Question 1

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 2

To improve the performance of queries on a logEntries collection, a MongoDB developer needs to find and analyze slow queries that have been executed. Which command should be used to identify and provide details on slow operations for analysis?

Options :

A : db.adminCommand({ "getLog": "global" })

B : db.setProfilingLevel(2, { "slowms": 100 })

C : db.logEntries.find({ "slowQuery": { $exists: true } })

D : db.logEntries.aggregate([ { $currentOp: { "slowOp": true } } ])

Answer: A

Question 3

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 4

In a MongoDB database for an online bookstore, you're designing a data model to handle books, authors, and reviews efficiently, considering a complex scenario involving relationships and potential data skew due to varying numbers of reviews per book. Given the requirements:Each book can have multiple authors.Each author can write multiple books.Books can have a large number of reviews, but most have a few or none, creating potential data skew.The application frequently queries books with their authors and a sample of reviews to avoid performance issues due to data skew.How should you model the relationships among books, authors, and reviews to optimize for query performance and data management?

Options :

A : Embed authors and a fixed number of top reviews in each book document.

B : Use a separate collection for reviews and link them to books with a book ID. Authors and books are embedded within each other-s documents.

C : Reference authors and reviews in the book document with foreign keys.

D : Embed books within author documents, including all reviews for each book.

Answer: A

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

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