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Question 121:
A multinational shipping, logistics, and parcel-tracking company uses Dynamics 365 Customer Insights to unify customer interactions from shipment-tracking events, delivery-exception cases, mobile-app package-status checks, customer-support ticket history, recurring scheduled pickup requests, billing-system payment patterns, and proactive communication-alert engagement. They want to compute a “Logistics Journey Reliability Score” over the last 170 days that evaluates tracking-check recency, response times to exceptions, delivery-exception recurrence patterns, support-ticket resolution engagement, mobile-app usage, and billing-payment consistency. The score must refresh automatically and appear as a numeric attribute for segmentation and fulfillment-accuracy optimization journeys. Which Customer Insights feature should they use to calculate this score?
A) Build a segment that filters customers by reliability
B) Build a measure that computes the journey reliability score
C) Use deterministic matching to unify repeat shippers
D) Import the score manually after quarterly logistics reviews
Answer: B
Explanation:
This scenario describes a global logistics organization seeking to track customer reliability and engagement across multiple digital and operational touchpoints. These touchpoints include shipment-tracking behaviors, delivery-exception events, package-status checks in the mobile app, support ticket history, scheduled pickup interactions, payment consistency, and communication-alert engagement. To generate a unified Logistics Journey Reliability Score that updates continuously and uses data across 170 days, Customer Insights must employ a measure.
Measures are designed to compute numeric values dynamically by aggregating interaction data. Because the score needs to evaluate recency, frequency, and pattern analysis, measures provide the correct functionality. They allow organizations to create weighted formulas, define time windows, and generate profile-level attributes that update whenever new data flows into the system.
Shipment-tracking activity indicates how often customers check their parcel status, which reflects engagement with the logistics journey. Measures can track recency and frequency of tracking events. Customers who frequently check tracking updates may be proactive, while those who rarely check may have different service expectations.
Delivery-exception responses offer insight into reliability and engagement. Exceptions such as address issues, missed deliveries, customs holds, or package rerouting require quick customer responses. Measures can calculate response times and incorporate them into the reliability score. Faster responses typically correlate with smoother logistics outcomes.
Support tickets also reveal customer behavior. Some customers open multiple support cases for small issues, while others engage only when absolutely necessary. Measures can evaluate ticket-resolution engagement and detect whether the customer consistently acknowledges resolutions or requires multiple follow-ups.
Mobile-app usage provides additional digital interaction signals. Customers who use the app to monitor shipments, schedule pickups, modify preferences, or manage delivery windows demonstrate higher digital readiness. Measures can compute mobile-app usage frequency and recency.
Scheduled pickup requests and billing-payment consistency reflect operational reliability. Measures can incorporate on-time payment patterns, failed payment retries, and proactive scheduling behaviors.
Option A is incorrect because segments filter customers into cohorts but cannot compute numeric reliability scores. Segmentation only works once the numeric attribute already exists.
Option C is incorrect because deterministic matching merges duplicate records. It does not interpret tracking logs or compute reliability metrics.
Option D is incorrect because manual quarterly imports fail to meet the requirement for automatic continuous recalculation. Logistics operations are highly dynamic, and customer engagement may change daily.
Measures are the only Customer Insights feature capable of computing weighted engagement values based on constantly updating data streams. Therefore, option B is the correct answer.
Question 122:
An international mobile network operator uses Dynamics 365 Customer Insights to unify subscriber interactions from call-detail records, 5G device telemetry, mobile data consumption patterns, prepaid top-up history, billing-payment behaviors, fraud-alert responses, Wi-Fi hotspot usage, and customer-support chat transcripts. Their external data-science team produces a monthly “Subscriber Retention Probability Score” predicting likelihood of churn within 60 days. They want this score automatically imported each month and mapped to unified subscriber profiles for segmentation and churn-prevention journeys. Which Customer Insights integration method should they use?
A) Manually upload the retention probability score each month
B) Import the predictive score using an enrichment table
C) Build a segment to approximate churn probability
D) Use deterministic matching to calculate retention likelihood
Answer: B
Explanation:
Telecommunications providers generate extremely large data volumes from numerous systems, including call-detail records, device telemetry, usage logs, payment behaviors, fraud alerts, and customer service interactions. In this scenario, the carrier receives a monthly Subscriber Retention Probability Score produced by an external predictive model. This score must be ingested automatically, merged into unified subscriber profiles, and used for segmentation. Customer Insights offers one feature designed specifically for recurring ingestion of external predictive results: enrichment tables.
Enrichment tables allow Customer Insights to take external datasets—such as churn predictions, lifetime value scores, or retention indices—and match them to existing unified profiles based on identifiers like subscriber ID, phone number, or billing account number. They support scheduled updates, ensuring that the external predictive scores overwrite outdated data every month.
Telecom datasets often reflect many dimensions of subscriber behavior. Call-detail records show call frequency, call duration, voice-service reliance, and international calling behavior. Device telemetry reflects 5G connectivity, signal stability, and device performance trends. Data-usage patterns may show whether subscribers rely heavily on mobile broadband. Prepaid top-up history highlights spending habits. Billing-payment difficulties often correlate with churn risk. Fraud-alert behavior reveals whether subscribers respond promptly to suspicious activity. Wi-Fi hotspot interactions show digital engagement, and customer-support transcripts reveal sentiment.
Because Customer Insights does not generate predictive scores itself, importing the score is essential. The enrichment table allows mapping the predictive output directly to subscriber profiles.
Option A is incorrect because manually uploading is not feasible at scale.
Option C is incorrect because segmentation does not approximate probability values. Segments group subscribers but do not create scores.
Option D is incorrect because deterministic matching only merges duplicate subscriber records. It does not compute predictions.
Enrichment tables allow consistent, accurate, automated ingestion, making option B correct.
Question 123:
A multinational energy provider uses Dynamics 365 Customer Insights to unify customer interactions from smart-meter energy-usage graphs, outage-reporting logs, mobile app account-management behavior, billing-cycle payment reliability, renewable energy program sign-ups, customer-support case history, and community energy-efficiency workshop participation. They want to compute an “Energy Engagement Optimization Score” that reflects outage-report recency, payment stability, smart-meter activity patterns, renewable-program participation, and mobile-app engagement across the last 155 days. The score must refresh automatically as new data arrives and be used as a numeric attribute in segmentation and sustainability-journey personalization. Which Customer Insights feature should they use to compute this score?
A) Build a segment for high-engagement energy customers
B) Build a measure to compute the engagement optimization score
C) Use deterministic matching to identify multi-property customers
D) Import the score manually after every billing cycle
Answer: B
Explanation:
Energy providers gather substantial behavioral and operational data from their customers. Smart-meter readings track daily consumption cycles, peak-usage times, and efficiency behaviors. Outage-report logs track when customers report power loss or service disturbances. App interactions reflect how users manage accounts, monitor energy usage, pay bills, or update service preferences. Billing-cycle payment stability often correlates with long-term account reliability. Renewable-energy program participation demonstrates commitment to sustainability. Support interactions reflect levels of engagement with the provider.
Customer Insights must compute an Energy Engagement Optimization Score that captures recency, frequency, and weighted behavior patterns across 155 days. Only a measure can compute a dynamic numeric score using multiple interaction sources and continuous recalculation.
Smart-meter readings often update daily or even hourly. A measure can evaluate usage-recency, compare usage patterns, and apply weighting for sustainable practices such as reduced peak usage.
Outage-report recency is also meaningful. Customers who report outages promptly contribute to accurate grid monitoring. Measures can incorporate these timestamps.
Billing-payment reliability reveals financial consistency. Measures can incorporate on-time payments or delinquencies across billing cycles.
App-engagement reflects digital readiness. Customers who use the provider’s mobile app frequently demonstrate stronger engagement with self-service tools. Measures can evaluate login frequency and recency.
Renewable-program participation indicates deeper involvement in sustainability programs. Measures can assign weighted points for enrollment or continued participation.
Option A is incorrect because segments do not compute numeric values.
Option C is incorrect because deterministic matching only resolves duplicates.
Option D is incorrect because manual imports violate the requirement for automated daily recalculations.
Measures allow full control over numeric scoring logic, aggregation, and recency weighting, making option B correct.
Question 124:
A global online learning platform uses Dynamics 365 Customer Insights to unify learner interactions from course enrollments, module-completion records, quiz attempts, live-webinar attendance, mobile study-app activity, certification exam attempts, and instructor feedback submissions. Their AI model generates a monthly “Learner Certification Success Probability Score.” They want this predictive score imported automatically and connected to unified learner profiles so they can segment learners by readiness and deliver personalized study-acceleration journeys. Which Customer Insights feature should they use to ingest this data?
A) Manually upload the monthly success probability score
B) Import the dataset as an enrichment table
C) Use a segment to approximate success probability
D) Use deterministic matching to generate probability values
Answer: B
Explanation:
The learning platform uses external AI to generate certification success probability scores. These scores need to be imported monthly and mapped to unified profiles. Customer Insights cannot compute predictive values but can ingest them via enrichment tables. Enrichment tables allow loading datasets containing numeric attributes generated by an external system and mapping them to learners using identifiers such as learner ID, email, or enrollment ID.
Learning platforms generate diverse behavioral data. Course enrollments show interest levels. Module completions reveal progress. Quiz attempts highlight understanding and improvement patterns. Live-webinar attendance shows engagement with real-time instruction. Mobile-app activity demonstrates continuous learning behaviors. Certification attempts reflect readiness. Instructor feedback reveals qualitative performance insights.
The AI model considers all of these factors when computing success probability. Customer Insights must ingest this dataset monthly. Enrichment tables provide the structured ingestion pipeline required to map externally generated data into unified learner profiles.
Option A is incorrect because manual uploads are not scalable or efficient.
Option C is incorrect because segmentation does not calculate probability scores; it classifies based on existing numeric values.
Option D is incorrect because deterministic matching merges duplicate profiles but cannot calculate probability.
Thus, the correct answer is B.
Question 125:
A luxury automotive-leasing company uses Dynamics 365 Customer Insights to unify customer interactions from vehicle-inspection logs, lease-renewal behavior, connected-car telemetry, mobile-app remote control features, dealership appointment records, high-end accessory purchases, and customer-support satisfaction surveys. They want to compute a “Premium Driver Engagement Score” based on lease-renewal consistency, driving-pattern recency, mobile-app usage frequency, premium accessory purchase patterns, and dealership-service interactions over the last 165 days. The score must update automatically as new data flows in and be used as a numeric profile attribute to personalize renewal and upgrade journeys. Which Customer Insights capability should they use to compute this score?
A) Build a segment showing engaged luxury drivers
B) Build a measure that computes the premium driver engagement score
C) Use deterministic matching to unify duplicate driver profiles
D) Import the score manually after yearly fleet reviews
Answer: B
Explanation:
Luxury automotive-leasing customers generate varied interaction data across numerous touchpoints. Vehicle-inspection logs track pre-return and mid-lease inspections. Lease-renewal patterns show whether customers renew early, renew on schedule, or delay. Connected-car telemetry provides real-time activity such as trip-duration patterns and remote-diagnostic behaviors. Mobile-app usage shows how drivers engage with remote features such as climate control, lock/unlock, vehicle locator, and EV battery monitoring. Accessory purchases reveal willingness to buy premium upgrades. Service appointments and survey responses provide insight into driver satisfaction.
To compute a Premium Driver Engagement Score across 165 days, Customer Insights requires a measure. Measures allow organizations to build numeric scoring formulas, apply recency filters, weight behavior categories, and calculate scores in real time whenever new data flows into the platform.
Option A is incorrect because segments do not compute numeric scores.
Option C is incorrect because deterministic matching only merges duplicates.
Option D is incorrect because manual yearly imports contradict the requirement for dynamic, ongoing recalculation.
Thus, option B is correct.
Question 126:
A multinational consumer smart-home electronics manufacturer uses Dynamics 365 Customer Insights to unify data from device-pairing logs, mobile-app feature-usage tracking, automated firmware-update compliance, IoT sensor alerts, in-app help-center interactions, product-registration histories, and cross-device ecosystem connectivity. They want to compute an advanced “Smart-Home Ecosystem Behavior Score” covering device-pairing recency, mobile-feature interaction intensity, firmware-update responsiveness, cross-device hub usage, IoT alert acknowledgment speed, and registration-completion frequency across the past 180 days. The score must refresh automatically and be applied as a numeric profile attribute for segmentation and predictive support-intervention journeys. Which Customer Insights feature should they use to compute this score?
A) Build a segment to classify smart-home customers
B) Build a measure to compute the ecosystem behavior score
C) Use deterministic matching to unify multi-device households
D) Import the score manually after semiannual evaluations
Answer: B
Explanation:
This scenario describes an ecosystem-driven smart-home manufacturer whose products generate significant streams of telemetric and behavioral data. Devices connect through hubs, mobile apps, cloud services, IoT sensors, and automated firmware updates. Customers also engage through registration portals, help-center tools, and device-pairing journeys. The company needs to compute a Smart-Home Ecosystem Behavior Score reflecting a wide set of behaviors across 180 days. Because the score is numeric, multi-factor, calculated from interaction data, and must refresh automatically, the only Customer Insights capability suited for this is a measure.
Device-pairing logs provide important signals. When customers pair new smart-home devices with hub controllers, it reflects system expansion and ecosystem commitment. Measures allow aggregation of pairing events, weighted by recency. Recent pairing activity suggests increased engagement.
Mobile-app feature interactions reflect digital behavior depth. Smart-home apps allow users to adjust lighting scenes, thermostat temperatures, security modes, appliance timers, or energy-monitoring tools. Measures can calculate frequency and recency of app feature usage, which often correlate with customer satisfaction and long-term retention.
Firmware-update responsiveness is crucial for device stability and security. Users who promptly install updates demonstrate proactive maintenance behavior. Measures can calculate the average time between update availability and installation, weighted for recent interactions.
Cross-device hub usage shows how customers integrate multiple devices into unified workflows. When customers set up multi-device routines—such as lights turning on when a door opens—the level of ecosystem engagement increases significantly. Measures can capture hub-related interactions.
IoT sensor alerts and acknowledgment behaviors reflect how quickly users respond to system notifications such as low-battery alerts, security alarms, water-leak sensors, or temperature spikes. Measures can incorporate acknowledgment frequency and response-time logic.
Registration completion indicates whether customers finish product registration workflows. Completing registrations typically signals brand trust and willingness to receive support or updates. Measures can incorporate patterns of completed registrations across device categories.
Option A is incorrect because segmentation cannot compute the score. Segments only classify customers once the score exists as a profile attribute created by a measure.
Option C is incorrect because deterministic matching focuses on merging duplicate customers or devices. It does not compute behavioral scores.
Option D is incorrect because manual semiannual imports contradict the requirement for continuous recalculation. IoT environments produce daily behavior changes requiring automatic scoring updates.
Measures uniquely allow weightings, aggregations, recency filters, cross-interaction modeling, and continuous score generation. Because the Smart-Home Ecosystem Behavior Score depends on dynamic multi-source telemetry and engagement input, a measure is the only correct choice.
Thus, the correct answer is B.
Question 127:
A global fintech payment-processing corporation uses Dynamics 365 Customer Insights to unify merchant behaviors from transaction-volume trends, dispute-resolution responsiveness, fraud-alert acknowledgments, terminal-health telemetry, digital dashboard interactions, merchant-support case history, and contract-renewal behavior. Their external analytics engine produces a quarterly “Merchant Financial Reliability Probability Score” predicting which merchants will maintain stable transaction behavior and low dispute ratios over the next 90 days. They want this score imported automatically on a quarterly schedule and mapped to unified merchant profiles for segmentation and fraud-prevention journeys. Which Customer Insights integration method should the organization use?
A) Manually enter reliability scores every quarter
B) Import the dataset as an enrichment table
C) Build segments to approximate reliability probability
D) Use deterministic matching to compute prediction values
Answer: B
Explanation:
This scenario involves a fintech corporation that unifies merchant data from numerous operational and behavioral touchpoints: transaction trends, disputes, fraud alerts, terminal telemetry, dashboard interactions, support cases, and contract renewals. Their predictive analytics engine evaluates these behaviors externally and generates a quarterly Merchant Financial Reliability Probability Score. Because the score originates outside Customer Insights, the organization needs to import it into the platform on a predictable quarterly basis. Customer Insights supports this through enrichment tables.
Enrichment tables are designed specifically for loading external data and mapping it onto unified profiles. They can store numeric outputs from third-party models, update them periodically, and ensure proper alignment with existing merchant identifiers. This perfectly fits the need to import quarterly predictive scores.
Transaction-volume trends indicate merchant activity levels. Rapid changes may signal growth, decline, or instability. The predictive model interprets these patterns, but Customer Insights itself does not run the model—so the ingestion must occur externally.
Dispute-resolution responsiveness is a critical factor. Merchants with slower dispute resolution times may present higher financial risk. The external model uses these attributes to generate a probability score.
Fraud-alert acknowledgment behaviors are equally important. Quick responses to fraud warnings indicate operational maturity and fraud-risk awareness.
Terminal-health telemetry reflects whether merchants maintain, update, and troubleshoot payment terminals. This telemetry can include device uptime, software version, connectivity, and failure patterns.
Merchant dashboard interactions show digital engagement. Merchants who frequently log into dashboards to monitor payments often maintain better financial control.
Support case history reveals operational friction, such as recurring technical issues or payment-flow interruptions.
Contract-renewal patterns reflect merchant retention likelihood.
The external predictive model processes all these data points and produces a Merchant Financial Reliability Probability Score. Customer Insights must ingest that score. Only enrichment tables support this functionality.
Option A is incorrect because manual quarterly entries introduce errors, inconsistency, and inefficiency.
Option C is incorrect because segmentation cannot replicate the complexity of predictive modeling. It only groups merchants after numeric data already exists.
Option D is incorrect because deterministic matching is not a predictive analytic tool—it simply merges duplicate or related profiles.
Enrichment tables allow mapping external deterministic or probabilistic model outputs to Customer Insights profiles on a repeating schedule, making option B the correct answer.
Question 128:
A global medical-equipment leasing provider uses Dynamics 365 Customer Insights to unify customer data from equipment-usage telemetry, scheduled maintenance logs, on-site technician service reports, renewal-contract interactions, billing-cycle behaviors, IoT alert acknowledgments, and digital-portal login patterns. They want to compute a “Medical Equipment Lifecycle Engagement Score” that evaluates usage-recency, maintenance-adherence frequency, responsiveness to IoT condition-alerts, portal-interaction depth, and renewal-contract consistency across 165 days. The score must refresh automatically and be available as a numeric profile attribute for segmentation and proactive maintenance-intervention journeys. Which Customer Insights capability should they use to compute the score?
A) Create a segment showing high-engagement medical customers
B) Build a measure that computes the lifecycle engagement score
C) Use deterministic matching to unify hospital locations
D) Import the score manually during annual contract reviews
Answer: B
Explanation:
The medical-equipment leasing provider collects a wide range of device and customer interaction data. Equipment-usage telemetry reports how frequently leased devices such as MRI machines, infusion pumps, ventilators, or monitoring systems are used. Scheduled maintenance logs record adherence to required service routines. Technician service reports show whether equipment experienced breakdowns, calibration events, or component replacements. Billing behavior and renewal patterns reveal reliability. IoT alerts signal maintenance conditions such as overheating or sensor failures. Portal login patterns reflect digital engagement with device controls, service schedules, and reporting dashboards.
The organization needs to compute a Medical Equipment Lifecycle Engagement Score reflecting all of these behaviors dynamically across a 165-day window. Because the score must be numeric, weighted, multi-factor, and automatically updated whenever new telemetry or service data arrives, the feature required is a measure.
Measures support time windows, aggregation of interaction data, conditional behavior weighting, and automatic recalculation. They can evaluate recency and frequency of telemetry events, calculate maintenance adherence by counting completed scheduled maintenance cycles, integrate service reports, and assign weighted contributions for portal login recency or IoT alert responsiveness.
Option A is incorrect because segments cannot compute numeric scores. They categorize customers based on attributes such as the lifecycle engagement score itself—but only after the score is computed by a measure.
Option C is incorrect because deterministic matching only merges duplicate profiles or records referring to the same customer or device. It does not compute behavioral scores.
Option D contradicts the requirement for automatic recalculation. Annual imports cannot reflect dynamic behaviors such as usage surges, alert responses, or maintenance interactions.
The nature of the engagement score demands real-time computational capability. Measures allow precisely this, integrating multiple streams of telemetry, maintenance, and engagement data into one cohesive numeric value.
Thus, the correct answer is B.
Question 129:
An international cruise-line company uses Dynamics 365 Customer Insights to unify passenger data from cabin-upgrade purchases, onboard entertainment check-ins, dining-reservation patterns, spa-service usage, mobile-app itinerary interactions, loyalty-reward redemptions, and post-cruise survey responses. Their machine-learning platform produces a monthly “Guest Experience Enrichment Probability Score” predicting which guests are most likely to purchase onboard experience upgrades or premium packages. They want this score automatically imported and aligned to unified passenger profiles to build advanced segmentation and premium-experience targeting journeys. Which Customer Insights feature should they use to ingest this score?
A) Enter the enrichment probability score manually
B) Import the predictive dataset as an enrichment table
C) Build a segment approximating enrichment probability
D) Use deterministic matching to compute enrichments
Answer: B
Explanation:
Cruise-line organizations generate extensive passenger behavior data. Cabin upgrades reflect willingness to purchase premium experiences. Entertainment check-ins show participation in shows, events, or activities. Dining reservations indicate culinary interests and spending behavior. Spa usage reflects interest in wellness experiences. Mobile-app itinerary interactions display planning engagement. Loyalty-reward redemptions show program participation. Survey responses indicate satisfaction levels.
Their machine-learning platform analyzes these signals externally to generate a Guest Experience Enrichment Probability Score. Customer Insights must import this score. Enrichment tables are designed to bring external datasets into Customer Insights and map them to unified profiles. They support periodic updates, making them ideal for monthly predictive score ingestion.
Option A is inaccurate because manual entry is not scalable.
Option C is incorrect because segmentation cannot compute probabilities, only filter on them after they exist.
Option D is incorrect because deterministic matching merges duplicate profiles, not compute machine-learning probabilities.
Thus, option B is correct.
Question 130:
A multinational industrial-machinery manufacturer uses Dynamics 365 Customer Insights to unify interactions from machine-telemetry events, predictive-maintenance sensor alerts, technician-service logs, customer-training portal usage, parts-order history, contract-renewal patterns, and billing-cycle behavior. They want to compute an “Industrial Operations Engagement Score” that measures telemetry-recency, sensor-alert responsiveness, technician-service interactions, training-portal usage depth, and parts-purchase consistency over a 160-day period. The score must refresh automatically and be stored as a numeric attribute for segmentation and proactive service-retention journeys. Which Customer Insights feature should they use to compute the score?
A) Build a segment showing highly engaged industrial clients
B) Build a measure to compute the operations engagement score
C) Use deterministic matching to unify customer locations
D) Import the score manually after contract renewal periods
Answer: B
Explanation:
Industrial-machinery clients engage with manufacturers in many complex ways. Machines produce telemetry describing usage cycles, wear indicators, performance fluctuations, and sensor triggers. Predictive maintenance alerts warn clients about part failures or required service. Technician-service logs document onsite repairs, calibrations, or inspections. Training portals teach operators to run machinery safely and efficiently. Parts orders reflect maintenance strategy. Renewal patterns show client satisfaction. Billing behavior shows reliability.
To compute an Industrial Operations Engagement Score capturing these behaviors across 160 days, Customer Insights must use a measure. Measures allow numeric scoring that recalculates dynamically as new data arrives. They can aggregate telemetry logs, compute recency, weight sensor alert responsiveness, integrate technician logs, evaluate training portal usage, and factor in parts-purchase frequency.
Option A is incorrect because segments categorize based on existing scores—they do not compute the score.
Option C is incorrect because deterministic matching merges duplicate profiles, not compute engagement metrics.
Option D is incorrect because manual imports contradict the requirement for automatic recalculation.
Thus, the correct answer is B.
Question 131:
A multinational travel-insurance corporation uses Dynamics 365 Customer Insights to unify customer interactions from online claim submissions, call-center claim-status inquiries, mobile-app policy-management activity, renewal-payment records, emergency-assistance chat logs, pre-travel medical clearance forms, and automated flight-delay alert acknowledgments. They want to calculate a “Travel Insurance Engagement Reliability Score” that evaluates claim-submission recency, payment-timeliness consistency, policy-management frequency, mobile-app interaction intensity, flight-alert responsiveness, and emergency-chat participation across 170 days. The score must refresh continuously and appear as a numeric profile attribute for segmentation and automated claim-support journeys. Which Customer Insights feature should be used to compute this score?
A) Build a segment for high-reliability travelers
B) Build a measure to generate the reliability score
C) Use deterministic matching to unify duplicate travelers
D) Import the reliability score manually every year
Answer: B
Explanation:
Travel-insurance companies generate huge volumes of structured and unstructured interactions across digital and service touchpoints. These include online claims, status inquiries, call-center escalations, chat conversations, mobile-app policy management, automated flight-delay alerts, renewal behaviors, and emergency-assistance engagements. To compute a Travel Insurance Engagement Reliability Score dynamically, the system must use a Customer Insights measure. Measures are the only component that can produce dynamic numeric values based on aggregated, time-filtered interaction data.
Claim-submission recency is essential for identifying customers who actively engage with claims processes. Measures allow you to aggregate claim events over 170 days, apply recency filters, and assign scoring weights for first-time claims versus repeat submissions. This data cannot be generated with segments alone because segments classify rather than compute.
Payment-timeliness consistency impacts customer reliability. Customers who pay premiums consistently and on time demonstrate high engagement and reliability. A measure can evaluate billing-cycle patterns, compute on-time ratios, and interpret payment deviations.
Mobile-app usage represents digital readiness. Customers who frequently open the insurer’s mobile app, update information, view policies, or check coverage details demonstrate stronger engagement. Measures can calculate the frequency and recency of app interactions and incorporate these into the score.
Flight-alert responsiveness is important because travel insurers automatically send alerts when a customer’s registered flight is delayed or canceled. Customers who acknowledge these alerts quickly display high situational awareness and responsiveness. Measures can evaluate the time between sending the alert and acknowledgment. This cannot be modeled by deterministic matching.
Emergency-chat assistance is a vital service. Customers who initiate or respond to travel-emergency chat sessions show a high reliance on insurer services. Measures can count interactions, evaluate recency, and assign values accordingly.
Pre-travel medical forms show fraud-prevention and compliance behavior. Customers who regularly submit these forms on time may receive higher engagement points within the measure.
Option A is incorrect because segments only classify customers after numeric scores exist. They cannot compute the score.
Option C is incorrect because deterministic matching only merges duplicate customer profiles. It has no analytical or scoring capability.
Option D is incorrect because manual yearly imports would not reflect the frequent behavioral updates from dynamic travel activity. Customer Insights requires continuous recalculation for such a behavioral metric.
Thus, the correct choice is a measure, making option B correct.
Question 132:
A global entertainment-streaming service uses Dynamics 365 Customer Insights to unify data from content-viewing logs, subscription-renewal patterns, mobile-app profile-switching activity, device-login telemetry, in-app search-query trends, watchlist-update behavior, and customer-support chat transcripts. Their data-science team externally produces a “Content Affinity Probability Score” every 30 days predicting the likelihood of subscribers purchasing premium add-on content packages. They want this score imported automatically each month and mapped to unified subscriber profiles for segmentation and premium-content targeting. Which Customer Insights feature should they use to ingest this predictive dataset?
A) Upload the dataset manually each month
B) Use an enrichment table to import the predictive score
C) Build a segment to approximate premium-affinity likelihood
D) Use deterministic matching to generate probability values
Answer: B
Explanation:
Entertainment-streaming platforms create vast digital interaction records that include viewing logs, profile behaviors, search queries, device telemetry, watchlist modifications, subscription history, and customer-support interactions. The predictive Content Affinity Probability Score is generated by an external data-science model, so the streaming provider must bring this dataset into Customer Insights. Enrichment tables are designed specifically to support such imports, align external numeric data with unified profiles, and update the system on a recurring schedule.
Viewing logs represent the strongest predictor of premium-content affinity. Users who watch niche genres or high-engagement categories often exhibit strong willingness to purchase associated premium content. The external model leverages this information to compute probabilities. Customer Insights cannot replicate such modeling internally, so enrichment tables become the required ingestion method.
In-app search behaviors reveal hidden intent. Subscribers frequently searching for unavailable titles may be prime candidates for targeted premium packages. When the external model produces probability scores reflecting this behavior, the enrichment table connects the predictive values directly to subscriber profiles.
Watchlist behavior shows commitment to long-term content consumption. Frequent additions to watchlists may signal interest in extended genre offerings. The predictive model uses this as a strong indicator.
Device-login telemetry shows how frequently subscribers switch devices or platforms. Highly engaged users who stream from multiple devices may have greater premium-package potential. This data informs the model but must be imported from outside.
Support chat logs show dissatisfaction or interest patterns. Highly engaged subscribers who submit requests for unavailable content often correlate with premium-add-on interest.
Because predictive models run outside Customer Insights, the resulting probability dataset must be ingested. Enrichment tables allow scheduled imports, schema mapping, profile alignment, and numeric attribute creation.
Option A is incorrect because manual uploads undermine scalability and reliability.
Option C is incorrect because segmentation cannot generate or approximate probability scores; it only filters based on attributes that already exist.
Option D is incorrect because deterministic matching has nothing to do with prediction or probability generation. It only merges duplicated subscriber identities.
Thus, enrichment tables fulfill the automatic ingestion requirement, making option B correct.
Question 133:
A global aerospace-engineering maintenance provider uses Dynamics 365 Customer Insights to unify data from aircraft sensor-telemetry, scheduled hangar-maintenance activity, pilot-reported issue logs, compliance-inspection results, parts-replacement orders, flight-operations training-portal usage, and aviation-safety hotline submissions. They want to create an “Aviation Operations Engagement Score” that captures maintenance-adherence frequency, sensor-alert responsiveness, training-portal recency, safety-hotline engagement, and parts-order patterns over the last 175 days. The score must refresh daily and be applied as a numeric attribute for aviation-safety improvement journeys. Which Customer Insights capability should they use to compute the score?
A) Classify engaged operators with a segment
B) Create a measure to compute the engagement score
C) Use deterministic matching to merge airport data
D) Import the score manually after safety audits
Answer: B
Explanation:
Aerospace-maintenance organizations work with extremely complex operational data across multiple systems. Aircraft sensor telemetry includes vibration metrics, turbine temperatures, hydraulic pressure readings, fuel-efficiency patterns, electrical system statuses, and dozens of real-time variables. Scheduled maintenance logs record routine inspections, heavy maintenance checks, hangar visits, and compliance evaluations. Pilot-reported issues often capture subtle operational anomalies. Training-portal usage reflects commitment to ongoing aviation-safety best practices. Parts-order histories reveal whether operators proactively maintain fleets. Safety hotline submissions reflect incident-reporting culture.
To compute an Aviation Operations Engagement Score across 175 days that updates daily, Customer Insights must use a measure. Measures allow numeric calculations, time-window filtering, recency weighting, and repeated recalculation whenever new data arrives.
Maintenance adherence is a major factor. Operators who consistently meet or exceed scheduled maintenance intervals demonstrate strong operational engagement. A measure can calculate the ratio of completed maintenance events to scheduled requirements during the 175-day period.
Sensor-alert responsiveness measures how quickly operators respond to telemetry-driven warnings, such as engine temperature spikes or hydraulic anomalies. Measures can evaluate alert timestamps and corresponding resolution times.
Training-portal usage highlights operator engagement with compliance standards. Frequent logins and course completions indicate commitment to aviation safety. Measures can evaluate recency and frequency.
Safety-hotline submissions show proactive reporting of potential hazards. Measures can incorporate hotline engagement into the scoring model.
Parts-order patterns reveal proactive maintenance behavior. Operators who regularly purchase preventive-maintenance parts display higher operational engagement.
Option A is incorrect because segmentation is a classification tool, not a computation engine.
Option C is incorrect because deterministic matching merges duplicate or overlapping profiles but cannot score behaviors.
Option D violates requirements for continuous recalculation because annual safety audits cannot capture daily telemetry changes.
Thus, a measure is required, making option B correct.
Question 134:
A global e-commerce marketplace uses Dynamics 365 Customer Insights to unify merchant interactions from product-listing activity, ad-campaign performance logs, seller-support case submissions, fulfillment-center check-in events, return-processing trends, storefront-customization activity, and seller-rating trends. The marketplace wants to compute a “Seller Growth Acceleration Score” that evaluates listing-volume recency, advertising-engagement depth, support-case responsiveness, storefront-customization frequency, and fulfillment-center interaction intensity across 160 days. The score must be recalculated automatically and stored as a numeric attribute for segmentation and seller-performance acceleration journeys. Which Customer Insights functionality should they use to compute this score?
A) Build a segment showing high-growth sellers
B) Build a measure that computes the growth acceleration score
C) Use deterministic matching to unify seller accounts
D) Import the score manually after quarterly reviews
Answer: B
Explanation:
E-commerce marketplaces rely heavily on data from merchant behaviors. Listing volumes indicate merchant productivity. Ad-campaign logs reveal seller-level marketing engagement. Support interactions show operational friction. Fulfillment interactions demonstrate engagement with the logistics ecosystem. Storefront customization reflects brand-building behavior. Rating trends show customer satisfaction patterns.
To compute a Seller Growth Acceleration Score, Customer Insights must aggregate multi-source interaction data across 160 days. Measures allow numeric scoring models that include recency, frequency, weighted values, and cross-behavior patterns.
Listing-volume recency is important because active sellers frequently add new products. Measures can track listing events and apply time weighting.
Advertising-engagement depth includes ad campaigns created, modified, and their performance metrics. Measures can use ad-interaction patterns to compute weighted scores.
Support-case responsiveness highlights operational reliability. Merchants who quickly resolve cases often maintain better fulfillment performance. Measures can capture responsiveness scores.
Storefront customization shows how merchants develop their brand identity. Frequent customization indicates strong engagement. Measures can evaluate the frequency and recency of customization events.
Fulfillment-center check-ins measure operational interactions. Merchants heavily involved with fulfillment programs often outperform others. Measures can capture this.
Option A is insufficient because segments only classify based on existing numeric attributes.
Option C is irrelevant because deterministic matching merges duplicates, not compute scores.
Option D is incorrect because manual quarterly imports conflict with the requirement for automatic recalculation.
Thus, a measure is required, making option B correct.
Question 135:
A multinational pharmaceutical-research organization uses Dynamics 365 Customer Insights to unify clinical-trial participant interactions from appointment-attendance logs, medication-adherence signals, wearable device telemetry, side-effect reporting submissions, patient-portal login patterns, telemedicine-session attendance, and lab-result acknowledgment behavior. They want to calculate a “Clinical Trial Digital Compliance Score” that measures adherence consistency, telemetry-recency, reporting-frequency, digital-portal interaction depth, and session-attendance patterns across 150 days. The score must refresh continuously and appear as a numeric attribute used to segment participants and manage compliance-risk journeys. Which Customer Insights feature should be selected to compute this score?
A) Create a segment identifying high-compliance participants
B) Build a measure that computes the compliance score
C) Use deterministic matching to merge participant identities
D) Import the compliance score manually during monitoring cycles
Answer: B
Explanation:
Clinical-trial environments produce extremely rich datasets through digital, medical, telemetric, and behavioral interactions. Appointment attendance logs show participant reliability. Medication adherence signals come from digital compliance tools. Wearable telemetry captures heart rate, sleep cycles, blood oxygen levels, and movement. Reporting submissions show whether participants respond actively to symptoms or side effects. Patient-portal usage reflects digital engagement. Telemedicine attendance indicates willingness to participate in virtual oversight sessions. Lab-result acknowledgment behavior reflects engagement with clinical instructions.
The Clinical Trial Digital Compliance Score must be computed using a measure because it requires dynamic, continuous scoring based on multi-source interaction data across 150 days. Measures allow weighted scoring models that refresh automatically.
Appointment-attendance consistency is crucial. Measures can calculate attendance ratios and recency.
Medication-adherence signals show whether participants take medications on schedule. Measures can evaluate adherence logs and detect patterns of consistent versus inconsistent behavior.
Wearable telemetry updates frequently. Measures can incorporate telemetry-recency and responsiveness to anomalies.
Side-effect reporting frequency reflects participant engagement and communication. Measures can count submissions and apply recency weighting.
Portal-login patterns indicate digital compliance and self-monitoring. Measures can incorporate login recency.
Telemedicine attendance reflects participant availability and cooperation. Measures can integrate session logs.
Option A is incorrect because segments cannot compute numeric scores; they classify using existing data.
Option C is unrelated because deterministic matching only merges duplicates.
Option D is incorrect because manual imports contradict the requirement for continuous recalculation.
Thus, option B is correct.
Question 136:
A multinational public-transportation authority uses Dynamics 365 Customer Insights to unify passenger behavior from mobile-ticketing scans, tap-in and tap-out station logs, transit-app route-planning activity, service-disruption alert acknowledgments, monthly-pass renewal patterns, complaint-form submissions, bus-GPS proximity notifications, and smart-card recharge activity. They want to compute a “Transit Rider Engagement Performance Score” based on travel-frequency patterns, pass-renewal timeliness, alert-acknowledgment recency, recharge-behavior consistency, route-planning depth, and support-interaction history across 180 days. The score must update automatically and appear as a numeric attribute for segmentation and ridership-loyalty optimization journeys. Which Customer Insights feature should be used to compute this score?
A) Build a segment to identify high-engagement riders
B) Build a measure to compute the rider-engagement performance score
C) Use deterministic matching to unify rider identities
D) Import the score manually every six months
Answer: B
Explanation:
Public transportation systems create large amounts of transactional, behavioral, and digital-interaction data from riders who engage through ticketing technologies, route-planning tools, support channels, smart-card systems, and emergency-alert infrastructures. The goal in this scenario is to compute a Transit Rider Engagement Performance Score using multiple inputs across 180 days. Because the desired score must be numeric, dynamically updated, recency-aware, multi-factor, and applied directly to unified passenger profiles, the correct Customer Insights feature for this requirement is a measure.
Mobile-ticket scans and smart-card tap-in or tap-out logs represent the most foundational part of transit-engagement behavior. Measures can evaluate travel frequency, determine recency, calculate usage-patterns across weekdays versus weekends, and weight scores accordingly. This type of numeric pattern recognition is not possible with segmentation alone, as segments classify rather than calculate.
Pass-renewal timeliness provides another important dimension. Riders with monthly or annual passes show clear patterns related to renewal behaviors. A measure can calculate on-time renewal ratios and produce weighted metrics that help organizations evaluate retention and loyalty trends.
Service disruption alerts are critical in public transportation, where riders must frequently adjust their journeys due to delays, rerouting, or closure events. Measures can incorporate the recency of alert acknowledgments, evaluating how actively riders engage with real-time system information. This can help enhance reliability modeling and commuter targeting.
Recharge-behavior consistency is another direct indicator of rider commitment. Riders who recharge their smart cards frequently or follow predictable recharge cycles often maintain long-term ridership. Measures can quantify recharge intervals and patterns within the 180-day window.
Transit-app route-planning activity offers visibility into user engagement outside actual travel events. Riders who plan routes frequently, experiment with new trip combinations, or check service availability contribute to system-wide engagement. Measures can factor in route-planning recency, frequency, and usage depth.
Complaint-form submissions and rider-support interactions provide qualitative and quantitative insights into how passengers interact with transit authorities when disruptions occur. Measures can include these interactions in scoring to evaluate whether riders remain engaged, dissatisfied, or seeking assistance.
Option A is incorrect because segments do not compute engagement scores; they filter passengers only after the numeric attribute already exists.
Option C is incorrect because deterministic matching simply merges multiple records for the same rider across ticketing systems, apps, and smart-card data. Matching resolves identity issues but cannot compute metrics.
Option D is incorrect because manual import contradicts the requirement for continuous recalculation. Dynamic transportation environments require real-time or near-real-time scoring.
Measures serve as the primary analytical scoring tool in Customer Insights, enabling transit authorities to transform large behavioral datasets into meaningful numeric attributes that enhance segmentation, loyalty modeling, and service optimization.
Therefore, option B is correct.
Question 137:
A global luxury-hotel chain uses Dynamics 365 Customer Insights to unify guest interactions from room-access keycard logs, spa-service reservations, dining-experience check-ins, loyalty-tier upgrade history, mobile-app concierge usage, in-room IoT device telemetry, housekeeping-preference selections, and post-stay review submissions. Their data-science platform generates a monthly “Guest Experience Expansion Probability Score.” They want this predictive score imported automatically and attached to unified guest profiles for segmentation and upsell-targeting journeys. Which Customer Insights integration method should the organization use?
A) Upload the predictive score manually each month
B) Import the dataset as an enrichment table
C) Build a segment that approximates expansion probability
D) Use deterministic matching to generate predictive values
Answer: B
Explanation:
Luxury-hospitality organizations collect data from diverse high-touch and digital-service interactions. These include spa reservations, dining engagements, loyalty-tier behaviors, mobile-concierge interactions, IoT-enabled room-preferences, housekeeping requests, and post-stay reviews. In this scenario, the hotel chain’s data-science platform produces a Guest Experience Expansion Probability Score, designed to predict whether guests may purchase upgrades, add spa packages, expand dining opportunities, or select premium concierge services. Because the score is generated externally, Customer Insights must import it through a feature designed for recurring ingestion and association with unified guest profiles: enrichment tables.
Enrichment tables allow organizations to bring external datasets—such as predictive scores, statistical models, or customer-lifetime-value forecasts—into Customer Insights on a scheduled basis. They match the external data to profiles using attributes such as guest ID, loyalty number, or email address. This ensures that predictive outputs are seamlessly integrated with segmentation systems, journey orchestration, and personalization logic.
Room-access keycard logs reveal guest movement patterns and stay-length dynamics. Although this data contributes indirectly to predictive modeling, the score itself is produced externally, making enrichment tables the required method of ingestion.
Spa-service reservations and dining check-ins provide insight into guests’ spending behaviors. Loyalty-tier upgrade patterns reveal long-term commitment to the brand. Mobile concierge usage indicates digital engagement. In-room telemetry shows comfort and automation preferences. Housekeeping selections and post-stay reviews indicate satisfaction and service expectations. These behavior signals are used by the external model to produce the probability score. However, Customer Insights cannot generate such probability values without receiving them from external systems.
Option A is incorrect because manual uploads introduce human error, delays, and inconsistency across operational cycles.
Option C is incorrect because segmentation cannot calculate probabilities. It merely filters based on criteria or pre-existing values.
Option D is incorrect because deterministic matching only resolves identity duplicates and does not compute predictive values.
Thus, enrichment tables provide the required structured ingestion pipeline, making option B correct.
Question 138:
A multinational construction-equipment manufacturer uses Dynamics 365 Customer Insights to unify contractor behaviors from equipment-rental telemetry, GPS usage logs, field-technician maintenance reports, parts-replacement orders, contractor-training-portal activity, fleet-usage scheduling patterns, and invoice-payment cycles. They want to compute a “Construction Fleet Operational Engagement Score” reflecting telemetry-recency, maintenance-compliance consistency, parts-order frequency, training-portal activity, and fleet-scheduling patterns over the last 165 days. The score must update automatically and appear as a numeric attribute used for segmentation and predictive maintenance journeys. Which Customer Insights functionality should be selected to compute this score?
A) Build a segment that identifies active contractors
B) Build a measure to compute the operational engagement score
C) Use deterministic matching to unify contractor records
D) Import the score manually during yearly operations reviews
Answer: B
Explanation:
Construction-equipment manufacturers work with equipment fleets deployed across large job sites, often under varying environmental and operational conditions. Telemetry streams from construction vehicles provide real-time insights into engine load, hydraulic system performance, fuel efficiency, idle time, and GPS movement. Maintenance reports from field technicians document repairs, inspections, and safety compliance. Parts-replacement orders show contractors’ maintenance discipline. Training-portal usage indicates whether contractors are keeping up with safety and operational best practices. Fleet-usage scheduling patterns reveal how efficiently contractors allocate heavy machinery across job sites.
The Construction Fleet Operational Engagement Score must capture these multi-source interaction behaviors across 165 days and update automatically. Customer Insights measures are designed to calculate numeric, multi-factor scores based on behavioral data. Measures support recency weighting, time-window filtering, aggregation logic, and continuous recalculation when new telemetry or interaction data arrives.
Telemetry-recency reflects how actively contractors use machinery. Measures can examine event timestamps, calculate intervals between telemetry signals, and interpret operational patterns.
Maintenance-compliance behaviors are essential in construction. Measures can evaluate whether contractors adhere to scheduled calibration, inspection, and servicing requirements.
Parts-order frequency reflects how proactively contractors maintain equipment. Measures can incorporate order volume, recency, and category types.
Training-portal activity demonstrates operation safety and compliance. Measures can incorporate login recency and course completion.
Fleet-scheduling patterns indicate whether contractors use equipment efficiently. Measures can evaluate frequency of scheduling events, cancellations, and deployment consistency.
Option A is incorrect because segments cannot compute engagement scores; they evaluate criteria after scores already exist.
Option C is incorrect because deterministic matching merges duplicate contractor profiles but cannot compute scores.
Option D violates the requirement for continuous, automated recalculation. Yearly manual import would not reflect ongoing operational behaviors.
Thus, a measure is required to compute the score, making option B correct.
Question 139:
A global online-education platform uses Dynamics 365 Customer Insights to unify learner interactions from course-module completions, quiz-attempt activity, live-session attendance logs, assignment-submission timestamps, mobile-app study engagement, certification-exam registrations, and peer-discussion forum participation. Their external data-science pipeline generates a quarterly “Learner Skill-Progression Probability Score” predicting which learners are likely to enroll in advanced certification tracks. They want this score imported automatically and mapped to unified learner profiles for segmentation and academic-progression journeys. Which Customer Insights feature should be used to ingest this predictive score?
A) Enter the predictive data manually
B) Import the dataset as an enrichment table
C) Build segments to approximate skill progression
D) Use deterministic matching to compute predictive scores
Answer: B
Explanation:
Online-education ecosystems rely heavily on large datasets that reflect learner performance, digital interaction intensity, real-time engagement with instructors, and participation in structured or community-driven learning environments. The Learner Skill-Progression Probability Score is produced outside Customer Insights by a dedicated machine-learning pipeline. Because Customer Insights does not compute predictive probabilities internally, the external output must be ingested using enrichment tables.
Course-module completions and quiz attempts reveal learner progress and mastery. Assignment-submission patterns show dedication and consistency. Live-session attendance indicates willingness to engage with synchronous instruction. Mobile-app engagement reflects study habits. Certification-exam registrations reveal readiness and commitment. Peer-forum interactions measure community involvement.
The external predictive model evaluates these interactions and produces a numeric probability value. Enrichment tables provide the structured ingestion layer for bringing this dataset into Customer Insights while matching the values to unified learner profiles based on identifiers such as learner ID, email, or enrollment information.
Option A is incorrect because manual entry would not support quarterly updates across thousands or millions of learners.
Option C is incorrect because segmentation does not generate complex probability values.
Option D is incorrect because deterministic matching resolves identity conflicts but cannot compute predictive scoring.
Therefore, enrichment tables are the correct tool, making option B the right answer.
Question 140:
A multinational healthcare-equipment company uses Dynamics 365 Customer Insights to unify interactions from device-telemetry signals, digital-portal health-tracking activity, remote-monitoring session logs, parts-replacement histories, maintenance-technician calibration reports, warranty-renewal behaviors, and IoT alert-acknowledgment timestamps. They want to compute a “Healthcare Device Lifecycle Engagement Score” evaluating telemetry activity, calibration-compliance behavior, IoT alert responsiveness, warranty-renewal consistency, and digital-portal engagement across 170 days. The score must update automatically and be stored as a numeric attribute for segmentation and proactive service and patient-safety journeys. Which Customer Insights feature should compute this score?
A) Build a segment showing highly engaged device owners
B) Build a measure to calculate the lifecycle engagement score
C) Use deterministic matching to unify device profiles
D) Import the score manually after renewal cycles
Answer: B
Explanation:
Healthcare-equipment manufacturers collect telemetry and behavioral data from medical device end users across clinical and home environments. Telemetry signals may include usage durations, battery health, system alerts, connectivity signals, device stress indicators, and operational patterns. Remote-monitoring sessions reveal how actively patients or clinicians engage with analytics dashboards. Maintenance-technician calibration logs show whether the equipment receives appropriate servicing. Parts-replacement histories reveal long-term usage and wear. Warranty renewals reflect commitment and satisfaction. IoT alert acknowledgments indicate how quickly users respond to safety-critical notifications.
To compute a Healthcare Device Lifecycle Engagement Score, Customer Insights must aggregate multi-source data over a 170-day operational window. Measures allow companies to build custom scoring models with weighted behavioral factors, dynamic recalculations, logical expressions, and event-frequency or recency evaluations.
Telemetry activity is one of the strongest indicators of real-world engagement. Measures can count telemetry events, evaluate recency, and measure usage frequency.
Calibration-compliance behavior ensures safety and accuracy. Measures can evaluate whether calibrations occur on schedule and integrate these results into the score.
IoT alert responsiveness is critical in healthcare. Measures can evaluate the time between alert issuance and acknowledgment and apply weighted scoring.
Warranty-renewal patterns give insight into user satisfaction. Measures can incorporate renewal recency and frequency.
Digital-portal engagement reflects active data monitoring and participation in device management. Measures can include login frequency and portal activity levels.
Option A is incorrect because segments cannot calculate numeric scores. They classify based on attribute values created by measures.
Option C is incorrect because deterministic matching only merges duplicate device or customer records.
Option D is incorrect because manual imports cannot support continuous updating in a safety-critical environment.
Measures remain the only Customer Insights feature capable of calculating and automatically refreshing such multi-factor engagement scores.
Thus, the correct answer is B.
