We offer the latest Associate-Data-Modeler practice test designed for free and effective online Associate Data Modeler certification preparation. It's a simulation of the real Associate-Data-Modeler exam experience, built to help you understand the structure, complexity, and topics you'll face on exam day.
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?
In a MongoDB database tracking e-commerce orders, each order document includes fields for customer ID, order date, and a status that changes over time. To automatically purge orders older than two years and efficiently support queries for recent orders by customer ID, which combination of indexes would be most appropriate?
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?
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?
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?
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