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Question 101:
A global automotive services company uses Dynamics 365 Customer Insights to unify customer data from dealership visits, connected-vehicle telemetry, mobile maintenance app interactions, warranty service appointments, roadside assistance logs, loyalty program redemptions, and EV charging-station behavior. They want to compute a “Vehicle Care Engagement Score” based on recency of service visits, frequency of maintenance-app usage, number of completed recommended services, EV charging patterns, and consistency of connected-vehicle diagnostic responses. The score must refresh automatically and appear as a numeric attribute for segmentation and automated maintenance-journey personalization. Which Customer Insights capability should they use to compute this score?
A) Build a segment to classify engaged customers
B) Build a measure to calculate the engagement score
C) Use deterministic matching to combine diagnostic data
D) Import the score manually once per year
Answer: B
Explanation:
This automotive services company requires a dynamic scoring model that interprets multiple interaction types, updates automatically, and appears as a numeric attribute on unified customer profiles. The scenario describes behaviors across dealership visits, connected-vehicle telemetry, EV charging sessions, and mobile maintenance app usage. Customer Insights measures are specifically designed for this type of multi-factor scoring computation because they can aggregate diverse interactions, apply recency filters, and generate real-time numerical outputs.
Recency of service visits is one of the strongest engagement indicators. Customers who seek maintenance regularly tend to maintain healthier vehicles and interact consistently with the brand. Measures can incorporate recency and frequency simultaneously, weighting more recent visits more heavily.
Maintenance-app usage is another important behavior. The app may include features like scheduling service appointments, tracking oil life, viewing diagnostic codes, or receiving manufacturer alerts. Frequent interactions indicate strong digital engagement. Measures can count app behaviors, evaluate usage patterns, and roll these signals into the engagement score.
Completed recommended services represent long-term engagement. Customers who respond to recommended service notifications—such as tire rotations, battery tests, or brake inspections—demonstrate strong vehicle-care habits. Measures can aggregate completed recommendations across the last several months.
EV charging patterns provide another layer of insight. EV owners interact regularly with charging stations, and Customer Insights can unify charging-session logs to determine charging frequency, charging locations, and charging duration. Measures can incorporate these events into the engagement score.
Connected-vehicle diagnostic responses are a unique engagement dimension because modern vehicles constantly report diagnostic data. Customers who respond promptly to diagnostics or alerts—such as low tire pressure, engine alerts, or required recalls—show stronger engagement with ongoing vehicle health. Measures can also assess the stability and frequency of diagnostic events.
Option A is incorrect because segmentation does not compute numeric values. Segments can use the score after it exists but cannot calculate it.
Option C is incorrect because deterministic matching only merges duplicate customer or vehicle records. It cannot analyze telemetry, charging events, or digital behaviors.
Option D is incorrect because manually importing the score once per year contradicts the requirement for automatic and continuous score updates. Vehicle telemetry and service events change frequently, requiring constant recalculation.
Measures uniquely support numerical scoring across multiple data types and update automatically as new data streams arrive. For this reason, the correct answer is option B.
Question 102:
A multinational insurance provider uses Dynamics 365 Customer Insights to unify policyholder data from claim submissions, mobile insurance-app activity, call-center interactions, email inquiries, roadside assistance events, online policy-management behavior, and risk-assessment surveys. Their internal AI engine generates a monthly “Policyholder Retention Probability Score” showing the likelihood that each customer will renew their policy within the next 60 days. They need this score automatically imported every month and mapped to unified profiles to support segmentation, retention campaigns, and personalized renewal-journey triggers. Which Customer Insights integration method should they use?
A) Type the retention probability score manually
B) Import the dataset as an enrichment table
C) Use segmentation to estimate renewal probabilities
D) Use deterministic matching to generate the score
Answer: B
Explanation:
This scenario requires the insurance provider to bring an externally generated predictive score into Customer Insights. The AI system produces a monthly Policyholder Retention Probability Score, meaning the data must be imported regularly and mapped to unified policyholder profiles. Enrichment tables are the correct Customer Insights mechanism for such recurring data ingestion because they allow external datasets to be imported, matched, and refreshed automatically.
Policyholder behavior is complex. Claims history reveals how often and under what circumstances customers file claims. Mobile app usage shows how actively they engage with digital tools for submitting documents, reviewing coverage details, or checking claim statuses. Call-center interactions reflect support needs and potential dissatisfaction. Email inquiries show communication frequency and interest in policy adjustments. Roadside assistance events indicate usage of extended benefits. Online policy-management behavior, such as updating coverage or adding beneficiaries, demonstrates engagement with insurance products. Risk-assessment surveys provide self-reported insights into customer lifestyle and risk factors.
All of these indicators feed into the external AI model, which calculates a probability score predicting the likelihood of policy renewal. Because Customer Insights does not compute the probability natively, the system must ingest the model’s output. Enrichment tables allow the insurance provider to upload the external dataset, map customer identifiers, and automatically integrate the score into profile attributes.
Option A is incorrect because manually typing scores monthly is not feasible, especially for a large global insurance provider with millions of policyholders. Manual entry is slow, error-prone, and inconsistent.
Option C is incorrect because segmentation cannot create predictive values. Segments classify customers based on existing attributes, not calculate probabilities or generate machine-learning outputs.
Option D is incorrect because deterministic matching combines duplicate profiles across systems. It has no analytical capability and cannot produce probability scores.
Once imported through enrichment, the retention probability score becomes a powerful tool. Segments can identify high-risk policyholders needing early intervention or low-risk policyholders eligible for cross-sell opportunities. Personalized journeys can use the score to send tailored renewal reminders, special offers, or coverage recommendations.
Enrichment is the only Customer Insights capability that supports regular, automated ingestion of external predictive datasets. Therefore, the correct answer is option B.
Question 103:
A global nonprofit organization uses Dynamics 365 Customer Insights to unify donor data from fundraising events, online donation forms, volunteer logs, campaign email engagement, advocacy-platform actions, mobile-app activity, and social-impact tracking tools. They want to compute a “Donor Impact Participation Score” reflecting donation recency, volunteering frequency, advocacy-action engagement, event attendance, and consistency of digital-platform usage across 180 days. The score must update dynamically and appear as a numeric attribute to power segmentation and targeted impact-story journeys. Which Customer Insights capability should they use to compute this score?
A) Create a segment to group donors by impact
B) Build a measure to calculate impact participation
C) Use deterministic matching to identify engaged donors
D) Import the score manually during annual campaigns
Answer: B
Explanation:
This nonprofit organization requires a numerical score that represents donor impact participation over time. The inputs include donation history, volunteering logs, event attendance, advocacy actions, and mobile or web engagement. Measures are the correct Customer Insights capability for this scenario because they can aggregate multi-channel interactions, apply time-based filters, weight engagement factors, and compute numerical values that refresh continuously.
Donation recency is a strong indicator of engagement. Donors who contribute regularly show higher impact participation. Measures allow filtering and scoring donations within the last 180 days and scaling values based on frequency and recency.
Volunteer activity is another critical dimension. Volunteers who consistently donate time often show strong alignment with the organization’s mission. Measures can analyze the volume of volunteer activity, such as hours logged, events attended, or volunteer shifts completed.
Advocacy-action engagement refers to activities such as signing petitions, attending training webinars, sharing social-impact messages, or participating in community campaigns. Measures can count these interactions and interpret how committed individuals are to advocacy work.
Event attendance demonstrates support and involvement. Fundraising events, awareness gatherings, community service days, and educational programs all provide engagement signals. Measures can add weighted values based on participation levels.
Digital-platform usage—including mobile-app actions, email clicks, donation-form visits, and social-impact tracking tool interactions—reveals consistent involvement across channels. Measures can combine these behaviors to complete the scoring model.
Option A is incorrect because segments cannot perform calculations or create numerical scores. They only classify donors once the score exists.
Option C is incorrect because deterministic matching only merges duplicate donor records. It does not evaluate engagement behaviors or compute scoring metrics.
Option D is incorrect because manually importing scores annually contradicts the requirement for dynamic updates. Donor engagement changes frequently, especially during campaign seasons.
Measures enable real-time, multi-factor scoring and provide a numeric profile attribute that improves segmentation accuracy and personalization. The correct answer is B.
Question 104:
A global consumer electronics company uses Dynamics 365 Customer Insights to unify product-registration records, warranty-activation logs, mobile device-app interactions, technical-support requests, repair-center visits, subscription renewals, and product-usage telemetry. Their analytics team generates a quarterly “Product Reliability Risk Index” showing which customers are likely to require support or repairs in the next 90 days. They need the index imported automatically each quarter and mapped to unified profiles for segmentation and proactive support journeys. Which Customer Insights integration method should they use?
A) Enter the reliability index scores manually
B) Import the index as an enrichment table
C) Use segments to estimate reliability patterns
D) Use deterministic matching to calculate device reliability
Answer: B
Explanation:
This scenario requires importing a quarterly Product Reliability Risk Index calculated by an external analytics team. Because the index is produced outside Customer Insights and needs to be mapped to unified profiles on a recurring schedule, enrichment tables are the correct integration method.
Product-registration data indicates which devices customers currently own. Warranty-activation logs show when coverage begins and expires. Device-app interactions reflect digital engagement, including software updates, device configuration, and feature usage. Technical-support requests reveal usability or performance concerns. Repair-center visits show recurring device issues. Subscription renewals demonstrate long-term product engagement. Telemetry data helps monitor battery performance, hardware events, and error logs.
The external analytics team uses these datasets to calculate the Product Reliability Risk Index. Because Customer Insights does not natively compute risk indexes, it must ingest the externally produced dataset. Enrichment tables support scheduled ingestion, mapping, and refreshing of external data. This matches the organization’s requirement for quarterly updates.
Option A is not feasible because manually entering thousands or millions of index values is inefficient and error-prone.
Option C is incorrect because segmentation cannot compute risk indexes. It only groups customers based on existing values.
Option D is incorrect because deterministic matching only merges duplicate records. It does not calculate any form of risk or reliability scoring.
Once imported into Customer Insights, the reliability index becomes a powerful attribute. Segments can identify customers at higher risk for repairs or those needing proactive support outreach. Automated journeys can send maintenance tips, warranty reminders, or upgrade offers.
Enrichment tables are the only feature that meets all requirements. Therefore, option B is correct.
Question 105:
A multinational entertainment streaming company uses Dynamics 365 Customer Insights to unify subscriber data from streaming-session logs, mobile app interactions, device-login histories, genre preferences, watchlist activity, customer support chats, and subscription-billing events. They want to compute a “Content Engagement Consistency Score” based on viewing-session frequency, cross-device engagement, watchlist activity depth, subscription payment recency, and responsiveness to personalized content recommendations. The score must update automatically and fuel segmentation and dynamic content-curation journeys. Which Customer Insights capability should they use to compute this score?
A) Create a segment to classify content-engaged subscribers
B) Build a measure that calculates engagement consistency
C) Use deterministic matching to detect content patterns
D) Import the score manually every year
Answer: B
Explanation:
The streaming company wants a numerical score measuring content-engagement consistency over time. Because the score must interpret multi-source behaviors, update dynamically, and appear as a numeric attribute on unified profiles, a measure is the correct Customer Insights capability to use.
Viewing-session frequency is a core indicator of engagement. Subscribers who watch content frequently exhibit strong platform usage. Measures allow weighting sessions based on recency and viewing duration.
Cross-device engagement is another valuable signal. Users who stream across multiple devices such as TVs, mobile phones, tablets, or gaming consoles demonstrate deeper platform loyalty. Measures can integrate login histories and device usage to incorporate this behavior.
Watchlist activity depth reflects intent to consume content. Adding items to the watchlist, removing them, or reorganizing them indicates proactive engagement. Measures can aggregate watchlist interactions and assign corresponding weights.
Subscription payment recency contributes to engagement consistency. Payments made on time reflect stable usage. Late payments or interruptions signal risk. Measures can incorporate subscription-billing event patterns.
Responsiveness to personalized recommendations is also a strong indicator. Clicking recommended content, watching suggested playlists, or opening curated collections all demonstrate engagement with personalized discovery mechanisms. Measures can quantify these actions.
Option A is incorrect because segmentation cannot compute the score. It can only group subscribers after the score exists.
Option C is incorrect because deterministic matching merges duplicate identities. It has no ability to compute content-engagement metrics.
Option D is incorrect because manually importing the score annually does not meet the requirement for automatic recalculation. Streaming behavior changes weekly or even daily.
Measures support all required capabilities: numerical scoring, multi-channel aggregation, recency analysis, and automated updates. Therefore, option B is correct.
Question 106:
A multinational logistics and supply-chain provider uses Dynamics 365 Customer Insights to unify data from shipment tracking events, customer portal interactions, mobile driver app telemetry, delivery-exception logs, warehouse scanning operations, contract renewal behaviors, and customer service tickets. They want to compute a “Logistics Engagement Reliability Score” that evaluates shipment-tracking recency, portal-login frequency, response times to delivery exceptions, consistency of contract renewals, and volume of successful on-time deliveries over the last 160 days. The score must recalculate automatically and be available as a numeric profile attribute for segmentation and supply-chain optimization journeys. Which Customer Insights capability should the company use to compute this score?
A) Build a segment that filters customers by reliability
B) Build a measure that computes the logistics engagement reliability score
C) Use deterministic matching to identify frequent shippers
D) Import the score manually every fiscal year
Answer: B
Explanation:
This scenario clearly requires a dynamic, multi-source engagement score that reflects customer reliability within a logistics ecosystem. Customer Insights measures are designed specifically for computing numeric values based on aggregated interactions, behavioral data, recency analysis, and multi-factor scoring models. Because the organization needs the score to update automatically whenever new shipment or interaction data is ingested, a measure is the correct approach.
The score includes shipment-tracking recency. Customers who frequently track shipments demonstrate high engagement and operational dependency on the logistics system. Measures allow recency filters to assign higher weights to more recent interactions.
Portal-login frequency is another factor. When customers log into the logistics portal to review shipment statuses, update delivery instructions, or manage orders, these logs become measurable interactions. Measures can aggregate login frequency and incorporate login recency to reflect digital engagement.
Response time to delivery exceptions is essential in logistics operations. Delivery exceptions may include address verification failures, customs holds, product damage, or delays. Customers who respond quickly help operations recover efficiently. Measures can evaluate how frequently customers respond within acceptable time frames.
Contract renewal consistency indicates long-term engagement. Customers who renew logistics contracts reliably often maintain stable shipping volumes and long-term operational alignment. Measures can include counts of renewals, renewal recency, and patterns of multiyear extensions.
On-time delivery volume is another component. While on-time delivery is often attributed to the logistics provider, it indirectly reflects customer reliability when evaluating the stability of demand and operational planning. Measures can calculate counts of successful deliveries linked to each customer.
Option A is incorrect because segmentation cannot compute numeric values. Segments can use the reliability score after it exists, but they cannot calculate it.
Option C is incorrect because deterministic matching only merges duplicate customer profiles. It does not evaluate interactions, shipment logs, contract renewals, or delivery behavior.
Option D is incorrect because manually importing the score annually contradicts the requirement for continuous automated recalculation. Logistics environments generate new events daily, and scores must reflect current behaviors.
Measures provide recency analysis, numerical scoring, aggregation functions, and continuous updates. They allow the logistics company to compute the detailed reliability model described. Thus, the correct answer is B.
Question 107:
A major telecommunications provider uses Dynamics 365 Customer Insights to unify subscriber data from call-detail records, broadband modem telemetry, customer support interactions, payment histories, mobile app usage, upgrade requests, and churn-risk surveys. Their in-house data-science platform generates a monthly “Subscriber Lifetime Value Growth Probability Score,” predicting which subscribers are likely to increase their long-term value through service upgrades or additional product adoption. They want this score imported automatically every month and mapped to unified subscriber profiles to drive segmentation, retention strategies, and personalized offer journeys. Which Customer Insights integration method should they use?
A) Enter the growth-probability scores manually
B) Import the predictive dataset as an enrichment table
C) Create segments to approximate probability values
D) Use deterministic matching to calculate lifetime value
Answer: B
Explanation:
This telecommunications provider relies on a monthly predictive model built outside Customer Insights to estimate lifetime-value growth probability. Because the score is externally generated, must be refreshed monthly, and needs to be mapped to unified subscriber profiles, enrichment tables are the correct Customer Insights mechanism.
Telecommunications data is extremely diverse. Call-detail records reflect voice-usage patterns, international calling habits, and peak usage times. Broadband modem telemetry shows service quality, outages, and connectivity patterns that can influence subscriber satisfaction. Customer support interactions, such as ticket volumes and resolution times, are critical for understanding subscriber sentiment. Payment histories reveal whether subscribers pay on time or show early signs of churn-risk behaviors. Mobile app usage patterns reflect digital engagement with account management, billing, and self-service troubleshooting tools. Upgrade requests show subscriber interest in higher-tier plans or additional services. Churn-risk survey responses provide attitudinal insights.
An external model may analyze all these factors to generate a Probability Score predicting lifetime-value growth. Because this metric does not originate from Customer Insights, the system must ingest the score. Enrichment tables allow the organization to import external datasets such as predictive outputs and match them with unified profiles based on subscriber identifiers.
Option A is incorrect because manually entering scores every month is operationally impossible for large telecom providers with millions of subscribers.
Option C is incorrect because segmentation cannot calculate predictions. Segments group subscribers based on attributes, but cannot produce probabilistic outputs derived from complex analytics.
Option D is incorrect because deterministic matching is used only to merge duplicate records—not to calculate lifetime-value predictions.
Once imported through enrichment, the score becomes a powerful profile attribute. Segmentation can identify high-probability customers for upsell opportunities or low-probability customers who need retention-focused interventions. Personalized journeys can deliver offers, recommend bundles, or trigger targeted communications.
Enrichment tables are the only Customer Insights feature supporting scheduled ingestion of external predictive data, making option B correct.
Question 108:
A global pharmaceuticals distributor uses Dynamics 365 Customer Insights to unify data from cold-chain IoT sensors, wholesaler order histories, shipment-temperature deviation logs, compliance-audit outcomes, distributor support tickets, product-return reasons, delivery-exception cases, and online portal behavior. They want to calculate a “Cold-Chain Compliance Engagement Score” that combines recency of compliant deliveries, frequency of temperature-safe shipments, responsiveness to compliance alerts, consistency of portal usage for tracking, and historical deviation-resolution patterns over the last 200 days. The score must refresh automatically and be stored as a numeric profile attribute for segmentation and proactive compliance-intervention journeys. Which Customer Insights capability should the organization use to calculate this score?
A) Build a segment grouping distributors by compliance
B) Build a measure to compute the compliance engagement score
C) Use deterministic matching to identify compliant distributors
D) Import the score manually after annual audits
Answer: B
Explanation:
A pharmaceuticals distribution network must manage an extremely sensitive supply chain, especially for products requiring cold-chain management. These products include vaccines, specialty medications, biologics, and temperature-sensitive injectables. Ensuring that every shipment maintains strict temperature conditions is essential for safety and regulatory compliance. In this scenario, the organization wants a dynamic Cold-Chain Compliance Engagement Score that evaluates ongoing distributor behavior across multiple touchpoints. The score must update automatically as new IoT, shipment, compliance, or portal-usage data enters Customer Insights. Because the score is multi-factor, numeric, time-sensitive, and needs continuous recalculation, a measure is the correct Customer Insights capability.
Temperature-safe shipments are a crucial component of cold-chain reliability. Cold-chain IoT sensors track temperature throughout transit. Measures can evaluate the frequency of shipments that remain within required temperature thresholds and weight these outcomes based on recency.
Compliance-alert responsiveness reflects how quickly distributors react when temperature anomalies or sensor deviations occur. Distributors who respond promptly help prevent product spoilage or safety risks. Measures can calculate responsiveness by comparing timestamps of alerts and distributor interactions.
Historical deviation-resolution patterns show whether distributors consistently resolve issues, escalate problems, or repeatedly require reminders. Measures can aggregate deviation logs over 200 days and assign weighted values.
Online portal behavior is another meaningful indicator. Distributors who regularly access the tracking portal demonstrate proactive compliance monitoring. Measures can track login frequency and recency.
Support tickets and compliance audits represent additional engagement signals. Measures can interpret audit outcomes and incorporate the patterns into the overall score.
Option A is incorrect because segments cannot compute numeric values. They can only categorize distributors after the score already exists.
Option C is incorrect because deterministic matching only merges duplicates. It does not interpret compliance behavior or compute scores.
Option D is incorrect because manually importing scores after annual audits contradicts the requirement for continuous, automated calculation. Cold-chain deviations occur in real time; relying on annual imports would allow major risks to go unnoticed.
Therefore, a measure is the correct solution, making option B the right answer.
Question 109:
A global retail banking corporation uses Dynamics 365 Customer Insights to unify customer data from credit-card transactions, loan-application systems, mobile-banking app interactions, ATM usage logs, fraud-alert histories, financial-advice session notes, and customer-service inquiries. Their internal AI platform produces a monthly “Customer Financial Health Stability Probability Score,” predicting the likelihood that each customer will maintain stable financial patterns over the next 90 days. They want this score automatically ingested each month and mapped to unified profiles for segmentation and targeted financial-wellness journeys. Which Customer Insights integration method should they use?
A) Enter the probability score manually
B) Import the predictive score as an enrichment table
C) Use segmentation to approximate financial health stability
D) Use deterministic matching to calculate probability values
Answer: B
Explanation:
This banking organization relies on a sophisticated external AI model to predict financial stability. The model analyzes credit-card spending patterns, loan-repayment behavior, ATM withdrawal habits, fraud-alert frequency, mobile-banking usage depth, investment activity, and customer-service interactions. Because the model is external and updated monthly, Customer Insights must ingest the dataset through a process that supports recurring, structured imports. Enrichment tables are specifically designed for this purpose.
The bank gathers rich transactional and behavioral data. Credit-card transactions reveal cash-flow patterns, discretionary spending, and debt-carrying habits. Loan activity shows repayment timeliness and financial discipline. ATM usage displays liquidity needs and withdrawal frequency. Fraud-alert histories may indicate financial vulnerability. Mobile-app interactions reflect engagement with account management tools, payment systems, and budgeting features. Customer-service inquiries show support needs and potential financial distress.
The external AI model consumes all these signals and calculates a Financial Health Stability Probability Score. Customer Insights itself does not perform this predictive analysis; therefore, it must ingest the score from an external system. Enrichment tables allow recurring imports based on a schedule, enabling Customer Insights to refresh the probability score each month.
Option A is incorrect because manually entering probability scores is not feasible given the customer volume and frequent update interval.
Option C is incorrect because segmentation does not generate probabilistic predictions. Segments classify customers only after they have the numeric attribute available.
Option D is incorrect because deterministic matching identifies duplicate profiles. It does not calculate any form of predictive likelihood.
After enrichment ingestion, Customer Insights can use the score for segmentation and personalized financial-wellness journeys. High-risk customers might receive budgeting assistance or payment-reminder journeys. Low-risk customers may be targeted for savings or investment programs.
Because enrichment tables uniquely support structured external data ingestion, the correct answer is B.
Question 110:
A global fitness-and-wellness technology platform uses Dynamics 365 Customer Insights to unify member data from smart-gym equipment usage logs, wearable fitness trackers, nutrition-plan adherence data, virtual-coaching session attendance, mobile-app activity, personalized workout-recommendation engagement, and supplement-purchase histories. They want to compute a “Holistic Wellness Engagement Score” based on workout-session consistency, wearable-sensor activity patterns, nutrition-plan tracking frequency, coaching-session completion rates, and responsiveness to personalized workout recommendations over the last 150 days. The score must refresh automatically and appear as a numeric attribute for segmentation and hyper-personalized wellness journeys. Which Customer Insights capability should they use to compute this score?
A) Create a segment to classify members by wellness level
B) Build a measure to compute holistic wellness engagement
C) Use deterministic matching to identify active members
D) Import the score manually at the end of each quarter
Answer: B
Explanation:
This wellness-technology platform requires a multi-factor scoring system that calculates a Holistic Wellness Engagement Score using diverse data sources. Because the score needs to refresh automatically and respond to ongoing member behavior, Customer Insights measures are the correct tool. Measures support numeric aggregation, recency windows, weighting models, and the ability to integrate multiple interaction types across different time frames.
Workout-session consistency is central to wellness engagement. Smart-gym equipment logs show visit frequency, workout duration, training patterns, and exercise diversity. Measures can assess both frequency and recency, making them ideal for evaluating workout regularity.
Wearable-sensor activity patterns reveal steps, heart-rate zones, sleep data, and calorie expenditure. These signals help determine whether members follow healthy routines. Measures can incorporate aggregated wearable readings over the last 150 days.
Nutrition-plan adherence is another critical factor. Nutrition apps often track meal logs, calorie intake, hydration, and compliance with dietary plans. Measures can aggregate nutrition-tracking frequency and recency.
Virtual-coaching sessions provide personalized guidance. Attending coaching sessions consistently reflects higher engagement. Measures can calculate completion rates for scheduled sessions.
Personalized workout-recommendation engagement indicates interest in tailored routines generated by AI fitness planners. Engagement patterns—such as which recommendations members open, follow, save, or ignore—provide strong predictive signals. Measures can evaluate how frequently members accept or interact with recommendations.
Option A is incorrect because segmentation cannot compute numeric scores. It only groups members based on existing profile attributes.
Option C is incorrect because deterministic matching only merges duplicate profiles. It does not interpret workout logs, wearable data, or nutrition behavior.
Option D contradicts the platform’s need for continuous automated updates. Manually importing scores quarterly would not reflect week-to-week behavior changes.
Measures uniquely combine all necessary capabilities—aggregation, numerical scoring, behavioral weighting, and automated refresh—making option B the correct answer.
Question 111:
A global aviation services corporation uses Dynamics 365 Customer Insights to unify passenger data from mobile check-in behavior, loyalty-tier activity, inflight Wi-Fi usage logs, duty-free purchasing patterns, customer feedback submissions, flight-disruption assistance interactions, and airport-lounge access histories. They want to compute a “Passenger Experience Consistency Score” based on recency of check-ins, frequency of inflight digital engagement, loyalty-tier maintenance stability, responsiveness to service recovery offers, and consistency of lounge usage across 180 days. The score must refresh automatically and be stored as a numeric attribute for segmentation and hyper-personalized experience journeys. Which Customer Insights capability should they use to compute this score?
A) Create a segment to classify passengers by consistency
B) Build a measure that computes the experience consistency score
C) Use deterministic matching to unify consistent travelers
D) Import the score manually after each travel season
Answer: B
Explanation:
This question describes a complex aviation ecosystem where passenger engagement spans numerous digital and physical touchpoints. Airlines today collect substantial experience data: mobile check-ins, digital boarding pass downloads, flight-status tracking, inflight Wi-Fi usage, duty-free browsing, loyalty program activity, post-flight feedback, lounge visits, and service-recovery interactions. A Passenger Experience Consistency Score requires aggregating many of these data points to form a single numerical value. Because the score must update automatically and appear as a numeric attribute on passenger profiles, the correct Customer Insights capability is a measure.
Mobile check-ins are an important indicator of passenger engagement. Travelers who consistently check in digitally show strong adoption of airline mobile tools. Measures allow the organization to track recency and frequency of digital check-ins, assign weights, and include them in the scoring model.
Inflight Wi-Fi usage reveals how passengers engage during travel. Frequent Wi-Fi sessions reflect deeper interaction with inflight services or digital content. Measures can look at Wi-Fi session counts, recency, and duration, incorporating these signals into the score.
Loyalty-tier maintenance stability is a huge factor in aviation engagement. Passengers must maintain or upgrade tier levels by flying certain distances or meeting spend thresholds. Measures can interpret tier-maintenance behavior, track how consistently travelers retain their status, and incorporate these trends.
Service-recovery responsiveness is also critical. During disruptions such as delays, cancellations, or baggage issues, airlines send recovery offers or notifications. Passengers who engage with these offers quickly are more likely to retain trust with the airline. Measures can evaluate the time between recovery notifications and passenger responses.
Lounge-access consistency offers another dimension. Frequent lounge visits indicate high engagement with premium airport services. Measures can analyze lounge-visit logs, capturing recency and frequency within the 180-day window.
Option A is incorrect because segmentation cannot calculate numeric values. Segments classify passengers only after the score is available.
Option C is incorrect because deterministic matching only merges duplicate profiles; it does not compute engagement scores or analyze passenger behavior across channels.
Option D is incorrect because manually importing a score after each travel season contradicts the need for real-time updates. Passenger behavior changes daily, especially among frequent travelers.
Measures provide the required analytical capability: they aggregate interactions, apply recency filters, produce weighted behavior models, and recalculate dynamically whenever new data is ingested. For airlines wanting fully personalized and timely passenger experiences, measures are essential.
Thus, the correct answer is B.
Question 112:
An international banking and wealth-management institution uses Dynamics 365 Customer Insights to unify customer data from investment-portfolio activity, high-value transaction patterns, market-alert engagement, account-management portal usage, advisor consultation logs, credit-product applications, and financial-planning tool interactions. Their predictive analytics system produces a monthly “Wealth Growth Propensity Score” indicating which customers are most likely to increase their assets under management in the next 120 days. They need this score automatically ingested and mapped to unified profiles to power segmentation and wealth-advisor proactive outreach journeys. Which Customer Insights integration method should they use?
A) Manually enter the propensity score each month
B) Import the predictive model output as an enrichment table
C) Create segments to approximate propensity values
D) Use deterministic matching to generate wealth scores
Answer: B
Explanation:
The bank in this scenario relies on an external predictive analytics engine to compute a Wealth Growth Propensity Score. Customer Insights needs to ingest this score monthly and map it to unified customer profiles. Because this is a recurring external dataset requiring structured ingestion, enrichment tables are the correct integration method.
Financial institutions manage complex customer interactions. Investment-portfolio activity includes trades, fund transfers, portfolio diversification, rebalancing patterns, and asset-allocation behaviors. High-value transaction patterns, such as large transfers, investment deposits, or withdrawals, influence wealth-growth projections. Market-alert engagement shows whether customers pay attention to financial news, price movements, and advisory notifications. Account-management portal usage reflects digital engagement across channels including mobile and desktop. Advisor consultations reveal interest in guided financial planning and advisory relationships. Credit-product applications demonstrate willingness to expand financial commitments.
Because Customer Insights does not perform predictive analytics natively, the Wealth Growth Propensity Score must be imported from the external engine. Enrichment tables support scheduled ingestion windows, allowing the bank to import datasets monthly, match them to unified customer profiles using identifiers such as customer ID, email, or investment account number, and refresh the score.
Option A is not feasible because manually entering thousands of scores every month for wealth clients would be time-consuming, error-prone, and impractical.
Option C is incorrect because segmentation does not calculate predictive scores. It can group customers based on propensity values, but it cannot create those values.
Option D is incorrect because deterministic matching only merges duplicate profiles, not compute wealth-growth predictions.
Once the score is imported via enrichment, it becomes extremely valuable for segmentation. High-propensity customers may be targeted for premium advisory services, cross-sell opportunities, or portfolio-expansion discussions. Medium-propensity customers may receive personalized nudges or educational content. Low-propensity customers may be encouraged to re-engage through financial-wellness journeys.
Enrichment tables allow the predictive model to integrate seamlessly with Customer Insights, providing financial advisors with up-to-date signals. This supports highly personalized and proactive outreach strategies.
Thus, option B is correct.
Question 113:
A global theme-park and entertainment company uses Dynamics 365 Customer Insights to unify guest interactions from ticket purchases, virtual-queue reservations, mobile-app attraction check-ins, ride telemetry sensors, in-park dining transactions, wearable wristband activity logs, and post-visit survey feedback. They want to compute a “Guest Immersive Engagement Score” based on recency of ride participation, frequency of mobile-app interactions, dining-spend patterns, virtual-queue usage, and completeness of post-visit surveys over the last 120 days. The score must update automatically and power segmentation and dynamic in-park experience journeys. Which Customer Insights capability should they use to compute this score?
A) Create a segment that identifies engaged guests
B) Build a measure that computes immersive engagement
C) Use deterministic matching to unify duplicate guests
D) Import the score manually after peak season
Answer: B
Explanation:
An immersive theme-park experience generates vast amounts of data from physical and digital touchpoints. Ride participation logs, virtual-queue reservations, wearable-device interactions, mobile-app usage, dining purchases, and survey responses collectively form a guest’s engagement profile. To compute a Guest Immersive Engagement Score that evaluates all these behaviors across 120 days, the organization must use a measure.
Ride participation is a core engagement indicator. Telemetry sensors, ride entry points, and wristband taps track which rides guests experience and how frequently. Measures can evaluate ride-visit recency and frequency.
Virtual-queue reservations represent digital engagement. Guests who frequently reserve spots through the mobile app often demonstrate high readiness to engage with attractions. Measures can comb through virtual-queue events and incorporate them into the score.
Dining-spend patterns show willingness to purchase premium in-park experiences. Measures can sum dining transactions, track purchase types, and apply weighted contributions.
Mobile-app interactions are another key component. Guests use apps for navigation, attraction recommendations, wait-time checking, and mobile food ordering. Measures can evaluate recency and depth of app usage.
Post-visit survey completeness helps determine feedback engagement. Guests who complete full surveys often express higher interest in helping improve park experiences. Measures can incorporate the presence or completeness of surveys.
Option A is incorrect because segmentation cannot calculate numeric values. It only categorizes guests based on attributes created by measures.
Option C is incorrect because deterministic matching only merges duplicate records. It does not evaluate or score guest interactions.
Option D is incorrect because manually importing scores after peak season does not satisfy the requirement for ongoing automatic recalculation.
Measures integrate all engagement signals into a single quantitative model. Because theme-park experiences depend on real-time personalization, a measure is absolutely required.
Thus, the correct answer is B.
Question 114:
A worldwide educational services provider uses Dynamics 365 Customer Insights to unify learner interactions from virtual classroom attendance, online course completions, adaptive-learning quiz performance, mentorship session logs, mobile-study-app engagement, certification exam attempts, and skill-development pathway progress. They want to compute a “Learner Progression Momentum Score” based on course-completion velocity, quiz-performance stability, mentorship participation, app-engagement recency, and certification-attempt frequencies over the last 150 days. The score must update dynamically and serve as a numeric attribute to fuel segmentation and skill-acceleration journeys. Which Customer Insights capability should be used to compute this score?
A) Create a segment showing high-momentum learners
B) Build a measure that calculates progression momentum
C) Use deterministic matching to identify learner profiles
D) Import the score manually during academic reviews
Answer: B
Explanation:
Education platforms rely heavily on engagement analytics to determine learner momentum. Learners engage across multiple dimensions: course completions, quiz attempts, mobile-app interactions, mentorship sessions, certification practice tests, and skill-pathway milestones. To compute a Learner Progression Momentum Score that captures these multi-channel signals, Customer Insights must use a measure.
Course-completion velocity indicates how quickly learners progress through content. Measures can analyze timestamps of completions, calculate velocity within the last 150 days, and weight faster progress differently.
Quiz-performance stability reveals whether learning is consistent. Stable performance across adaptive quizzes indicates strong retention. Measures can aggregate attempt patterns and scores.
Mentorship participation is a strong indicator of momentum. Learners often schedule coaching sessions for clarification or advancement. Measures can evaluate mentorship session counts and recency.
Mobile study-app engagement demonstrates daily learning habits. Measures can determine recency and frequency of study-app usage.
Certification-attempt frequencies indicate readiness for skill validation. Measures can assess how many attempts learners make and how recently.
Option A is incorrect because segmentation cannot compute momentum. It can only classify learners after the score exists.
Option C is incorrect because deterministic matching only fixes duplicates.
Option D is incorrect because manually importing scores during academic reviews contradicts the need for dynamic recalculation.
Measures allow complex aggregation across many learning behaviors, making them the only correct tool.
Thus, option B is correct.
Question 115:
A luxury fashion retail group uses Dynamics 365 Customer Insights to unify customer data from boutique appointment bookings, VIP styling-session attendance, mobile shopping-app interactions, runway livestream engagement, purchase histories, loyalty-reward redemptions, and fashion-trend preference quizzes. They want to compute a “Fashion Loyalty Depth Score” based on VIP appointment recency, app-engagement frequency, livestream participation, trend-quiz completion, and premium-category purchase patterns within 200 days. The score must refresh automatically and be available as a numeric attribute for segmentation and luxury-tier upgrade journeys. Which Customer Insights capability should they use to compute this score?
A) Create a segment showing deep-loyalty customers
B) Build a measure that computes loyalty depth
C) Use deterministic matching to identify loyal customers
D) Import the score manually at fiscal-year end
Answer: B
Explanation:
Luxury fashion loyalty involves deep emotional and experiential engagement across digital and physical channels. Customers interact through boutique appointments, VIP styling experiences, exclusive fashion events, personalized mobile apps, livestream shows, and curated purchasing journeys. A Fashion Loyalty Depth Score requires aggregating all of these behaviors and weighting them accordingly. Customer Insights measures are the only capability capable of computing such a score.
VIP appointment recency and frequency are strong indicators of deep brand loyalty. Customers attending styling sessions engage closely with brand representatives. Measures can evaluate appointment logs over 200 days and weight them based on recency.
Mobile-app interactions, such as browsing collections, saving lookbooks, or customizing digital wardrobes, provide digital engagement signals. Measures can capture mobile browsing recency and activity volume.
Livestream engagement reflects interest in new collections. Measures can include livestream participation events.
Fashion-trend quizzes show interest in upcoming styles. Measures can calculate quiz completion frequency.
Premium-category purchases such as couture, limited-edition accessories, and exclusive runway items indicate deep loyalty. Measures can aggregate purchase histories and assign weighted scores based on product category.
Option A is incorrect because segments do not compute scores.
Option C is incorrect because deterministic matching only merges duplicates.
Option D is incorrect because manual yearly imports do not support dynamic engagement tracking.
Measures uniquely allow real-time loyalty depth calculations.
Thus, the correct answer is B.
Question 116:
A multinational electronics brand uses Dynamics 365 Customer Insights to unify customer data from warranty registrations, product IoT diagnostics, mobile app usage for device controls, online support chat logs, in-store repair appointments, product review submissions, and accessory purchase behaviors. They want to compute a “Device Ecosystem Engagement Score” that measures warranty-activation recency, frequency of IoT-generated diagnostic events, mobile-app usage intensity, responsiveness to automated support alerts, and cross-sell accessory purchases over the last 180 days. The score must recalculate automatically and serve as a numeric profile attribute for segmentation and lifecycle-marketing journeys. Which Customer Insights capability should they use to compute this score?
A) Build a segment grouping users by device engagement
B) Build a measure to compute the device ecosystem engagement score
C) Use deterministic matching to unify multi-device owners
D) Import the score manually after semi-annual evaluations
Answer: B
Explanation:
This question requires identifying the Dynamics 365 Customer Insights capability best suited for automatically computing a complex, dynamic, multi-factor engagement score that pulls from numerous behavioral and product-related activities. The scenario describes a global electronics brand that captures data from many channels: warranty activations, IoT device telemetry, mobile-app interactions, support chat logs, in-store repair visits, product reviews, and accessory purchases. Each of these data points contributes a unique behavioral signal. The organization wants these signals synthesized into a unified numerical score reflecting the customer’s relationship with the device ecosystem. In Customer Insights, only measures can calculate such a numeric score using aggregated interactions, recency filters, and continuous recalculation.
Warranty registration behavior is critical for electronics manufacturers. Customers who activate warranties promptly are more engaged and likely to adopt brand services. Measures allow calculation of recency and frequency values tied to warranty events.
IoT diagnostics are important because modern electronics often send telemetry such as performance metrics, battery health, component alerts, or usage cycles. Frequent IoT events might indicate heavy usage or proactive device monitoring. Measures can weight diagnostic frequency and recency across a 180-day window.
Mobile-app usage intensity is another major engagement indicator. Customers increasingly rely on mobile apps to adjust device settings, activate smart modes, receive firmware updates, or integrate devices with other smart-home components. Measures can compute usage frequency, session counts, and recency.
Support-alert responsiveness is also measured. When devices send automated warnings such as overheating notices, firmware-update prompts, or maintenance alerts, engaged customers tend to respond. Measures can incorporate alert-response time and frequency.
Accessory purchases reveal cross-sell engagement. Customers who purchase additional accessories such as cases, chargers, or companion devices typically have a deeper ecosystem commitment. Measures can calculate purchase frequency and category type to contribute to the score.
Option A is incorrect because segments do not compute numeric values. Segments filter customers based on existing attributes but cannot generate engagement scores.
Option C is incorrect because deterministic matching is used only to merge duplicate profiles. It does not measure IoT interactions, mobile-app usage, or accessory purchases.
Option D is incorrect because manually importing scores twice a year violates the requirement for automated recalculation and continuous profile updates. Electronics engagement changes frequently with device lifecycle moments, making manual updates impractical.
Because the scenario describes a numeric score requiring ongoing aggregation and real-time recalculation, the solution must be a measure. Measures allow organizations to incorporate multiple data sources, apply weighting logic, define time windows, and store results as profile attributes usable in segmentation and customer journeys.
Thus, option B is correct.
Question 117:
A global property-management and smart-home IoT company uses Dynamics 365 Customer Insights to unify data from tenant rent-payment behavior, maintenance-request submissions, smart-thermostat activity, door-lock usage logs, energy-consumption analytics, digital-community-portal interactions, and satisfaction-survey responses. Their data-science team generates a monthly “Tenant Stability Prediction Score” estimating likelihood of lease renewal within the next 120 days. They want this predictive score imported monthly and mapped to unified tenant profiles to create segments for high-risk tenants, stable tenants, and proactive-renewal journeys. Which Customer Insights integration method should they use to ingest this score?
A) Manually enter prediction scores every month
B) Import the prediction dataset as an enrichment table
C) Use segmentation to derive probability approximations
D) Use deterministic matching to generate prediction values
Answer: B
Explanation:
This scenario makes it clear that the predictive model does not run inside Customer Insights. The company’s data-science team provides an external dataset containing a Tenant Stability Prediction Score every month. The question is which Customer Insights feature allows regular ingestion of external numeric output and maps it correctly to unified tenant profiles. Enrichment tables are explicitly designed for recurring imports of third-party or externally computed datasets.
Property-management organizations track large volumes of behavioral information. Rent-payment behavior includes on-time payments, partial payments, or late-payment frequency. Maintenance requests reflect tenant satisfaction and property condition. Smart-home device interactions show how actively tenants use leased-property amenities. Door-lock logs track entry patterns or security interactions. Energy-consumption analytics show sustainability habits, which may correlate with long-term tenancy. Community-portal interactions include announcements, maintenance scheduling, or amenity reservations. Survey responses reflect sentiment toward property management.
A stability prediction score analyzes all these behavioral and sentiment signals to estimate lease-renewal probability. Because the score is produced externally, Customer Insights must import it. Enrichment tables allow the organization to load new files monthly, map them to unified profiles using tenant IDs, property numbers, email addresses, or other matching attributes.
Option A is incorrect because manually entering prediction scores every month is inefficient and error-prone, especially for large rental communities.
Option C is incorrect because segmentation does not compute predictive values. Segments categorize tenants into cohorts but cannot generate predictions.
Option D is incorrect because deterministic matching merges duplicates. It does not calculate predictive scores.
Once imported through enrichment, the prediction score becomes a profile attribute used in segmentation. High-risk tenants may receive special retention messaging or proactive maintenance offers. Stable tenants may be targeted for lease-extension promotions. Tenants showing strong probability of renewal can be encouraged toward long-term multi-year deals.
Enrichment tables also support refresh scheduling, validation, and mapping rules, ensuring that new scores replace outdated ones. This aligns perfectly with the monthly update requirement.
Thus, option B is correct.
Question 118:
A global automotive manufacturer uses Dynamics 365 Customer Insights to unify driver behavior from connected-vehicle telemetry, dealership-service records, mobile app remote-control usage, EV battery-charging logs, warranty-extension purchases, recall-notification engagement, and digital-navigation history. They want to compute a “Driver Lifecycle Engagement Score” that measures driving-behavior recency, service-center visit patterns, app-engagement frequency, battery-charging consistency, and responsiveness to recall notifications across 160 days. The score must update in real time and appear as a numeric attribute for segmentation and owner-retention journeys. Which Customer Insights feature should they use to compute this score?
A) Build a segment categorizing drivers by engagement
B) Build a measure that computes the lifecycle engagement score
C) Use deterministic matching to unify multi-vehicle owners
D) Import the score manually after quarterly service cycles
Answer: B
Explanation:
The automotive industry generates highly dynamic and complex interaction data. Drivers interact with their vehicles through connected telemetry, mobile apps, service centers, and digital alerts. To evaluate these behaviors over a 160-day period and compute a numeric lifecycle-engagement score, Customer Insights must use a measure. Measures calculate numeric attributes using interaction data and update automatically whenever new telemetry or service data arrives.
Connected-vehicle telemetry is especially important for engagement scoring. Modern vehicles track acceleration, braking patterns, trip duration, geolocation events, and performance indicators. Measures can aggregate trip counts, recency of driving activity, and usage intensity.
Dealership service records reflect maintenance engagement. Drivers who regularly schedule oil changes, tire rotations, or diagnostic checks demonstrate strong adherence to vehicle-health routines. Measures can evaluate service-frequency and recency.
Mobile app usage shows how often drivers use remote start, climate-control preconditioning, lock/unlock commands, or EV range monitoring. Measures can aggregate app interactions and weight them based on time.
EV charging logs offer additional behavioral insight. Consistent charging patterns indicate strong familiarity with the ecosystem. Measures can calculate frequency, recency, and charging-station variety.
Recall-notification engagement is crucial for safety compliance. Drivers who respond quickly to recall alerts exhibit high engagement and maintenance awareness. Measures can encode responsiveness intervals.
Option A is incorrect because segments do not calculate scores—they use them after they exist.
Option C is incorrect because deterministic matching only merges duplicates, not compute engagement.
Option D contradicts the requirement for real-time updates.
Measures support numerical scoring, recency filters, aggregation across multiple channels, and dynamic recalculation—exactly what the automotive manufacturer requires.
Thus, the correct answer is B.
Question 119:
A multinational hospitality group uses Dynamics 365 Customer Insights to unify guest interactions from hotel-stay histories, dining-reservation patterns, spa-service usage, loyalty-reward redemptions, mobile app check-in features, room-service ordering, and post-stay satisfaction surveys. Their analytics team generates a quarterly “Guest Value Expansion Probability Score,” predicting which guests are likely to upgrade to premium suites or purchase high-value packages in the next 90 days. They want this score imported automatically and attached to unified profiles for segmentation and luxury-tier marketing journeys. Which Customer Insights integration method should they use to ingest this probability score?
A) Manually upload the probability score
B) Import the dataset as an enrichment table
C) Build a segment to approximate probability levels
D) Use deterministic matching to compute expansion probability
Answer: B
Explanation:
In this hospitality scenario, Customer Insights must import an external predictive dataset created by an analytics team. The predictive model produces a Guest Value Expansion Probability Score estimating a guest’s likelihood of upgrading accommodations or purchasing premium packages. Because the score is generated outside Customer Insights and must be refreshed quarterly, enrichment tables are the correct integration method.
Hotel-stay histories include stay frequency, room-category usage, booking patterns, and length of stay. Dining-reservation patterns reveal guest preferences for high-end restaurants or casual dining. Spa-service usage often indicates willingness to purchase premium wellness services. Loyalty reward redemptions show involvement with membership programs. Mobile-app check-ins and room-service ordering reflect digital engagement. Post-stay surveys provide sentiment data.
The predictive model analyzes all these variables and produces a probability score. Customer Insights must ingest this external score and map it to unified guest profiles. Enrichment tables support structured ingestion of external datasets, identify matching keys like loyalty ID or reservation ID, and ensure that new scores overwrite outdated ones.
Option A is incorrect because manual uploads introduce errors and are not scalable for large guest populations.
Option C is incorrect because segmentation cannot approximate predictive probabilities; it relies on existing numeric attributes.
Option D is incorrect because deterministic matching identifies duplicates and does not compute predictive scores.
Enrichment tables provide the refresh capability, mapping logic, and automation necessary to integrate quarterly predictive analytics.
Thus, option B is correct.
Question 120:
An international subscription-based fitness-equipment company uses Dynamics 365 Customer Insights to unify customer interactions from smart-equipment usage logs, workout-class streaming history, mobile fitness-app engagement, accessory purchases, customer-support interactions, and membership-renewal patterns. They want to build a “Subscriber Fitness Engagement Score” measuring workout frequency, streaming-session completion rates, app-engagement depth, purchase behavior, and renewal-timing consistency over the last 140 days. The score must refresh automatically and be used as a numeric profile attribute in segmentation and retention-improvement journeys. Which Customer Insights capability should they use to compute this score?
A) Create a segment showing highly engaged subscribers
B) Build a measure to compute the engagement score
C) Use deterministic matching to remove duplicate accounts
D) Import the score manually each membership cycle
Answer: B
Explanation:
The fitness-equipment company collects extensive engagement data across many channels: smart-equipment telemetry, workout streaming sessions, mobile-app activities, accessory purchases, support interactions, and subscription renewals. The organization wants a single numeric score representing subscriber engagement across all interactions. Only a measure can compute this type of dynamic, multi-channel engagement score.
Smart-equipment usage logs show workout frequency, intensity, duration, and exercise type. Measures can analyze recency and frequency patterns.
Streaming-class completion rates indicate commitment. Measures can calculate completion percentages and viewing consistency.
Mobile-app engagement reveals depth of interaction, such as tracking calories, monitoring heart rate, scheduling workouts, or saving fitness plans. Measures can aggregate app events.
Accessory purchases show cross-sell behavior. Measures can incorporate purchase recency and item-category weight.
Membership-renewal patterns are strong engagement indicators. Measures can track renewal times and detect early-renewal behaviors.
Option A is incorrect because segments cannot compute numeric scores. They can only use scores that already exist.
Option C is incorrect because deterministic matching merges duplicate records and cannot compute engagement.
Option D contradicts the requirement for automatic recalculation.
Thus, option B is correct.
