What Karat Actually Is
Karat is a synchronous video interview platform. Companies use it to run live one-on-one interviews between candidates and real people — usually engineers or hiring managers — but the recording and evaluation happen asynchronously on the backend. Walmart adopted it as part of their technical screening pipeline for software engineering roles. The platform records your answers, scores them against a rubric, and passes the results to recruiters. The questions themselves are straightforward but heavily timed. You will typically see three to five items in a single session, ranging from basic coding problems to system design prompts. The format is less intimidating than LeetCode hard problems but more structured than a whiteboard session. You get around 20 to 40 minutes total, depending on the role level. The harder questions show up when the role is L4 or above. I went through a Karat interview for a Walmart supply chain engineering position back in early 2024. The session had three questions: one array manipulation problem, one about designing a rate limiter, and a behavioral follow-up. What threw me off was not the difficulty — it was the pacing. The timer on each question was strict, and there was no pause between them. I lost five minutes on the first problem because I did not account for empty input arrays, which cost me on the second question where I ran out of time.
The workaround I ended up using was simple: I stopped trying to write perfect code and started writing pseudo-code faster, then explained the edge cases out loud while typing. The interviewer was listening, and they cared more about the thinking process than the final syntax. That approach got me through the second question with two minutes to spare.
How the Platform Works Under the Hood
Karat uses a web-based interface with a shared coding environment. Both you and the interviewer see the same editor. There is a built-in compiler that runs your code in real time. The recording captures your screen, voice, and sometimes your camera feed. After the session, the interviewer fills out a scorecard covering correctness, communication, and problem decomposition. One thing most candidates do not realize is that the scoring is heavily weighted toward how you handle ambiguity. Walmart interviewers are trained to look for whether you ask clarifying questions before diving in. If you start coding immediately without confirming constraints, you lose points even if your solution is correct. I learned this the hard way during my own session — I jumped straight into the array problem and only realized after the fact that I should have asked about input size and whether negative numbers were possible.
Get the Full Details
Practical Strategy That Actually Works
Here is the process I recommend. Do not memorize solutions. Instead, practice speaking your thoughts while coding under time pressure. Use a timer set to 15 minutes per question and force yourself to talk through edge cases before writing a single line. The biggest mistake I see is candidates staying silent for three or four minutes while they think, then blasting out code in the last 90 seconds. That signals poor communication skills to the evaluator. Another detail that matters: the platform supports multiple languages, but Python and Java are the most stable. I tried C++ once and the compiler warnings cluttered the output enough to distract both me and the interviewer. Stick to what you know well. Also, test your camera and microphone five minutes before the scheduled slot. The platform does not have a great fallback if your audio cuts out mid-session, and restarting takes time you do not have.
What the Scoring Really Looks Like
Walmart uses a four-point scale for Karat evaluations. A score of 3 means competent and hireable for the level applied to. A 2 is borderline and usually requires a second interview to resolve. A 1 is a clear no. The rubric covers three categories: technical correctness, problem-solving approach, and communication quality. Technical correctness gets the most weight, but it is common for a candidate to pass on technique alone if they communicate clearly and recover from initial mistakes. I interviewed candidates through Karat for my own team afterward, and I can tell you that the strongest performers were the ones who admitted when they were stuck rather than fumbling silently. They would say something like "I am not sure about the boundary condition here, so I will assume non-negative integers" and then proceed. That kind of clarity moved the needle significantly, even when the code had a small bug.
Where Karat Falls Short
The platform has real limitations. The shared editor does not support custom libraries well, so if you need something like numpy or a specific utility class, you are out of luck. Network latency is also a factor — if your connection drops, you lose progress on the current question and cannot easily switch to a backup problem. I had one candidate whose screen froze during the rate limiter question, and the session ended with an incomplete submission that hurt their score unfairly. For preparation, I recommend focusing on the types of problems that actually show up: arrays, strings, hash maps, basic tree traversal, and simple system design prompts like URL shorteners or cache eviction. Avoid spending time on dynamic programming or graph algorithms unless the role specifically mentions them. Walmart Karat interviews rarely go that deep for mid-level positions. If you want to simulate the real environment, use platforms like Pramp or interviewquery.com to practice with a live partner under timed conditions. That gives you the closest approximation to the pressure of the actual Karat session. Reading through common question lists helps, but nothing replaces practicing out loud while someone watches you code.
