Question 1

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?

Options :

A : Embedding authors within books documents.

B : Storing all books and authors in a single document.

C : Referencing authors from books documents by storing author IDs in books.

D : Using a separate collection for each author-s books.

Answer: A

Question 2

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 3

In designing a catalog application, a developer is dealing with Product entities that have multiple Review entities. The application frequently displays products along with the top three reviews based on ratings. How should the developer model this data to optimize for performance?

Options :

A : Use a separate collection to maintain a list of product IDs and their associated top three review IDs.

B : Embed the top three Review documents within each Product document.

C : Store each Review in a separate document, linking them to their respective Product via a product ID.

D : Embed all Review documents within their respective Product document.

Answer: B

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

In the context of a MongoDB database application for an online news platform, the data modeler is tasked with optimizing the storage and retrieval of news articles that are frequently accessed by readers. The articles undergo occasional updates by the editorial team and include metadata such as publication date, author, and a set of tags. The application has a high read-to-write ratio, with the need for efficient retrieval of articles based on tags and publication dates. What data modeling strategy should be adopted to maximize query performance for retrieving articles based on metadata?

Options :

A : Adopt the Bucket Pattern, grouping articles into buckets by publication month or year.

B : Apply the Reference Pattern, creating separate collections for articles and metadata, with references between them.

C : Employ the Indexing Pattern, creating indexes on tags and publication dates within the articles collection.

D : Implement the Embedded Documents Pattern, embedding metadata within each article document.

Answer: C

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