Free MongoDB Associate-Data-Modeler Exam Questions

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?

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?

To improve the performance of queries on a logEntries collection, a MongoDB developer needs to find and analyze slow queries that have been executed. Which command should be used to identify and provide details on slow operations for analysis?

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?

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?