Adobe Analytics Business Practitioner Expert study plan

Map the full data-collection lifecycle from page load through beacon transmission

Draft a report suite design covering traffic, conversion, and multi-suite tagging needs

Define a naming and ID convention for eVars, props, and events

Distinguish when to use a prop versus an eVar versus a success event for a given business question

Configure eVars and props with correct allocation and expiration settings for sample use cases

Build a data layer and tag-rule plan that populates every variable in the week-1 spec

Specify processing rules and VISTA-style logic alternatives to clean incoming data

Identify common data-collection failure points (missing events, truncated values, duplicate beacons)

Construct container-based segments using hit, visit, and visitor scope correctly

Combine sequential segments to answer a funnel-drop-off question

Build calculated metrics with correct formatting and metric references

Explain how virtual report suites inherit and override settings

Assemble a freeform table with breakdowns, filters, and correct attribution context

Choose the right visualization for a trend, comparison, and distribution question

Configure date comparisons and anomaly detection for a KPI panel

Publish a shared project with curated panels and a text summary

Write a business requirements document with measurable success criteria

Translate a stakeholder question into variables, events, and dimensions

Identify and document KPIs with owners and targets

Produce a solution design review checklist

Define marketing channel processing rules and test their order of precedence

Compare first-touch, last-touch, and linear attribution for one conversion path

Apply data governance practices for classifications, data retention, and access

Explain data repair and data feeds as governance tools

Set up users, groups, and product profiles with least-privilege permissions

Diagnose a data discrepancy using the debugger and report-suite logs

Explain report-suite settings that affect data (traffic variables, internal URL filters, time zone)

Document an escalation path for common data-quality incidents

Rehearse timed practice questions across all six domains

Revisit weak areas identified by practice scores

Summarize each domain in a one-page cheat sheet

Simulate a full exam under time constraints