MB-910 Describe Dynamics 365 Customer Insights Practice Question
Your Customer Insights instance has ingested data from a CRM and an e-commerce platform. After unification, some customers appear as duplicates. What is the most likely cause?
⚠ Common exam trap
Candidates often confuse 'matching rules' with 'data mapping' or 'confidence levels,' assuming duplicates come from unmapped fields or low confidence, rather than understanding that overly permissive matching criteria directly cause false positive merges.
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Matching rules are too loose
When matching rules are too loose, the system merges records that are not truly the same customer, creating duplicates after unification. In Customer Insights, matching rules define the conditions (e.g., exact match on email, fuzzy match on name) that determine whether records from different sources represent the same entity. Loose rules allow records with minor differences to be considered matches, leading to false positives and duplicate customer profiles.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Matching rules are too strict
Why it's wrong here
Strict rules cause missed matches, not duplicates.
- ✓
Matching rules are too loose
Why this is correct
Loose rules can merge unrelated records.
- ✗
Data sources have different field names
Why it's wrong here
Field mapping addresses this, not duplicates.
- ✗
Confidence level is set too low
Why it's wrong here
Low confidence prevents matches.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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