The panic in that scenario is familiar, and it's also the real problem. A week out, with a scope that wide, the instinct to boil the ocean is exactly what will sink you. You cannot master DSA, pandas, SQL, live modeling, case studies, and GenAI in seven days. Anyone who tells you otherwise is selling something. But you can build a focused prep strategy that turns that overwhelming breadth into a deliberate sequence of choices. The goal is not to cover everything. The goal is to make your strengths undeniable and your gaps manageable.
Here is what that means in practice. Start by categorizing the possible topics into three buckets: strong, shaky, and unfamiliar. Be honest with yourself. The strong bucket is where you spend most of your time, not because you need the practice, but because that is where you can create a repeatable, confident performance. For the shaky areas, pick two or three high-probability topics and drill them to a functional level. For the unfamiliar, do not try to learn them. Instead, prepare a one-sentence answer for each that shows you understand the concept's purpose, even if you cannot solve it cold. That is not a cop-out. That is triage. Interviewers can smell a week-long cram job from the first question, and they are far more impressed by someone who says, "I did not get deep into this, but here is how I would approach it," than by a rambling guess.
The behavioral round is your best friend here, and most candidates waste it. You have one week, so use the behavioral slot to anchor your narrative. Prepare three stories that demonstrate how you debugged something hard, how you made a tradeoff under pressure, and how you handled a disagreement. Those stories can be told in any technical round, regardless of whether the question is about a dataframe or a dynamic programming problem. They are your safety net. When you feel lost on a technical question, you can pivot to a story that shows your process. That is not avoidance. That is control.
The live modeling and case study rounds are where the "anything" part of the job description lives. Here is the practical move: do not try to predict the industry. Instead, practice structuring a response. For any modeling prompt, you should have a default framework: clarify the goal, state your assumptions, build a simple version first, then add complexity only if asked. For a case study, the same logic applies. The interviewer is not testing whether you know the exact formula. They are testing whether you can think in front of them without freezing. That is a skill you can practice in two hours a day for the next week. Use a timer. Do it out loud. Record yourself. That repetition will do more for your confidence than any last-minute attempt to memorize a sorting algorithm.
Here is the concrete point. Take the next hour to write down every topic from the recruiter's list. Next to each one, write one of three labels: strong, shaky, or unfamiliar. Then allocate your prep time in a 60/30/10 split across those buckets. That split is not a suggestion. It is a boundary. When you feel the urge to open a pandas tutorial because someone on the internet said it might be on the test, stop and ask yourself which bucket it belongs to. If it is unfamiliar, you already have your answer. If it is strong, you are just avoiding the hard work of polishing your delivery. The person who gets the offer is not the one who knew everything. It is the one who walked in with a plan, executed it, and made the interviewer feel like they were in good hands. That is the prep strategy. That is the confidence. Start there.