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The coordinate system tripped me up for a second because [0,0] being bottom-left means you have to flip the row index.
First, parse the input to extract the coordinate header and the grid of characters. Then, convert the given coordinates from a bottom-left origin to the corresponding row and column indices in the grid, and return the character at that position. Handle multiple coordinates if specified.
Pro tip: Clarify the coordinate system and indexing assumptions upfront, and consider edge cases like out-of-bounds coordinates or malformed input to demonstrate thoroughness.
Extract the coordinate header (e.g., '[2,4]') and the subsequent rows of characters. Ensure the header format is correctly interpreted.
Store the rows in a list or 2D array, noting that the first row after the header is the top row of the block.
For a given (x, y) with origin at bottom-left, compute the row index as (number of rows - 1 - y) and the column index as x.
Access the character at the computed row and column. If multiple coordinates are provided, repeat for each.
Check for out-of-bounds coordinates, empty grid, or malformed input, and decide on appropriate error handling or return values.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
This is where I started to feel the pressure.
First, clarify the input format and constraints (e.g., block structure, index range, duplicates). Then, design a parsing strategy to extract the index and character from each block, and use an array or hash map to place characters at their indices, handling potential gaps or duplicates. Finally, reconstruct the password by iterating over the sorted indices or the array in order.
Pro tip: Mention that you would validate the input to ensure indices are unique and within bounds, and discuss how to handle missing indices (e.g., if the password is incomplete). This shows attention to edge cases and robustness.
Ask questions to understand the exact format of each block (e.g., how the integer and character are delimited), whether indices are 0-based or 1-based, and if there can be duplicate indices or gaps.
Iterate through the input, splitting each block into its integer index and character. Store these pairs in a list or directly place them into a data structure.
Use an array (if indices are dense and known) or a hash map to map each index to its character. Check for conflicts (duplicate indices) and handle them (e.g., overwrite or error).
Determine the maximum index to know the password length. Iterate from the smallest to largest index (or 0 to max) and concatenate characters, filling gaps with a placeholder or skipping if allowed.
Consider empty input, invalid indices (negative or too large), duplicate indices, and missing indices. Discuss how to handle them (e.g., throw error, ignore, or fill with default).
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Use a hash set to track indices seen so far while iterating through the blocks. When you encounter an index already in the set, you've found the start of a new password; return the password accumulated from the previous start up to (but not including) this repeated index. If no repetition occurs, return the entire accumulated password.
Pro tip: Clarify with the interviewer whether the repeated index itself belongs to the new password or the old one, as this off-by-one detail often determines correctness. Also discuss handling edge cases like no repetition or empty input.
Confirm that blocks are read sequentially and that a repeated index marks the beginning of a new password. Ask whether the repeated index is included in the new password or excluded from the old one.
Use a hash set to store indices seen in the current password, enabling O(1) lookups. Maintain a list or string builder to accumulate the current password.
Process each block one by one. For each index, check if it's already in the set. If not, add it and append the block to the current password. If it is, stop and return the current password.
Consider scenarios where no repetition occurs (return the entire password) or where the input is empty (return an empty string). Ensure the solution works for the first password only.
State that the time complexity is O(n) where n is the number of blocks read until repetition, and space complexity is O(k) where k is the number of unique indices in the password.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Basically a graph traversal disguised as a simple lookup problem.
Model the assignments as a directed graph where each variable points to its dependencies, then perform a depth-first search from the target while memoizing resolved values. Handle cycles by detecting back edges and either returning an error or a sentinel value.
Pro tip: Clarify upfront whether cycles are possible and how they should be handled; this shows you think about edge cases and prevents incorrect assumptions. Also, mention that memoization avoids redundant work and makes the solution efficient.
Ask if the assignments are guaranteed acyclic, what to do if the target is unassigned, and whether values can be non-numeric. This ensures you handle all scenarios.
Parse the assignments into a map from variable to its dependency (or numeric value). Represent each variable as a node with an edge to the variable it depends on.
Starting from the target, recursively resolve dependencies. Use a memo table to store computed values and a visiting set to detect cycles.
If a cycle is detected, return an error or a designated value. If a variable has no assignment, treat it as unresolved or return an error.
Once the target's value is computed, return it. Optionally, discuss iterative approaches (e.g., topological sort) as alternatives.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Pretty straightforward extension once the base case works.
First, clarify the problem: given a set of assignments where each can be a direct copy or a single addition/subtraction of two previously assigned variables, determine if a target assignment is possible. Then, model the dependencies as a directed graph and use topological sorting or DFS with memoization to evaluate each variable's value, propagating computed values to resolve arithmetic expressions.
Pro tip: Mention that you would handle cycles gracefully by detecting them early (e.g., during topological sort) and return false, since cyclic dependencies make the assignments impossible. Also, note that you can optimize by only computing values on demand rather than all variables.
Confirm that each assignment can be a direct copy or a single addition/subtraction of two previously assigned variables, and that the goal is to determine if a target variable can be computed. Ask about input format, variable naming, and whether values are integers or floats.
Represent each variable as a node, with directed edges from operands to the result. For an assignment like T3 = T4 + T5, add edges T4 -> T3 and T5 -> T3. This captures the order of computation.
Use topological sorting (Kahn's algorithm or DFS) to check for cycles. If a cycle exists, the assignments are invalid. Otherwise, the topological order gives a valid sequence to compute values.
Traverse the graph in topological order, computing each variable's value from its operands. Use a hash map to store computed values. For direct copies, just assign the value; for arithmetic, apply the operator.
Consider cases where operands are not yet defined, division by zero (if subtraction leads to negative values? Actually, no division, but consider overflow), and multiple assignments to the same variable. Verify the target variable's value is computed.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Use a topological sort algorithm (Kahn's or DFS-based) to detect cycles while processing the dependency graph. If a cycle is detected, immediately return 'IMPOSSIBLE'; otherwise, return the valid order. This avoids infinite loops and handles the follow-up efficiently.
Pro tip: Mention that you can detect cycles during the topological sort itself, so you don't need a separate pass. Also, clarify that returning 'IMPOSSIBLE' is a sentinel value and discuss how to handle it in the caller.
Restate that the input is a dependency graph where nodes are tasks and edges represent dependencies. The goal is to return a valid execution order or 'IMPOSSIBLE' if a cycle exists.
Select either Kahn's algorithm (BFS-based) or DFS with recursion stack. Explain that both can detect cycles while producing a topological order.
For Kahn's: compute in-degrees, enqueue nodes with zero in-degree, and count processed nodes; if count < total nodes, a cycle exists. For DFS: track visited and recursion stack; if a node is revisited in the current stack, a cycle exists.
If a cycle is detected, return 'IMPOSSIBLE'. Otherwise, return the topological order (e.g., list of tasks).
State time and space complexity (O(V+E)). Discuss edge cases: empty graph, self-loop, multiple cycles, disconnected components.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.