mParticle Practitioner study plan
mParticle Practitioner · Give each week a clear focus.
8 weeks · About 6 hours per week
Advanced Real-Time Identity
Map the full mParticle identity resolution pipeline from login ID to MPID, and diagram how identities are prioritized and merged at each stage
Compare the three identity strategy types (none, uniform, specific) and write down when each is appropriate for a web-to-app or app-to-web journey
Configure a test workspace with two identity types (email and customer ID) and observe how a single user resolves across web and mobile inputs
List the common identity fragmentation causes (anonymous sessions, pre-login events, ID changes) and describe the mitigation for each
Your checkpoint
A one-page identity resolution diagram plus a written table mapping each identity strategy type to its use case and failure mode
Advanced Real-Time Identity
Trace how identity changes propagate to downstream outputs, including forwarding rules, audience membership, and connected integrations
Explain the difference between identity sync, identity change, and identity alias events and when each is emitted
Build a test scenario where a user logs in after anonymous activity and verify how historical events are attributed
Document the implications of identity changes on data retention and on predictive model inputs
Your checkpoint
A test log documenting at least three identity scenarios with observed event attribution and downstream effects, annotated with the expected versus actual behavior
Hybrid Architecture
Compare the server-to-server, client-side SDK, and hybrid deployment models and identify the tradeoffs in latency, data completeness, and identity continuity
Diagram the role of the Events API, the Identity API, and the platform's event pipeline in a hybrid setup
Identify which events should fire client-side versus server-side for a typical e-commerce and a typical media app
List the data quality checks that catch duplicate or missing events in a hybrid deployment
Your checkpoint
A hybrid architecture decision matrix covering at least four deployment scenarios with recommended event routing for each
Audiences and Segmentation
Build an audience from real-time event criteria and verify membership updates within the expected processing window
Compare real-time, calculated, and predictive audience types and match each to a business use case
Configure audience criteria using logical operators and nested conditions, then test edge cases such as near-miss users
Explain how audience membership is evaluated against identity changes and how it flows to downstream integrations
Your checkpoint
A working set of three audiences (one real-time, one calculated, one rule-based with nested logic) with a test matrix showing which test users were included or excluded and why
Audiences and Segmentation & Predictions and Predictive Audiences
Define the inputs, training window, and output of a predictive model and describe how predictions differ from calculated attributes
Configure a predictive audience for churn or purchase propensity and set the audience threshold and refresh cadence
Explain how model scoring outputs are written back as user attributes and how those attributes can drive segmentation
Identify the minimum data volume and event history needed before a model produces useful results
Your checkpoint
A predictive audience specification document with model inputs, threshold rationale, refresh cadence, and a validation plan
Predictions and Predictive Audiences
Evaluate model quality using the available diagnostics and explain what a low-confidence or insufficient-data state means for activation
Design a holdout or control group strategy to measure the lift of a predictive audience against a baseline audience
Describe how prediction scores decay over time and how refresh cadence affects audience churn
Document the governance considerations for using predictive attributes in downstream systems
Your checkpoint
A model evaluation report with a sample scoring distribution, a chosen threshold, and a documented control-group measurement plan
Integrations and Activation
Map the event and audience output types (event forwarding, audience membership, user attributes) to the integration categories that consume them
Configure an audience connection to a downstream destination and verify membership sync behavior, including add and remove events
Explain the role of data filters, forwarding rules, and identity mapping in controlling what leaves the platform
Identify the failure modes of an integration (auth expiry, schema mismatch, rate limits) and the monitoring signals for each
Your checkpoint
An activation map listing at least five destinations, the output type sent to each, the identity key used, and the monitoring signal for failure
Hybrid Architecture & Audiences and Segmentation & Predictions and Predictive Audiences & Advanced Real-Time Identity & Integrations and Activation
Assemble an end-to-end scenario that spans identity resolution, audience creation, prediction, and activation in one flow
Audit the scenario for identity gaps, audience logic errors, and activation mismatches
Complete timed practice on the heaviest domains and review every missed item against source documentation
Write a personal weak-spot list with the specific configuration or concept to revisit before exam day
Your checkpoint
A full end-to-end solution walkthrough document plus a prioritized weak-spot remediation list with at least ten items
Milestones
Week 2: Can explain identity resolution end to end without notes, including how a post-login identity change affects prior anonymous events and downstream audience membership
Week 4: Has built and tested at least three audiences of different types and can predict inclusion or exclusion for a novel test user before running the test
Week 6: Can justify a predictive audience threshold using model diagnostics and describe how a control group would measure lift
Week 8: Completes a timed mixed-domain practice set with at least 80 percent accuracy and has no unresolved items on the weak-spot list