Evidence Before After

Zava Learning — Before / After Evidence

Prove impact and recovery with the right visual for the fault — not a chart by reflex. Resource Group: @@RG@@. Services: learner-portal, course-api, assessment-api. Retrieve zava-brand and zava-report-template with SearchMemory and apply the house style. Use the windows and root cause confirmed by rca-analysis.

Step 1 — Decide the visual FIRST (do not skip)

Classify what actually changed, then pick the visual that explains that. Plotting a smooth metric for a binary/config fault (e.g. "availability before/after" for an NSG block) is misleading and adds no insight — don't do it.

If a fault has both a binary cause and a metric symptom (common), lead with the diagram that explains the cause and use one metric chart only as supporting recovery proof.

Step 2 — Capture the before/after STATE (config & binary faults)

For connectivity/config/RBAC faults, the evidence is the changed configuration, not a metric:

• Read the relevant config with RunAzCliReadCommands (e.g. NSG effective rules, App Gateway backend health, Container Apps revision/ingress, role assignments) at the mitigated state, and reconstruct the pre-fix state from rca-analysis / change history.

• Build a state delta table: item · before · after · effect, e.g. NSG rule block-appgw (prio 100 DENY) · present · removed · AppGW→apps unblocked; AppGW backend health · Unhealthy · Healthy · probes pass; GET /api/quiz · 502 · 200.

• Render a before→after path diagram showing the broken hop and the fixed hop. Author it as ASCII (preferred for chat/PagerDuty notes) and/or a Mermaid graph; for a polished report image use ExecutePythonCode (graphviz/matplotlib). Example shape:

  BEFORE (blocked)                         AFTER (fixed)
  AppGW ──►  NSG subnet  ──► [apis]         AppGW ──►  NSG subnet  ──► [apis]
                │ DENY-100 ✗                              │ (rule removed) ✓
  backend: UNHEALTHY (502)                 backend: HEALTHY (200)
  

Step 3 — Pull metrics (performance/availability faults, or as recovery proof)

Only when a metric genuinely tells the story. Define three windows — Before (healthy baseline), During (detection→mitigation), After (post-fix, long enough to be credible) — using the exact windows from rca-analysis. Per affected service pull the metric matching the symptom:

• Availability / HTTP 5xx / success rate (QueryAppInsightsByResourceId).
• Latency — for the latency scenario, ms=<duration> from assessment-api console logs via QueryLogAnalyticsByResourceId; report p50/p95/p99.
• Replica count / restarts / throughput (ListAvailableMetrics, GetMetricTimeSeriesElementsForAzureResource).

Render brand-colored: before series in Critical red, after in Zava Teal, target/baseline as a dashed Slate line, every series labeled; annotate mitigation and fix points. PlotAreaChartWithCorrelation / PlotScatter for the series, PlotBarChart for before-vs-after bars. Use ExecutePythonCode (matplotlib) only for a comparison the Plot* tools can't express.

Rules

• One headline visual that explains the cause; supporting visuals only if the data backs them.
• Never plot a metric that doesn't change shape across the fault, or fabricate a trend to fill a slide.
• Same units, axes, and aggregation before vs. after — no misleading scales.
• Don't claim recovery a number doesn't support; if the after-window is too short, say so.

Verification

The visual matches the fault class: a before/after diagram + state delta table for config/binary faults, or a before/after chart + metric delta table for performance/availability faults — all from real config/telemetry over clearly stated windows, ready for rca-analysis and zava-reporting.