Warmup part, and I actually fumbled the Luhn implementation on the first try because I mixed up which digits get doubled.
Start by clarifying requirements and edge cases, then outline the validation steps: check card type (VISA), length (16 digits), and Luhn checksum. Implement the Luhn algorithm efficiently, and discuss trade-offs like input sanitization, error handling, and potential optimizations.
Pro tip: Mention that while Luhn catches most accidental errors, it's not a security measure; for production, you'd also validate with a payment processor and consider PCI compliance.
Ask about input format (string vs. number), expected output (boolean or error details), and whether to handle only VISA or other card types.
Describe the sequence: remove non-digit characters, check length and prefix, then apply Luhn checksum.
Detail the algorithm: from rightmost digit, double every second digit, sum digits of products, and check if total modulo 10 is zero.
Write clean code, possibly in a single pass from right to left, and discuss time/space complexity (O(n) time, O(1) space).
Cover empty input, non-numeric characters, leading zeros, and mention that Luhn is not foolproof; discuss error handling and extensibility.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Straightforward extension of the first part.
Design a modular validator with a registry of card networks, each defining its own length and prefix rules. Implement a two-phase validation: first identify the network based on prefixes and length, then apply the Luhn checksum. Return specific error codes for unknown networks and checksum failures.
Pro tip: Mention that in production systems like Stripe, card validation is often combined with BIN lookup services for real-time network detection, but for this exercise, a rule-based approach suffices. Also, highlight the importance of not logging full card numbers for security.
Create a data structure (e.g., map or list) that maps each network to its required length(s) and prefix patterns. For example, VISA: length 16, prefix '4'; Mastercard: length 16, prefixes '51'-'55'; Amex: length 15, prefixes '34' or '37'.
Given a card number, iterate through the network specifications to find a match based on length and prefix. If no match, return an 'unknown network' error code.
Once the network is identified, validate the card number using the Luhn algorithm. If the checksum fails, return a 'checksum failure' error code.
If all checks pass, return success (e.g., true or a success code). Otherwise, return the appropriate error code (e.g., 'unknown_network', 'invalid_checksum').
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
First, clarify the problem: each network has a set of valid card number patterns (e.g., prefixes and lengths). For each redacted number, generate all possible completions by replacing '*' with digits, then check each completion against the network rules. Count valid completions per network and sort the results alphabetically by network name.
Pro tip: Precompute the valid card number patterns for each network and use a trie or prefix matching to efficiently validate completions, especially if the number of wildcards is large. Also, consider that the number of completions can be huge (10^5), so avoid brute-force enumeration if possible; instead, use dynamic programming or combinatorics to count valid completions directly.
Ask about the exact format of card numbers, the definition of 'valid' for each network (e.g., length, prefix, Luhn check), and the maximum number of wildcards. Confirm that output should be sorted alphabetically by network name.
Implement a function that checks if a fully specified card number is valid for a given network. This may involve checking length, prefix ranges, and possibly the Luhn algorithm.
If wildcards are few (≤5), brute-force enumeration (10^5) is acceptable. For larger wildcards, use dynamic programming over positions, tracking prefix validity and remaining length, or use a trie of valid patterns to prune early.
Write code to count valid completions per network, ensuring edge cases like leading zeros, varying lengths, and overlapping network rules are handled. Test with sample inputs and verify counts.
Collect counts in a dictionary keyed by network name, then output them sorted alphabetically by network name, as required.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
First, clarify the card network validation rules (e.g., Luhn check, prefixes, lengths) and the exact meaning of 'one error'. Then, for each possible original digit at the '?' position, generate candidate numbers by reversing the two error types (digit change and adjacent swap), validate each candidate against all rules, and collect valid ones. Finally, sort the valid numbers numerically and output them with their network names.
Pro tip: Mention that you would precompute network rules and use a trie or prefix table for fast prefix matching, and emphasize that the number of candidates is small (at most 20 per error type), so brute-force reversal is efficient and avoids overcomplicating.
Ask the interviewer to confirm the card network validation rules (Luhn algorithm, accepted lengths, and prefix ranges) and that exactly one error (digit change or adjacent swap) occurred. Also confirm the output format: list of original numbers with network names, sorted numerically.
For the single '?' position, try all 10 digits to form a base number. Then, for each base, generate candidates by reversing a digit change (replace each digit with 0-9) and reversing an adjacent swap (swap each adjacent pair). Deduplicate candidates.
For each candidate, check if it satisfies the Luhn algorithm, has a valid length, and matches a known network prefix. If valid, record the number and its network name.
Collect all valid original numbers, sort them numerically, and output each with its network name. Ensure no duplicates and that the sorting is correct (e.g., as integers, not strings).
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.