Question 1

In the process of optimizing a MongoDB schema for a social media application that features user profiles, posts, and interactions (likes, comments), a data modeler is evaluating the use of embedded documents versus referencing. Considering the application experiences frequent reads and writes on user interactions, and the volume of interactions is high: What is the primary advantage of embedding user interactions directly within user documents instead of referencing them in separate collections?

Options :

A : Embedding interactions allows for more complex transactions across documents.

B : Embedding interactions facilitates more efficient reads by reducing the need for join operations.

C : Embedding interactions improves write efficiency by reducing the number of write operations.

D : Embedding interactions ensures that data cardinality remains low, improving indexing efficiency.

Answer: B

Question 2

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

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

In the design of a MongoDB database for an application that manages user profiles with varying degrees of completeness and privacy settings, what are the positives and negatives of relying on in-app enforcement versus schema validation to ensure data integrity and compliance with business requirements?

Options :

A : Schema validation ensures that all data writes adhere to the defined schema, reducing the need for additional application logic but lacks flexibility for handling complex conditional validation.

B : In-app enforcement is more performance-efficient because it avoids the overhead of database-level checks but can result in data integrity issues if the application crashes.

C : In-app enforcement allows for more complex logic and conditional validation but may lead to inconsistency if not implemented correctly across all application components.

D : Schema validation simplifies application code by offloading rules to the database but can lead to performance bottlenecks under heavy write loads.

Answer: C

Question 5

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

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