Question 1

When optimizing a MongoDB collection's schema for a high-traffic application, what factor is crucial for determining the most effective document structure?

Options :

A : Modeling documents to reflect the application-s access patterns, prioritizing query performance and data retrieval efficiency.

B : Designing documents based on the visual appeal of the JSON structure in the database management interface.

C : Ensuring all documents in a collection have a uniform size by adding placeholder data where necessary.

D : Organizing fields in alphabetical order within documents to facilitate easier indexing.

Answer: A

Question 2

A developer is building a project management tool where each Project can have multiple Task entities. The tasks are frequently updated and accessed together with their parent project. What is the most efficient way to model this data relationship in MongoDB to optimize for task read and write operations?

Options :

A : Embed Task documents as an array of subdocuments within each Project.

B : Create a separate collection for tasks and projects, linking them through a shared projectId and taskId in a third collection.

C : Store each Task in a separate collection, with a projectId to reference back to their Project.

Answer: A

Question 3

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?

Options :

A : Create a compound index on status and deliveryDate in descending order to match the sort pattern.

B : Create an index on the deliveryDate field to optimize the sort operation.

C : Create a compound index on status and deliveryDate in ascending order.

D : Remove the sort operation to decrease the complexity of the query.

Answer: A

Question 4

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?

Options :

A : Index on { quantity: 1, productId: 1, saleDate: -1 }

B : Index on { saleDate: -1, productId: 1, quantity: 1 }

C : Index on { productId: 1, saleDate: -1, quantity: 1 }

D : Index on { productId: 1, quantity: 1, saleDate: -1 }

Answer: C

Question 5

In a blogging application, the main entity Article contains text, a title, and metadata. Each article can have multiple comments made by users. The application frequently retrieves an article along with all its comments for display. How should the developer optimize the schema for read performance?

Options :

A : Embed Comment documents directly within an Article document in an array of subdocuments.

B : Store each Comment in a separate collection and reference the Article ID in every Comment.

C : Use a third collection to manage relationships between Article and Comment documents with document references.

D : Duplicate the Article information in each Comment document to avoid joins.

Answer: A