New Relic APM Practitioner study plan

Map the APM 360 data model by diagramming how applications, transactions, segments, and service boundaries relate in a single traced request.

Instrument one local sample application with an agent and confirm it reports transaction and error data to a New Relic account.

Differentiate web transactions, non-web transactions, and background jobs by listing which metric names appear for each in the UI.

Configure one Apdex threshold and explain how satisfaction, toleration, and frustration scores are computed from response times.

Break down a slow transaction into its segment tree and identify the top three contributors by exclusive time.

Explain how distributed tracing propagates context across services and where trace IDs connect spans.

Interpret error analytics groupings and set up an error rate alert condition with a defined threshold.

Distinguish key transactions from regular transactions and justify when to mark one.

Correlate APM transaction data with infrastructure host metrics for the same time window and explain the join keys.

Write three NRQL queries against transaction and error event types, including one with FACET and one with a time-series aggregation.

Configure a dashboard combining APM golden signals with host CPU and memory charts.

Describe how log data links to APM entities through entity GUIDs and trace context.

Install and validate an infrastructure agent on a host and confirm the core sample event types are reporting.

Build a log parsing rule that extracts a custom attribute and verify it appears in queries.

Explain the difference between infrastructure events, log events, and metric timeslice data in terms of retention and query cost.

Set up one alert policy spanning an infrastructure condition and a log-based condition.

Instrument a browser application and verify page load timing, AJAX calls, and JavaScript errors appear in Browser monitoring.

Explain how the browser agent correlates a browser session to a backend transaction via distributed tracing headers.

Configure a synthetic monitor for a critical user journey and interpret its results and location-based failures.

Identify Core Web Vitals metrics and describe how each is surfaced in the digital experience data.

Analyze session replay and page view data to locate a user-facing performance regression and describe the evidence chain.

Set up a synthetic monitor alert and a browser error alert, distinguishing their trigger conditions.

Explain how applied intelligence features surface anomalies versus static threshold alerts, and when each is appropriate.

Describe how alert quality management reduces noisy conditions using incident and notification data.

Explain how the platform correlates related alerts into a single incident and what signals drive that grouping.

Configure anomaly detection on one metric and document the baseline behavior it learned.

Describe how AI-assisted root cause analysis uses entity relationships and telemetry to propose likely causes.

Evaluate one AI-generated insight for accuracy and note the supporting evidence you would require before acting.

Synthesize all four domains into a single end-to-end troubleshooting scenario from browser symptom to infrastructure cause.

Re-derive the Apdex formula, distributed trace propagation, and alert correlation logic from memory without notes.

Review every missed practice question and write a corrected explanation for each.

Build a one-page quick-reference sheet of NRQL patterns and key entity relationships for exam-day recall.