Grinding through hundreds of LeetCode problems is the default advice everyone gives, but it's not really what separates a strong interview from a mediocre one. In a real technical interview, you're not just solving a problem ā you're understanding it, explaining your reasoning, writing working code, analyzing its complexity, and responding on your feet when the interviewer pushes further. Solving problems matters, but so does how you communicate your way through them.
Start With the Problem, Not the Code
The fastest way to sink an interview is to start typing before you've actually understood what's being asked. Take a moment first to pin down the basics: What does the input look like, and what should come out? Could there be duplicates, or could the input be empty? What are the constraints, and is the data already sorted? Getting these details straight up front saves you from confidently solving a problem you were never actually asked.
Talk Through the Brute-Force Approach First
You don't need to leap straight to the optimal solution ā in fact, doing so can make for a worse interview, not a better one. It's often more useful to start with the straightforward approach: if you're looking for relationships between elements in an array, for instance, you might begin by considering every pair. Walk through the complexity of that approach ā if it comes out to O(nĀ²), ask yourself whether the structure of the problem gives you room to cut down on the work. This kind of thinking shows the interviewer how you optimize, rather than just that you memorized the right answer in advance.
Learn to Recognize the Right Data Structure
A lot of problems become far more manageable once you spot which data structure they're really asking for:
Hash maps shine at frequency counting, fast lookups, duplicate detection, and mapping keys to values.
Stacks are your go-to for parentheses matching, monotonic stack problems, backtracking, and anything involving nested structures.
Queues handle BFS, level-order traversal, and scheduling-style problems.
Heaps are built for top-K problems, priority-based processing, and tracking running minimums or maximums.
Graphs come up whenever the problem is really about relationships, networks, dependencies, or traversal.
The more of these patterns you internalize, the faster you'll recognize what's actually going on in an unfamiliar problem.
Reason Through Time Complexity
Once you've got an approach, sit with its complexity for a moment ā is it O(n), O(n log n), O(nĀ²)? ā and be ready to explain why. Ask yourself how many passes you're making through the input, whether you're sorting anywhere, whether there are nested loops, and how much extra memory you're using. This kind of analysis becomes especially important the moment an interviewer asks you to make your solution faster.
Don't Skip Edge Cases
Code that handles the obvious example can still quietly fall apart elsewhere. Before you call a solution done, run it mentally against the edge cases: empty input, a single element, duplicates, very large inputs, negative values, data that's already sorted (or reverse-sorted), missing values. You don't have to write a formal test for each one, but you should be able to show the interviewer you've thought about them.
Expect the Follow-Up Questions
Getting a working solution rarely ends the conversation. Interviewers like to push further: Can you make it faster? Can you cut the memory usage? What if the input were huge, or arrived as a stream instead of all at once? Could you solve it without sorting? These questions exist to probe whether you actually understand the trade-offs in your solution, so it's worth knowing more than one way to approach a given problem rather than banking on a single memorized path.
Practice Thinking Out Loud
Working through a problem silently in your head is a completely different skill from narrating your way through one live. When you practice, say it out loud: restate the problem, call out the constraints that matter, describe your first approach, explain how you'd optimize it, write the code, test it, then talk through the complexity. The closer this gets to how a real interview actually feels, the less foreign the real thing will be.
Practice by Pattern, Not at Random
Working through problems in no particular order isn't the most efficient way to prepare. You'll get more out of your time by grouping problems into patterns instead:
Pattern Common Topics
Hashing Frequency, lookup, duplicates
Two Pointers Arrays, strings
Sliding Window Subarrays, substrings
Binary Search Sorted data
BFS / DFS Trees, graphs
Dynamic Programming Optimization problems
Intervals Scheduling and ranges
Heap Top K and priority problems
Once these patterns click, unfamiliar problems stop feeling quite so unfamiliar.
Don't Neglect the Non-Coding Half
Coding is only one piece of an Amazon SDE interview. Depending on the role, you'll likely also face behavioral questions tied to Amazon's Leadership Principles ā so a prep plan that's 100% algorithms is an incomplete one. A more balanced plan makes room for coding practice, mock interviews, behavioral stories, review of the Leadership Principles, and a polished resume.
Final Thoughts
In the end, preparing for an Amazon SDE interview has less to do with memorizing a stack of solutions and more to do with building a repeatable way of thinking through problems ā understanding requirements, picking the right data structure, weighing trade-offs, writing clean code, reasoning about complexity, catching edge cases, and handling whatever follow-up comes your way. Get comfortable with that process, and it'll serve you well beyond this one interview too.
