Adobe Customer Journey Analytics Developer Expert study plan

Adobe Customer Journey Analytics Developer Expert · Give each week a clear focus.

8 weeks · About 6 hours per week

Week 1

Data ingestion and connection configuration & Data modeling, identity, and stitching

Map the end-to-end ingestion path from source to dataset, labeling each handoff point where identity or schema can change
Configure a sandbox connection to a web SDK dataset and document every schema field you had to map or transform
Write out the difference between a person ID, a namespace, and a stitched ID in your own words and check it against the docs
List the five most common reasons a row drops out of a connection and rank them by how often you have seen them

A one-page ingestion-and-identity map with annotated field mappings for one real or sandbox dataset

Week 2

Build a connection that combines two datasets with different schemas and resolve at least one field-type conflict
Configure a rolling data window and predict, in writing, what happens to late-arriving records before you test it
Practice backfilling a historical dataset and verify row counts against the source
Draft a checklist for validating a new connection before anyone builds reports on it

A validated two-dataset connection plus a written pre-flight checklist with row-count reconciliation

Week 3

Compare stitching options for a cross-device scenario and justify a choice with a written tradeoff note
Configure a stitching field on a dataset and trace how it propagates into the data view
Explain the difference between a repeated person ID and a stitched identity to a hypothetical stakeholder
Identify three scenarios where stitching will produce misleading results and describe the guardrails

A stitching design note covering field choice, expected coverage, known limits, and one test result

Week 4

Create a data view with at least ten components and document the attribution and allocation settings for each
Build calculated metrics that depend on a stitched identity and confirm the numbers behave as expected
Test how changing a component's attribution model shifts a key metric and record the delta
Write a short rationale for every context-aware setting you enabled

A documented data view spec listing each component, its settings, and the observed metric delta when settings change

Week 5

Build a Workspace project that answers a specific business question using only your documented components
Create at least two derived fields and explain when each should be used instead of a calculated metric
Reproduce a metric from two different component paths and reconcile any gap
Practice freeform table troubleshooting: find and fix one broken or misleading visualization

A published Workspace project with a short methodology note and a reconciled metric comparison

Week 6

Design a derived data workflow that supports a use case your raw schema cannot answer directly
Document the latency and cost implications of your derived data design
Build an analysis that would fail without derived data and show the before/after
Write a rollback plan in case a derived data change produces wrong numbers in production

A derived data design doc with before/after analysis output and a rollback plan

Week 7

Configure data governance controls (labels, access, retention) on your sandbox and verify each takes effect
Walk through a deletion or privacy request end to end and document every system it touches
Build a troubleshooting runbook covering the five failures you are most likely to face
Diagnose one deliberately broken connection or data view and record the root cause

A governance-and-troubleshooting runbook with one verified privacy-request walkthrough and one diagnosed failure

Week 8

Re-run your week-1 through week-7 deliverables against a fresh sandbox and note anything that no longer works
Complete a timed full-length practice set and tag every miss by domain
Re-study your two weakest domains using only your own notes and docs
Write a one-page summary of the decisions you would make differently on a real implementation

A scored practice-set results sheet with domain breakdown and a one-page lessons-learned summary

Week 2: You can build a multi-dataset connection unaided and explain in writing why each dropped row dropped

Week 4: You can justify every data view setting and demonstrate the metric impact of changing attribution

Week 6: You can design and defend a derived data workflow with latency, cost, and rollback considered

Week 8: Practice-set score shows no domain below your target threshold, and every miss is tagged and reviewed