If int[][] arr = new int[3][]; then arr[0] is a ________.
- 1D array
- 2D array
- empty array
When you declare a two-dimensional array like int[][] arr = new int[3][];, arr[0] is a 1D array that can hold integers. In this declaration, you specify the number of rows (3), but the number of columns is left unspecified, so it's an array of arrays with no specific size.
The default value of an object reference declared as an instance variable is ________.
- null
- 0
- FALSE
- TRUE
The default value of an object reference declared as an instance variable in Java is "null." When you declare an instance variable for a class, it initially points to no object until you explicitly assign an object to it. "null" signifies the absence of an object reference.
What is the default value of a local variable of data type boolean in Java?
- 1
- 0
- FALSE
- TRUE
Local variables in Java don't have default values. They must be initialized before use. However, for class-level variables of boolean type, the default value is false.
How can SQL Injection be prevented when executing queries using JDBC?
- Using Prepared Statements and Parameterized Queries
- Using a plain SQL query string with user inputs
- Escaping special characters manually in SQL queries
- Using the executeUpdate() method instead of executeQuery()
SQL Injection can be prevented in Java when executing JDBC queries by using Prepared Statements and Parameterized Queries. These mechanisms ensure that user inputs are treated as data and not executable code, thus protecting against malicious SQL injection. Options 2 and 3 are not secure and can leave the application vulnerable to attacks. Option 4 is incorrect, as it relates to result sets and not prevention of SQL injection.
Envision a scenario where you need to design a chat server for thousands of concurrent connections. How would you design the server and what Java networking APIs would you use?
- Implement a multi-threaded server using Java's ServerSocket and create a thread per connection.
- Use Java NIO (New I/O) with non-blocking sockets and a selector to efficiently manage connections.
- Use Java's SocketChannel and ServerSocketChannel with multi-threading to handle concurrent connections.
- Utilize a single-threaded server using Java's Socket and a thread pool to manage connections.
To efficiently handle thousands of concurrent connections in a chat server, Java NIO (Option 2) with non-blocking sockets and a selector is the preferred choice. It allows a single thread to manage multiple connections efficiently. Options 1 and 4, which use traditional multi-threading with ServerSocket or a thread pool with Socket, may lead to high resource consumption and thread management overhead. Option 3, although it uses NIO, suggests multi-threading, which is less efficient than a single-threaded NIO with a selector for such high concurrency scenarios.
Envision a scenario where you need to update a user’s details and also log the changes in an audit table. This operation needs to ensure data integrity and consistency. How would you achieve this using JDBC?
- Use a database transaction to wrap both the user's update and the audit log insertion, ensuring that both operations succeed or fail together.
- Perform the user update first, and if it succeeds, log the change in the audit table as a separate transaction.
- Use separate database connections for the user update and audit log insertion to ensure isolation.
- Implement a manual synchronization mechanism to ensure consistency between user updates and audit log entries.
Ensuring data integrity and consistency in this scenario requires using a database transaction to wrap both the user's update and the audit log insertion. This ensures that both operations succeed or fail together, maintaining data consistency. Performing them as separate transactions (Option 2) can lead to inconsistencies if one operation succeeds and the other fails. Using separate connections (Option 3) is not necessary when using transactions. Manual synchronization (Option 4) is error-prone and not recommended for such scenarios.
In a scenario where performance is critical, how would you decide whether to use parallel streams? What factors would you consider to ensure that the use of parallel streams actually enhances performance instead of degrading it?
- a. Always use parallel streams for better performance as they utilize multiple CPU cores.
- b. Analyze the size of the data set, the complexity of the stream operations, and the available CPU cores. Use parallel streams only if the data is sufficiently large and operations are computationally intensive.
- c. Use parallel streams for small data sets and sequential streams for large data sets to balance performance.
- d. Parallel streams should never be used as they introduce thread synchronization overhead.
Option 'b' is the correct approach. The decision to use parallel streams should be based on data set size, operation complexity, and available resources. Parallel streams may introduce overhead for small data sets or operations that are not computationally intensive. Options 'a' and 'c' are not universally applicable, and option 'd' is incorrect.
Imagine you are working on a multi-threaded application where you need to process a list of orders and then store the results in a map. Explain how you can achieve concurrency while using the Stream API.
- a. Use the parallelStream() method to process orders concurrently and collect results using Collectors.toConcurrentMap().
- b. Create multiple threads manually and divide the work among them, then merge the results into a concurrent map.
- c. Use a single thread to process orders and update a synchronized map for concurrent access.
- d. Use the stream() method and a synchronized block to process orders concurrently and store results in a concurrent map.
Option 'a' is the correct approach. It uses parallelStream() to process orders concurrently and safely stores results in a concurrent map. Option 'b' is feasible but involves more complex threading management. Option 'c' uses a single thread, which doesn't achieve concurrency. Option 'd' attempts concurrency but doesn't utilize the Stream API correctly.
Consider a scenario where you need to sort a list of employees based on their age and then return the first employee’s name. How would you achieve this using Stream API?
- a. Use the sorted() method to sort the list of employees by age, then use findFirst() to retrieve the first employee's name.
- b. Create a custom Comparator to sort the employees, then use stream().filter().findFirst() to get the first employee's name.
- c. Use stream().min(Comparator.comparingInt(Employee::getAge)).get().getName() to directly get the name of the first employee.
- d. Sort the list using a loop and compare each employee's age, then return the name of the first employee found.
In this scenario, option 'a' is the correct approach using the Stream API. The sorted() method is used to sort employees by age, and findFirst() returns the first employee's name. Option 'b' is a valid approach but less efficient. Option 'c' is concise but may throw exceptions if the list is empty. Option 'd' is inefficient and not recommended with Streams.
In a high-throughput application that processes messages, if the order of message processing is crucial, how would you design your threading model to ensure that messages are processed in order, even when multiple threads are being utilized?
- Use a single-threaded executor or a fixed-size thread pool with a single thread to process messages sequentially.
- Implement a custom message queue with a single worker thread that dequeues and processes messages in order.
- Assign each message a unique identifier, and use a priority queue with a comparator based on the message order.
- Utilize a multi-threaded executor with thread-safe synchronization mechanisms to ensure ordered message processing.
To ensure that messages are processed in order, a single-threaded executor or a fixed-size thread pool with only one thread can be used. This guarantees sequential processing. Other options may provide parallelism but won't guarantee order. Option 2 is a viable solution but not the most straightforward. Options 3 and 4 don't directly address the need for ordered processing.
Considering a real-world scenario where a thread pool is being used to manage multiple client requests to a server, what could be the potential issues if the thread pool size is too small or too large? How would you determine an optimal thread pool size?
- Too small thread pool size can lead to resource underutilization and slow response times, while too large a thread pool can consume excessive resources and cause contention. Optimal size depends on factors like available CPU cores and the nature of tasks.
- Too small thread pool size can lead to excessive thread creation overhead, while too large a thread pool can cause high memory usage and thread contention. Optimal size depends on the task execution time and CPU core count.
- Too small thread pool size may lead to thread starvation, while too large a thread pool can consume excessive memory. Optimal size depends on the number of clients and available memory.
- Too small thread pool size can lead to low throughput, while too large a thread pool can cause excessive context switching. Optimal size depends on the task's CPU and I/O requirements.
The optimal thread pool size depends on various factors such as the nature of tasks, available CPU cores, and memory resources. A too small thread pool may result in underutilization, while a too large one may lead to resource contention. Determining the optimal size requires careful analysis and possibly performance testing. Option 2 provides a more accurate description of potential issues related to thread pool size.
Consider a scenario where you are tasked with designing a distributed application where objects need to be serialized and transmitted over the network. How would you optimize the serialization process to ensure minimal network usage and maximize performance?
- Use binary serialization formats like Protocol Buffers or Avro that are highly efficient in terms of both size and speed.
- Implement custom object pooling and reuse mechanisms to minimize the overhead of creating and serializing objects.
- Utilize data compression techniques during serialization to reduce the size of transmitted data.
- Implement lazy loading and on-demand deserialization to transmit only the necessary parts of objects over the network.
In a distributed application, optimizing serialization is crucial for minimizing network usage and maximizing performance. Option 1 is the correct choice because binary serialization formats like Protocol Buffers and Avro are known for their efficiency in terms of both size and speed. Option 2 and 4 are helpful but address different aspects of optimization. Option 3 focuses on data size but doesn't address serialization speed.