- A
The marketing team is manually removing customers from the segment based on campaign feedback
Why wrong: Manual removal would be visible in logs and is not automatic.
- B
The predictive model is failing to calculate scores for all customers, resulting in fewer qualified customers
Why wrong: A failing model would likely produce errors, not systematic decreases.
- C
The predictive model's propensity scores are being recalculated nightly, and some customers no longer meet the threshold due to recent data
Daily retraining can change scores, causing dynamic segment membership to fluctuate.
- D
The data source for transactions is being refreshed, causing some customer profiles to be temporarily unavailable
Why wrong: Profiles are not removed during refresh; they are updated.
MB-910 Describe Dynamics 365 Customer Insights Practice Question
This MB-910 practice question tests your understanding of describe dynamics 365 customer insights. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
You are a customer data analyst at a financial services company. The company uses Dynamics 365 Customer Insights to manage customer data. Recently, the marketing team created a segment called 'High Propensity to Buy' using a predictive model built in Customer Insights. The segment is used in a real-time marketing campaign in Dynamics 365 Marketing. The campaign has been running for a week, but the marketing team reports that the segment size seems to be decreasing each day, and they are concerned that customers are being incorrectly removed. Upon investigation, you find that the predictive model is retrained nightly based on the latest transaction data. What is the most likely cause of the shrinking segment?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue:
"most likely"Why it matters: Probability qualifier — the question wants the most probable cause or outcome, not a guaranteed one. Eliminate low-probability options.
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
The predictive model's propensity scores are being recalculated nightly, and some customers no longer meet the threshold due to recent data
The predictive model in Dynamics 365 Customer Insights is retrained nightly using the latest transaction data. This retraining recalculates propensity scores for all customers. As new transaction data is ingested, some customers' scores may drop below the threshold defined for the 'High Propensity to Buy' segment, causing them to be automatically removed from the segment. This daily recalibration is the most likely reason for the shrinking segment size.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
The marketing team is manually removing customers from the segment based on campaign feedback
Why it's wrong here
Manual removal would be visible in logs and is not automatic.
- ✗
The predictive model is failing to calculate scores for all customers, resulting in fewer qualified customers
Why it's wrong here
A failing model would likely produce errors, not systematic decreases.
- ✓
The predictive model's propensity scores are being recalculated nightly, and some customers no longer meet the threshold due to recent data
Why this is correct
Daily retraining can change scores, causing dynamic segment membership to fluctuate.
Clue confirmation
The clue word "most likely" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
The data source for transactions is being refreshed, causing some customer profiles to be temporarily unavailable
Why it's wrong here
Profiles are not removed during refresh; they are updated.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates may assume the predictive model is static or that segment membership is fixed once created, rather than understanding that nightly retraining dynamically recalculates scores and can shrink segments as customer behavior changes.
Detailed technical explanation
How to think about this question
Under the hood, Dynamics 365 Customer Insights uses Azure Machine Learning to train predictive models. When a model is retrained nightly, the propensity scores are recalculated based on the latest transactional data, and the segment membership is dynamically updated via the segment's SQL-like query that filters on the score threshold. This means customers whose recent behavior (e.g., reduced engagement or lower transaction value) lowers their score are automatically excluded, which is a designed behavior for real-time marketing segments. In a real-world scenario, this could happen if a customer's recent inactivity or a refund transaction reduces their predicted likelihood to buy.
KKey Concepts to Remember
- Read the scenario before looking for a memorised answer.
- Find the constraint that changes the correct option.
- Eliminate answers that are true in general but not in this case.
TExam Day Tips
- Watch for words such as best, first, most likely and least administrative effort.
- Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
How this comes up in practice
A cloud solutions architect for a retail company is evaluating services for a new workload. The correct answer here reflects best practice for the specific scenario described — not a general cloud recommendation. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Cloud exam questions reward reading the constraint carefully: the same technology can be right or wrong depending on the use case.
What to study next
Got this wrong? Here's your next step.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
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Describe Dynamics 365 Customer Insights — study guide chapter
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FAQ
Questions learners often ask
What does this MB-910 question test?
Describe Dynamics 365 Customer Insights — This question tests Describe Dynamics 365 Customer Insights — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: The predictive model's propensity scores are being recalculated nightly, and some customers no longer meet the threshold due to recent data — The predictive model in Dynamics 365 Customer Insights is retrained nightly using the latest transaction data. This retraining recalculates propensity scores for all customers. As new transaction data is ingested, some customers' scores may drop below the threshold defined for the 'High Propensity to Buy' segment, causing them to be automatically removed from the segment. This daily recalibration is the most likely reason for the shrinking segment size.
What should I do if I get this MB-910 question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Are there clue words in this question I should notice?
Yes — watch for: "most likely". Probability qualifier — the question wants the most probable cause or outcome, not a guaranteed one. Eliminate low-probability options.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
About these practice questions
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Last reviewed: Jun 11, 2026
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