MB-260 study plan

Map every Customer Insights - Data capability to its admin surface (environment setup, roles, data sources) so you can locate any setting in under a minute

Provision a trial environment and assign a custom role, then document which permissions each persona needs

Identify the supported ingestion methods (Power Query connectors, Azure Data Lake, APIs, Microsoft Dataverse) and record the file/format constraints for each

Ingest one sample customer table and one sample transaction table into the environment and confirm row counts match the source

Configure at least three different connectors and note the authentication and gateway requirements for each

Build a data source with multiple entities and define primary keys and semantic types for every field

Create a data ingestion schedule and verify that an incremental refresh picks up changed rows without duplicating them

Resolve two deliberately introduced data quality issues (duplicate keys, inconsistent date formats) using the built-in diagnostics

Explain how tables, entities, attributes, and relationships feed the unification process

Configure match rules using exact and fuzzy matching, then explain the effect of each threshold you set

Define deduplication rules and rank order so surviving fields come from the correct source

Run unification and inspect the resulting customer profiles, including the unified activity timeline

Diagnose and fix at least three unification errors surfaced in the results (missing keys, conflicting types, low match rates)

Create segments using the segment builder with multiple rule types, including a chained segment based on membership in another segment

Enrich profiles with at least one enrichment source and verify the new attributes appear on unified profiles

Compare segment member counts before and after an enrichment refresh to confirm the enrichment applied

Build a measure that aggregates transaction data and attach it to customer profiles

Set up a prediction or AI-driven insight and interpret the model results and confidence indicators

Design a segment that uses a measure value as a filter condition

Document how enrichment, measures, and segments interact when data refreshes

Configure an export destination and export a segment and its associated entity data

Create a scheduled export and confirm the delivery by inspecting the output

Describe how measures, predictions, and exports depend on refresh order and identify what breaks when upstream data changes

Tune a measure's aggregation logic to correct at least one inaccurate result

Re-run the full pipeline from ingestion to export in a clean environment without consulting notes

Time each stage and record where you lose the most time

Write a troubleshooting checklist covering the five most likely failure points across domains

Complete a timed practice set weighted toward Domains 3 and 4 and review every missed item

Review weak areas identified in Week 7 and re-do the associated hands-on tasks

Summarize each domain's key configuration decisions on a single reference card

Take a full-length timed practice exam and score above 80 percent

Restate the reasoning behind every missed question without looking at the answer