom feature telemetry records application-specific behavior that generic infrastructure events may not reveal. The distinguishing requirement is: An invoice approval extension needs explicit business-level counters beyond generic page-open telemetry.

That mechanism explains why the alternative action—enable and verify environment-level telemetry to the intended application insights resource—helps in another case. It does not justify the proposed fix here; custom feature telemetry records application-specific behavior that generic infrastructure events may not reveal. The distinguishing requirement is: An invoice approval extension needs explicit business-level counters beyond generic page-open telemetry.

This would verify analyze application insights traces using kql filters for environment, app id, version and time, which addresses a different engineering need. It does not demonstrate that the current issue is resolved: An invoice approval extension needs explicit business-level counters beyond generic page-open telemetry. The decisive local proof is sample business actions produce identifiable feature events with useful non-sensitive dimensions.

This would verify use timing and performance telemetry to locate the measured slow operation before optimizing, which addresses a different engineering need. It does not demonstrate that the current issue is resolved: An invoice approval extension needs explicit business-level counters beyond generic page-open telemetry. The decisive local proof is sample business actions produce identifiable feature events with useful non-sensitive dimensions.

This would verify enable and verify environment-level telemetry to the intended application insights resource, which addresses a different engineering need. It does not demonstrate that the current issue is resolved: An invoice approval extension needs explicit business-level counters beyond generic page-open telemetry. The decisive local proof is sample business actions produce identifiable feature events with useful non-sensitive dimensions.

This correction is for the separate condition: A regional operations team needs to compare latency for two app versions using structured telemetry fields. It would not directly restore the intended behavior in this situation: An invoice approval extension needs explicit business-level counters beyond generic page-open telemetry. Here, instrument the missing business paths with appropriate feature telemetry and exclude confidential field values, because custom feature telemetry records application-specific behavior that generic infrastructure events may not reveal.

This correction is for the separate condition: A warehouse application sometimes times out and engineers guess which database operation is expensive. It would not directly restore the intended behavior in this situation: An invoice approval extension needs explicit business-level counters beyond generic page-open telemetry. Here, instrument the missing business paths with appropriate feature telemetry and exclude confidential field values, because custom feature telemetry records application-specific behavior that generic infrastructure events may not reveal.

This correction is for the separate condition: A support team wants error and latency signals from an on-premises Business Central instance into Azure Monitor. It would not directly restore the intended behavior in this situation: An invoice approval extension needs explicit business-level counters beyond generic page-open telemetry. Here, instrument the missing business paths with appropriate feature telemetry and exclude confidential field values, because custom feature telemetry records application-specific behavior that generic infrastructure events may not reveal.

This choice primarily fits a different requirement: A large extension's posting route is slow but the team optimizes UI rendering without evidence of the bottleneck. There, performance work should target observed long-running calls rather than speculative components. In the current situation, A support engineer sees frequent exception events but cannot isolate whether they came from the latest extension deployment. Structured telemetry dimensions let investigators narrow events to a deployment and correlate performance or errors. Acceptance requires a filtered query returns the known failed operation in the correct release window without mixing other apps.

This choice primarily fits a different requirement: An administrator needs diagnostic telemetry from a Business Central online production environment that currently has no Application Insights connection. There, environment-level telemetry sends service operation events to azure application insights for monitoring and troubleshooting. In the current situation, A support engineer sees frequent exception events but cannot isolate whether they came from the latest extension deployment. Structured telemetry dimensions let investigators narrow events to a deployment and correlate performance or errors. Acceptance requires a filtered query returns the known failed operation in the correct release window without mixing other apps.

This choice primarily fits a different requirement: A marketplace app publisher wants platform-emitted runtime errors and app lifecycle telemetry from only its extension across customer tenants, without adding new feature-specific events. There, application-level telemetry can emit extension-related events to an app publisher's resource separately from general environment telemetry. In the current situation, A support engineer sees frequent exception events but cannot isolate whether they came from the latest extension deployment. Structured telemetry dimensions let investigators narrow events to a deployment and correlate performance or errors. Acceptance requires a filtered query returns the known failed operation in the correct release window without mixing other apps.

That mechanism explains why the alternative action—use timing and performance telemetry to locate the measured slow operation before optimizing—helps in another case. It does not justify the proposed fix here; structured telemetry dimensions let investigators narrow events to a deployment and correlate performance or errors. The distinguishing requirement is: A support engineer sees frequent exception events but cannot isolate whether they came from the latest extension deployment.

That mechanism explains why the alternative action—enable and verify environment-level telemetry to the intended application insights resource—helps in another case. It does not justify the proposed fix here; structured telemetry dimensions let investigators narrow events to a deployment and correlate performance or errors. The distinguishing requirement is: A support engineer sees frequent exception events but cannot isolate whether they came from the latest extension deployment.

That mechanism explains why the alternative action—configure supported app-level application insights telemetry for the extension—helps in another case. It does not justify the proposed fix here; structured telemetry dimensions let investigators narrow events to a deployment and correlate performance or errors. The distinguishing requirement is: A support engineer sees frequent exception events but cannot isolate whether they came from the latest extension deployment.

This would verify use timing and performance telemetry to locate the measured slow operation before optimizing, which addresses a different engineering need. It does not demonstrate that the current issue is resolved: A support engineer sees frequent exception events but cannot isolate whether they came from the latest extension deployment. The decisive local proof is a filtered query returns the known failed operation in the correct release window without mixing other apps.

This would verify enable and verify environment-level telemetry to the intended application insights resource, which addresses a different engineering need. It does not demonstrate that the current issue is resolved: A support engineer sees frequent exception events but cannot isolate whether they came from the latest extension deployment. The decisive local proof is a filtered query returns the known failed operation in the correct release window without mixing other apps.

This would verify configure supported app-level application insights telemetry for the extension, which addresses a different engineering need. It does not demonstrate that the current issue is resolved: A support engineer sees frequent exception events but cannot isolate whether they came from the latest extension deployment. The decisive local proof is a filtered query returns the known failed operation in the correct release window without mixing other apps.

This correction is for the separate condition: A warehouse application sometimes times out and engineers guess which database operation is expensive. It would not directly restore the intended behavior in this situation: A support engineer sees frequent exception events but cannot isolate whether they came from the latest extension deployment. Here, correct the kql dimensions and time window so relevant traces are included and unrelated ones excluded, because structured telemetry dimensions let investigators narrow events to a deployment and correlate performance or errors.

This correction is for the separate condition: A support team wants error and latency signals from an on-premises Business Central instance into Azure Monitor. It would not directly restore the intended behavior in this situation: A support engineer sees frequent exception events but cannot isolate whether they came from the latest extension deployment. Here, correct the kql dimensions and time window so relevant traces are included and unrelated ones excluded, because structured telemetry dimensions let investigators narrow events to a deployment and correlate performance or errors.

This correction is for the separate condition: A partner needs platform-generated diagnostics for only one extension across tenants, rather than new manual feature telemetry or whole-environment traces. It would not directly restore the intended behavior in this situation: A support engineer sees frequent exception events but cannot isolate whether they came from the latest extension deployment. Here, correct the kql dimensions and time window so relevant traces are included and unrelated ones excluded, because structured telemetry dimensions let investigators narrow events to a deployment and correlate performance or errors.