How do you filter documents using multiple conditions?

Data validation in MongoDB can be implemented using schema validation rules defined in collections. By specifying validation criteria using JSON Schema, you can enforce data integrity. For example, a `users` collection can have a validation rule that ensures the 'age' field is an integer between 0 and 120, preventing invalid data entries.

The `explain()` method in MongoDB provides insights into how a query is executed, helping developers optimize performance. It returns details about query execution plans, index usage, and performance metrics. For example, using `db.users.find({age: 25}).explain()` reveals if an index was used, helping to identify potential performance bottlenecks.

User roles and permissions in MongoDB are managed using role-based access control (RBAC). You can define custom roles with specific privileges. For example, `db.createRole({role: 'readWrite', privileges: [{resource: {db: 'myDB', collection: ''}, actions: ['find', 'insert', 'update']}]})` creates a role that allows reading and writing.

The MongoDB configuration file defines server settings and options for running the database instance. It specifies parameters such as port number, data directory, log file path, and replica set configurations. For example, a config file may include `storage: { dbPath: '/var/lib/mongo' }` to set the data storage location.

To query documents in MongoDB, use the `find()` method. The basic syntax is `db.collection.find({query})`. For example, `db.users.find({age: 30})` retrieves all users aged 30. You can also use conditions like `$gt`, `$lt`, and `$in` for advanced filtering.

To filter documents with multiple conditions in MongoDB, combine conditions using logical operators like `$and` and `$or`. For example, `db.users.find({$and: [{age: {$gt: 20}}, {city: 'New York'}]})` retrieves users over 20 years old living in New York.

Wildcard indexes in MongoDB enable indexing of fields within documents that may have unpredictable structures. They allow querying on any field without explicitly defining all possible fields. For instance, `db.collection.createIndex({'$**': 1})` creates a wildcard index, which is useful for collections with varying schema attributes.

Change streams in MongoDB provide a way to listen for real-time changes to documents in a collection. They allow applications to react to updates, insertions, and deletions without polling. For example, using `db.collection.watch()` lets you respond instantly to changes, enabling real-time applications like chat systems.

MongoDB Atlas is a fully managed cloud database service provided by MongoDB. It simplifies database deployment, scaling, and management, offering automated backups, monitoring, and security features. By using Atlas, developers can focus on building applications without worrying about infrastructure management, as it handles scaling and redundancy automatically.

Key differences between SQL databases and MongoDB include data structure, schema, and query language. SQL uses tables with fixed schemas, while MongoDB uses collections of documents with flexible schemas. SQL queries are structured in SQL language, whereas MongoDB uses a JSON-like query syntax, making it more intuitive for developers familiar with JSON.