Free MongoDB Associate-Data-Modeler Exam Questions

In MongoDB, how should the analysis of query and analytics patterns influence the decision on indexing strategies, particularly with respect to the operational context of queries being part of a transaction?

A team is developing a logistics application using MongoDB to track shipments in real-time. Each shipment record updates frequently with GPS coordinates, and the system requires immediate access to the latest location data for efficient routing and delivery updates. The application also needs to maintain a compact record of each shipment's path without unnecessary data duplication. Which MongoDB feature or design pattern should be used to model the shipment tracking system?

A data modeler is tasked with enhancing query efficiency for the salesRecords collection. Considering the queries:

db.salesRecords.find({"productId": 15}).sort({"saleDate": -1, "quantity": 1})

and

db.salesRecords.find({"saleDate": {"$gte": ISODate("2023-06-01")}}).sort({"productId": 1, "quantity": -1})

which indexing strategy would be most effective in improving response times for both queries?

A financial trading platform stores transaction data in MongoDB, including trade details, user portfolios, and real-time market data. The platform must efficiently process and display up-to-date portfolio valuations, historical trades, and market trends. Given the critical need for low-latency responses to support real-time decision-making and historical analysis, which MongoDB schema design pattern should be prioritized to optimize the platform's most critical data access patterns?

In MongoDB schema design, what is a critical implication to consider when adding another piece of data into an array field, especially in the context of document size limitations and performance?