Free MongoDB Associate-Data-Modeler Exam Questions

A MongoDB data modeler is optimizing the performance of an application that frequently executes the following query:

db.orders.find(

   { status: "shipped", deliveryDate: { $lte: new Date("2023-12-31") } }

).sort({ deliveryDate: -1 })

The explain method reveals the following key details indicating the query is not efficiently utilizing indexes:

{

   "queryPlanner": {

      ...

      "winningPlan": {

         "stage": "SORT",

         "sortPattern": { "deliveryDate": -1 },

         ...

         "inputStage": {

            "stage": "FETCH",

            "inputStage": {

               "stage": "IXSCAN",

               "keyPattern": { "status": 1 },

               ...

            }

         }

      }

   },

   "executionStats": {

      "executionSuccess": true,

      "nReturned": 100,

      "executionTimeMillis": 120,

      "totalKeysExamined": 500,

      "totalDocsExamined": 500,

      ...

   },

   ...

}

What action should the data modeler take to enhance the performance of this query?

In a MongoDB database tracking e-commerce orders, each order document includes fields for customer ID, order date, and a status that changes over time. To automatically purge orders older than two years and efficiently support queries for recent orders by customer ID, which combination of indexes would be most appropriate?

In designing a data model for a new online bookstore application, you are tasked with structuring the relationship between Books and Authors. Each book can have one or more authors, and each author can write multiple books. Considering the requirement for fast reads and updates of both books and their associated authors, which of the following data modeling patterns would be most optimal?

In a MongoDB database for an online bookstore, you're designing a data model to handle books, authors, and reviews efficiently, considering a complex scenario involving relationships and potential data skew due to varying numbers of reviews per book. Given the requirements:

Each book can have multiple authors.

Each author can write multiple books.

Books can have a large number of reviews, but most have a few or none, creating potential data skew.

The application frequently queries books with their authors and a sample of reviews to avoid performance issues due to data skew.

How should you model the relationships among books, authors, and reviews to optimize for query performance and data management?

A startup is designing a new MongoDB database to store user information and their respective device data. Each user can have multiple devices, and each device can generate daily usage statistics. The startup's application needs to frequently access a user's profile and their device's latest usage statistics. Considering scalability, efficiency, and security, which data model would best suit the startup's requirements?