Amplitude Certification study plan
Amplitude Certification · Give each week a clear focus.
8 weeks · About 6 hours per week
Product Analytics
Define Amplitude's core event data model (events, properties, user properties) in your own words
Distinguish between event segmentation, funnel, and retention analyses and when to use each
Build one event segmentation chart from a sample dataset and annotate each configuration choice
Explain how Amplitude computes active users and unique user counts
Your checkpoint
A one-page annotated screenshot walkthrough of an event segmentation analysis explaining every setting
Product Analytics
Construct a multi-step funnel and identify the largest drop-off step
Differentiate retention, stickiness, and lifecycle analyses with a worked example of each
Describe how cohorts are defined and applied across chart types
Compute and interpret a basic metric (e.g., conversion rate) from a sample funnel
Your checkpoint
A funnel + retention analysis write-up identifying one drop-off and a proposed hypothesis
Data Governance and Integrations
Outline the Amplitude data taxonomy: naming conventions for events and properties
Explain the roles of the tracking plan and data governance features (e.g., blocking, validation)
Compare client-side vs server-side (HTTP API) ingestion and list tradeoffs
Identify common data-quality issues and how to detect them
Your checkpoint
A draft tracking plan for a hypothetical product covering 8-10 events with properties and types
Data Governance and Integrations
Describe the major integration categories (SDKs, warehouses, CDPs, cloud storage) and their purposes
Map a data flow from source to Amplitude for at least two integration paths
Explain identity resolution and how user IDs and device IDs merge
List governance controls that protect PII during ingestion
Your checkpoint
A data-flow diagram plus a one-page governance checklist for a new integration
Feature Experimentation and Feature Management
Define experimentation vocabulary: hypothesis, control, variant, primary metric, guardrail metric
Explain how Amplitude Experiment assigns users to variants and handles exposure events
Distinguish feature flags from experiments and describe flag targeting/rollout strategies
Identify common experimentation pitfalls (sample ratio mismatch, peeking)
Your checkpoint
An experiment design document with hypothesis, metrics, sample-size reasoning, and rollout plan
Feature Experimentation and Feature Management
Interpret experiment results including statistical significance and confidence intervals
Describe how to safely ramp a feature flag from 1% to full rollout
Explain how experiment exposure data flows into Amplitude analytics
Draft a decision rule for shipping vs. iterating vs. stopping an experiment
Your checkpoint
A results-interpretation memo for a mock experiment with a clear ship/no-ship recommendation
Session Replay, Heatmaps, Guides and Surveys
Explain how Session Replay captures sessions and what privacy controls (masking) apply
Describe what heatmaps reveal and how they complement funnel analysis
Outline how Guides and Surveys are targeted, triggered, and measured
Connect Session Replay findings back to a specific funnel drop-off
Your checkpoint
A replay-and-heatmap review that ties one observed behavior to a funnel metric and a survey question
Product Analytics & Data Governance and Integrations & Feature Experimentation and Feature Management & Session Replay, Heatmaps, Guides and Surveys
Summarize each domain's key concepts on a single consolidated cheat sheet
Complete a timed practice set across all four domains and log missed topics
Re-review the two weakest topics identified from practice results
Verify recall of governance and experimentation definitions without notes
Your checkpoint
A scored full-length practice attempt with a written remediation list for weak areas
Milestones
Week 2: Can explain and build segmentation, funnel, and retention analyses without notes
Week 4: Tracking plan and data-flow diagram complete; can describe identity resolution and PII controls
Week 6: Can design an experiment and interpret its results with a defensible decision rule
Week 8: Practice score logged and every missed topic has a remediation action