Question 1

In an e-commerce application, the main entity Product includes details such as name, description, and price. Each Product can have multiple Review documents, which include ratings and customer comments. The application frequently displays products along with their reviews. How should the developer optimize the schema for read performance?

Options :

A : Embed Review documents within the Product document as an array of subdocuments.

B : Store each Review in a separate collection and use the Product ID to link to each Review.

C : Create a separate Review collection and a ProductReview join collection to manage the many-to-many relationship.

D : Store Review documents in a separate collection without storing a reference to the Product ID.

Answer: A

Question 2

A MongoDB data modeler needs to optimize a collection named orders to efficiently query documents based on the status field, which can have values like 'pending', 'completed', and 'canceled'. The collection has millions of documents, and the queries should return results quickly even under heavy load. Which of the following approaches best achieves this requirement while maintaining the collection's scalability and performance?

Options :

A : Create a separate collection for each status value and distribute the documents accordingly, querying the relevant collection based on the status.

B : Index the status field using a single field index, ensuring queries filtering by 'status' are more efficient.

C : Convert the status field into an array and use a multi-key index, ensuring multiple status values can be queried simultaneously.

D : Use a hashed index on the status field to evenly distribute the documents across the collection.

Answer: B

Question 3

In a MongoDB database, the events collection stores documents related to various events with fields like eventId, title, location, and date. The schema needs to be updated to support a new tags array field, which will store keywords related to each event. What is the most effective way to introduce this change, ensuring backward compatibility and minimal disruption to the existing application?

Options :

A : Create a separate collection named eventTags to store tags with references to their corresponding events by eventId, and adjust the application to aggregate data from both collections.

B : Introduce a schemaVersion field in the documents of the events collection, setting it to 2 for documents including the tags field, and modify the application to handle documents based on their schema version.

C : Add the tags field with an empty array as the default value to all existing documents in the events collection, requiring minimal application changes.

D : Migrate all documents to a new collection named events_v2, including the tags field for new documents, and update the application code to reference the new collection.

Answer: B

Question 4

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 5

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

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