Question 1

A mobile application tracks user activities, interactions, and engagement metrics to offer personalized content and recommendations. To optimize the application's performance, particularly for generating monthly engagement reports and user insights, a data modeler is tasked with designing an efficient data storage strategy. What approach should the data modeler adopt to best meet these requirements?

Options :

A : Maintain separate collections for users, activities, and interactions, calculating engagement metrics and insights in real time upon each query.

B : Use a single collection for activities and employ on-the-fly aggregation queries for generating user engagement reports and insights whenever needed.

C : Store all user data, including activities and interactions, as embedded documents within a single user document in a single collection.

D : Utilize separate collections for Users, Activities, and EngagementMetrics. Use the computed pattern to periodically update EngagementMetrics with aggregated data.

Answer: D

Question 2

A financial trading platform stores transaction data in MongoDB, including trade details, user portfolios, and real-time market data. The platform must efficiently process and display up-to-date portfolio valuations, historical trades, and market trends. Given the critical need for low-latency responses to support real-time decision-making and historical analysis, which MongoDB schema design pattern should be prioritized to optimize the platform's most critical data access patterns?

Options :

A : Outlier Pattern

B : Computed Pattern

C : Versioning Pattern

D : Subset Pattern

Answer: B

Question 3

Regarding MongoDB schema design and document structure, which two of the following statements are accurate?

Options :

A : All documents within a collection must have the same fields and data types.

B : Embedding documents is recommended for frequently accessed data that is read together.

C : Schema validation rules are enforced by default on all collections upon creation.

D : Denormalization can reduce the need for expensive joins and improve read performance.

Answer: B,D

Question 4

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 5

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