The Top 20 Core Data Interview Questions for iOS Developers

As an iOS developer, you need to have a solid grasp of Core Data to build robust, data-driven apps. During job interviews, expect questions that evaluate your practical skills and conceptual understanding of this key framework In this guide, I’ll share the 20 most common Core Data interview questions and sample answers to help you ace your next interview

1. What is Core Data and what are its key components?

Core Data is Apple’s object graph and persistence framework for managing model layer objects in iOS and macOS apps The key components are

  • Managed Object Model – Describes the data types in your app. Like a database schema.
  • Managed Object Context – Tracks changes to objects, handles undo/redo.
  • Persistent Store Coordinator – Manages stores, handles loading data and schema migration.
  • Persistent Store – Represents on-disk storage – SQLite, binary, In-Memory, etc.

Together, these components allow you to focus on your app’s objects while Core Data handles the details of data persistence.

2. Explain how Core Data saves data – from Managed Object Context to Persistent Store.

When you modify a managed object, the Managed Object Context tracks these changes in its undo manager. When you invoke save() on the context, it pushes changes to the Persistent Store Coordinator.

The coordinator validates the changes against the Managed Object Model schema. If it’s correct, it sends the serialized data to the right persistent store to be saved on disk.

This leads to an atomic commit, where either all changes are saved successfully or, if an error happens, none of them are applied. This maintains data consistency.

3. How is Core Data schema migration handled?

Schema migration allows seamless updates to the data model as the app evolves. It involves:

  • Adding a new model version in Xcode.
  • Enabling lightweight migration to automatically map simple changes.
  • For complex changes, creating a Mapping Model file to manually specify mapping between source and destination models.

In NSPersistentStoreDescription, the Migration Policy tells us how to move data between stores.

4. Explain Parent-Child Object Contexts and their benefits.

Parent-Child Object Contexts allow main queue and private queue contexts to work together:

  • Main queue context focuses on UI work. Child contexts do background data tasks.
  • Improves concurrency and reduces main thread blocking.
  • Better memory management since child only loads necessary data.
  • Undo handling is separate for each context.

This improves responsiveness and efficiency in large, complex apps.

5. How is multithreading handled in Core Data?

Core Data uses confinement, thread or queue contexts for multithreading:

  • ConfinementContext – Restricts to one thread.
  • MainQueueContext – For main thread.
  • PrivateQueueContext – Creates own dispatch queue.

This enables background data tasks while keeping UI responsive. Changes are merged between contexts to maintain data consistency.

6. When would you use in-memory store versus SQLite persistent store?

In-memory stores are useful for:

  • Caching temporary data that doesn’t need persistence.
  • Read-only or test data that is pre-populated on launch.

SQLite persistent stores are better for:

  • Large, permanent datasets that require full data persistence.
  • Frequent reads/writes for performance.
  • Leveraging SQLite’s querying and indexing capabilities.

So choose in-memory for short-lived caches and SQLite for your critical, persistent app data.

7. Explain strategies for handling large datasets without degrading performance.

Strategies include:

  • Batching – Fetch objects in batches rather than all at once.
  • Predicates – Filter fetch requests to load only needed data.
  • Background context – Shift heavy loads off main thread.
  • Faulting – Delay loading data until accessed.
  • FetchedResultsController – Efficiently manage table/collection data.

Carefully structuring and limiting fetch requests is key to performance with large data volumes.

8. How can you validate data before committing changes in Core Data?

Core Data provides both automatic and custom validation:

  • Automatic – Ensures mandatory attributes aren’t nil on commit.
  • Custom – Override validateForInsert, validateForUpdate, etc to add logic.

When validation fails, Core Data returns specific errors that you can handle appropriately, such as displaying error messages to the user.

9. Explain the NSFetchedResultsController and its benefits.

NSFetchedResultsController efficiently manages results from a fetch request to drive UITableView/UICollectionView.


  • Organizes results into sections with headers/footers.
  • Monitors changes and updates results.
  • Delegate methods handle updates after data changes.
  • Avoids need to load all objects into memory.

This makes NSFetchedResultsController ideal for apps that display long lists or grids of Core Data objects.

10. How can you implement undo support with Core Data?

The Managed Object Context provides undo/redo through its undo manager. Core Data tracks changes as undo actions, storing them on the context’s undo stack.

To leverage undo:

  • Set context.automaticallyMergesChangesFromParent = true
  • On save failure, call context.rollback() to revert changes.
  • Implement NSUndoManagerDelegate methods to respond to undo/redo.

Undo support is especially useful for editing interfaces that need to revert accidental changes.

11. What are the differences between Core Data and SQLite?

Core Data is an object graph and persistence framework. SQLite is a relational database. Key differences:

  • Core Data layers on top of SQLite, providing object mapping, relationship management, undo support etc.
  • SQLite offers raw SQL power and multi-threading capabilities.
  • Core Data better suits apps dealing with objects, UI and heavy data relationships.
  • SQLite excels at complex querying and analytics on large tabular datasets.

So Core Data solves higher-level data management while SQLite handles lower-level storage and querying.

12. Explain the role of the Managed Object Model in Core Data.

The Managed Object Model defines the schema for data in the application:

  • Includes Entity definitions – like tables.
  • Attributes define properties on entities – like columns.
  • Relationships connect entities – similar to foreign keys.

This object model represents the app’s data types and structure. The Persistent Store Coordinator uses this model to manage schema and validate changes.

13. How can you filter Core Data results using NSPredicate?

To filter fetch requests, create an NSPredicate with format strings, arguments and clauses:

let pred = NSPredicate(format:"age > %d AND name BEGINSWITH %@", 21, "A")request.predicate = pred

Common examples:

  • Basic comparisons – age >= 30, name != 'John'
  • Complex clauses – age > 20 AND (name BEGINSWITH 'A' OR name BEGINSWITH 'B')
  • Comparing attributes – age > salary/2000

Predicates allow fetching only the data you need.

14. What are the NSFetchedResultsController delegate methods used to update UITableView/UICollectionView?

Key methods are:

  • controllerWillChangeContent(_:) – Called before updates.
  • controller(_:didChange:atIndexPath:for:newIndexPath:) – Reports specific changes.
  • controllerDidChangeContent(_:) – Called after updates finish.

In these methods, insert/delete/reload rows or sections accordingly. This keeps the UI synchronized with data changes.

15. Discuss strategies for syncing Core Data across multiple devices.

Strategies include:

  • iCloud – Core Data integrates directly but has limitations.
  • Third party libraries – Robust sync but often paid.
  • Custom server sync – Flexible but complex to implement.

The best approach depends on sync requirements, security needs and resources. A hybrid can combine iCloud’s convenience with a custom server handling selective sync for sensitive data.

16. How can you improve Core Data performance in large apps?


  • Analyze costly fetches and add predicates to restrict results.
  • Use batch fetching to progressively load objects as needed.
  • Shift intensive operations to background contexts.
  • Implement caching and prefetching strategies to optimize data access.
  • Profile repeatedly during development to catch any degradation.

Depending on bottlenecks, scaling hardware or optimizing SQLite indexes could also help.

17. What are the pros and cons of using Core Data?


  • Robust model layer persistence framework.
  • Simplifies object lifecycle and relationships.
  • Migrations, validation, undo management and iCloud sync.


  • Steep learning curve.
  • Can be misused/abused resulting in sluggish apps.
  • iOS/macOS only – limits cross platform code reuse.

When applied correctly, Core Data

core data interview questions

Can you provide an example of how you would use NSIncrementalStore?

NSIncrementalStore is used for integrating Core Data with external data sources. One way I might use it is to create a unique incremental store that links to an API that gives me data from outside sources.

First, I would create a subclass of NSIncrementalStore and implement the required methods for retrieving and saving data. Next, I would define a mapping between the API’s data schema and my Core Data model. If I set this up, I could get data from the API and store it in the local Core Data store.

When the external data source is updated, NSIncrementalStore would get only the changes and make the necessary changes to the local Core Data store. This would make it easy for the data from the outside source to be synced with the data in the local Core Data store.

To show that this method works, I would set up a performance test that would compare the sync times between using this custom NSIncrementalStore and getting data directly from the external API. I would expect to see a significant improvement in sync times by utilizing NSIncrementalStores incremental data fetching capabilities.

What is NSFetchedPropertyDescription and how would you use it?

A class below NSPropertyDescription describes properties that are fetched as part of a fetch request or as part of the content of an object relationship fault. It allows you to define a property that fetches its value based on a fetch request. In simple terms, it provides the ability to fetch additional data lazily.

For example, imagine an app that has a list of employees and their departments. You may have a department entity and an employee entity, with a to-many relationship from department to employee. You could use an NSFetchedPropertyDescription to count how many employees are in each department if you want to show that number:

  • Fill out the NSFetchedPropertyDescription with a name, the name of the entity, and the fetch request.
  • The request to fetch should get the employee entities with a condition that sorts them by department.
  • Change the type of the result to an integer to get the number of employees.
  • Add the NSFetchedPropertyDescription to the department entity.
  • When you get a list of departments, each one will now have the number of employees listed in it.

Using an NSFetchedPropertyDescription can save time and memory by lazily fetching additional data only when needed. It also allows you to aggregate data across relationships, which can be useful for generating reports or statistics.

iOS Data Persistence Strategies | Interview Questions with Answers

What does a data analyst interview look like?

Technical Screen: This part is specific to data analyst roles. The technical interview can involve SQL and Python questions or a take-home test. On-site interview: The final step tends to focus on your business acumen. Once you have passed through these core parts of the interview process, you may have to wait for an offer.

How does core data work?

Core Data automatically resolves (fires) the fault when you access data in the fault. This lazy loading of the related objects is much better for memory use, and much faster for fetching objects related to rarely used (or very large) objects.

How do you answer a data analytics interview question?

Despite being a relatively simple question, this one can be hard for many people to answer. Essentially, the interviewer is looking for a relatively concise and focused answer about what’s brought you to the field of data analytics and what interests you about this role.

How do you answer a data cleaning interview question?

Data cleaning (also known as data preparation or data cleansing) takes up a large part of your work hours as a data analyst. When you answer this question, you can show the interviewer how you handle the process. You’ll want to explain how you handle missing data, duplicates, outliers, and more.

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