Quick Answer
The correct answer is historical opportunity data and real-time activity data. Dynamics 365 Sales calculates a predictive opportunity score by combining these two distinct data types: historical opportunity data provides the baseline patterns—such as win/loss rates, deal size, and duration—that the machine learning model learns from, while real-time activity data captures current engagement signals like emails, meetings, and calls logged against the opportunity. On the MB-910 exam, this question tests your understanding of how the predictive score engine blends past trends with present interactions to forecast deal closure likelihood. A common trap is assuming only one data type is used, but the model requires both to function. To remember this, think of the score as a two-part recipe: the “history” of similar deals sets the recipe, and the “real-time” activity is the fresh ingredient that adjusts the flavor.
This MB-910 practice question tests your understanding of describe dynamics 365 sales. 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.
Which TWO data types are used by Dynamics 365 Sales to calculate a predictive score for an opportunity? (Choose two.)
Real-time activity data
Recent customer interactions influence score.
Product catalog details
Why wrong: Not used for scoring.
Social media sentiment
Why wrong: Not part of predictive scoring.
Competitor price lists
Why wrong: Not used.
Historical opportunity data
Past win/loss data is used.
Answer choices
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
Real-time activity data
Dynamics 365 Sales uses real-time activity data (e.g., emails, meetings, and calls logged against the opportunity) to capture current engagement signals, which are critical for calculating a predictive score. Historical opportunity data (e.g., win/loss rates, deal size, and duration) provides the baseline patterns that the machine learning model learns from to forecast the likelihood of closing a deal.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
For each option: why learners choose it and why it is or isn't the right answer here.
Real-time activity data
Why this is correct
Recent customer interactions influence score.
Related concept
Read the scenario before looking for a memorised answer.
Product catalog details
Why it's wrong here
Not used for scoring.
Social media sentiment
Why it's wrong here
Not part of predictive scoring.
Competitor price lists
Why it's wrong here
Not used.
Historical opportunity data
Why this is correct
Past win/loss data is used.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
The trap here is that candidates often confuse 'predictive scoring' with 'lead scoring' or assume external data (like social sentiment or competitor data) is used, when in fact Dynamics 365 Sales predictive scoring relies solely on internal historical and real-time activity data.
Detailed technical explanation
The predictive scoring model in Dynamics 365 Sales is built on Azure Machine Learning, which uses a logistic regression algorithm trained on historical opportunities (including outcome, duration, and interaction patterns) and real-time activity data captured via the timeline and activity logs. The model is tenant-specific and retrained periodically to adapt to changing sales patterns, but it never ingests external data like social feeds or competitor pricing. A subtle behavior: the model can be customized to include additional fields, but by default it only uses these two data types.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
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
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FAQ
Describe Dynamics 365 Sales — This question tests Describe Dynamics 365 Sales — Read the scenario before looking for a memorised answer..
The correct answer is: Real-time activity data — Dynamics 365 Sales uses real-time activity data (e.g., emails, meetings, and calls logged against the opportunity) to capture current engagement signals, which are critical for calculating a predictive score. Historical opportunity data (e.g., win/loss rates, deal size, and duration) provides the baseline patterns that the machine learning model learns from to forecast the likelihood of closing a deal.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Read the scenario before looking for a memorised answer.
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Last reviewed: Jun 24, 2026
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