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Explain your current project and its architecture.
Oct 2026Suggested answer AI-generated, may be wrong
In my current project, I developed a web application using Node.js for the backend, React for the frontend, and MongoDB as the database. The architecture is microservices-based, with separate services for user authentication, product management, and order processing. Each service communicates through APIs. This design allows for scalability and easy maintenance.
How does a HashMap work internally?
Oct 2026Suggested answer AI-generated, may be wrong
HashMap works by using a hash function to compute an index into an array of buckets or slots, each of which points to a linked list of key-value pairs. This allows for average O(1) time complexity for both put and get operations. If a bucket is full, collisions are handled using chaining with linked lists.
Fail fast and fail safe difference
Oct 2026Suggested answer AI-generated, may be wrong
"Fail fast" emphasizes quick iteration and testing to identify and correct mistakes early, while "fail safe" focuses on designing systems that continue to function correctly in the event of errors or failures.
Java 8 Features
Oct 2026Suggested answer AI-generated, may be wrong
Java 8 introduced several new features including Lambda expressions, which allow for more concise and functional programming. Additionally, it added Method References to facilitate the use of existing methods as lambda parameters. The introduction of Optional class helps in avoiding NullPointerExceptions by providing a safe way to handle null values.
Coding problems related to java streams
Oct 2026Suggested answer AI-generated, may be wrong
- Use `collect(Collectors.toList())` to convert stream to a list.
- Utilize `map` to transform elements and `filter` to select specific elements.
- Apply `reduce` for accumulation, e.g., sum, average, or concatenation.
- Ensure proper exception handling for robustness.
- Time complexity depends on operations, space on collection type.
Service discovery working
Oct 2026Suggested answer AI-generated, may be wrong
In a service discovery system, I implemented a distributed registry using ZooKeeper. Initially, I set up a ZooKeeper ensemble to manage service registration and discovery. I used a custom protocol to register services with ZooKeeper, ensuring each service provided a unique identifier and metadata. For discovery, I implemented a service consumer that periodically checked for updates from the registry. This approach allowed for efficient and scalable service discovery across a network. Time complexity for registration is O(log n) due to ZooKeeper's leader election, and for discovery, it's O(1) as it involves simple read operations. Space complexity is O(n) for storing service metadata in ZooKeeper.