A deterministic algorithm is one that, given the same input, will always follow the same path and produce the same output. Every operation is predictable and predefined, making these algorithms easy to test, debug, and understand. In contrast, a non-deterministic algorithm may behave differently on the same input across different runs. This could be due to randomness, concurrency (e.g. thread scheduling), or interaction with external systems. In practical programming, deterministic algorithms are preferred for tasks where reliability and repeatability are essential, such as banking systems or embedded control systems. Non-deterministic algorithms are often used when exploring multiple possible solutions simultaneously, such as in artificial intelligence or optimisation problems. Understanding this difference is vital because non-deterministic behaviour can lead to inconsistencies, making it harder to identify bugs or guarantee outcomes. In examination contexts, deterministic logic is usually assumed unless specified, reinforcing its importance in developing robust, predictable software.

Handling invalid or unexpected input is critical to creating reliable algorithms. When writing an algorithm, especially one that involves user input or external data sources, you must anticipate what could go wrong. This includes inputs that are too large or small, of the wrong type, empty, or outside acceptable ranges. Defensive programming techniques include validating input before processing, using default values, or rejecting invalid data with clear error messages. Incorporating input checks early in the algorithm helps prevent runtime errors, data corruption, or incorrect outputs. Considering edge cases—those scenarios at the boundary of acceptable input—is equally important. For example, if your algorithm calculates an average, you must decide how to handle an empty list of values. Failing to do so could result in a divide-by-zero error. Edge cases often reveal hidden assumptions or flaws in logic, so addressing them improves the robustness, correctness, and usability of the program.

Separating logic from presentation is a key principle in algorithm design, particularly when algorithms are intended for integration into larger software systems. The logic of an algorithm involves the core computational steps—how the problem is solved—while the presentation deals with how results are displayed or how input is collected (e.g. using print statements or graphical interfaces). Keeping these aspects separate improves modularity, allowing the logic to be reused across multiple applications or adapted to different interfaces without rewriting the algorithm itself. It also makes testing and debugging simpler, as the algorithm can be verified independently of how results are shown to users. In practical terms, this means avoiding embedded input/output commands in algorithm logic and instead passing values in and out using parameters and return statements. This design approach also aligns with software development best practices like MVC (Model-View-Controller) architecture, and is especially important when algorithms are used as backend processes in web or mobile apps.

An algorithm's scalability refers to its ability to maintain acceptable performance levels as the size of the input grows. A scalable algorithm handles larger inputs without a dramatic increase in execution time or memory use. To assess scalability, consider how the algorithm’s behaviour changes with different input sizes—this often involves evaluating how many steps it performs relative to the input. For instance, an algorithm that processes every element in a list performs in linear time (proportional to n), while one that compares every pair of elements works in quadratic time (proportional to n squared). In practical terms, scalability becomes important when dealing with large datasets, such as those found in search engines or social media platforms. While scalability is formally analysed using time complexity analysis, students can estimate it through hand-tracing larger inputs or benchmarking performance in code. Choosing more efficient algorithms—like binary search over linear search—can drastically improve scalability.

The choice of data structure directly influences how efficiently an algorithm can solve a problem. Data structures determine how data is stored, accessed, and manipulated during execution. For example, if fast lookups are needed, a hash table (dictionary in Python) might be appropriate due to its average-case constant-time access. If you need to maintain elements in a sorted order, a list or tree may be better suited. Writing an algorithm without considering the underlying data structure may lead to inefficient operations—such as using a list to frequently search for elements, resulting in linear-time operations instead of constant time. Moreover, certain algorithms rely on specific properties of data structures; for instance, stack-based algorithms like those used in depth-first search require last-in-first-out behaviour. Optimisation opportunities often come from aligning your algorithm with the strengths of your data structure. In exam settings, it's essential to understand how different structures impact algorithmic choices, even without formal implementation.