LinkedIn technical interviews are known for their challenging questions, designed to evaluate a candidate’s problem-solving skills, logical thinking, and ability to handle follow-up inquiries. For international candidates, these interviews can be particularly daunting due to language barriers and the high-pressure environment. This article details a real LinkedIn interview question and showcases how CSOAHELP's real-time keyword prompts enable candidates to confidently navigate every step of the process.
The interviewer started the session with the following problem:
"Given the standard mapping from English letters to digits on a phone keypad (e.g., 2 -> a, b, c; 3 -> d, e, f; etc.), write a program that outputs all words that can be formed from any n-digit phone number from the list of given KNOWN_WORDS considering the mapping mentioned above."
The interviewer provided additional context with an example:
"For example:
KNOWN_WORDS = ['careers', 'linkedin', 'hiring', 'interview', 'linkedgo']
phoneNumber: 2273377
Output: ['careers']
phoneNumber: 54653346
Output: ['linkedin', 'linkedgo']."
Immediately after the problem was introduced, CSOAHELP provided a behind-the-scenes suggestion: "Clarify input-output requirements and ask about performance constraints." Following this prompt, the candidate engaged in clarifying questions to ensure a precise understanding of the task:
- Candidate: "Just to confirm, the
KNOWN_WORDSlist is pre-defined and fixed, correct? Also, can I assume the input phone numbers will always be valid numeric strings without spaces or special characters?" - Interviewer: "Yes, that’s correct. The
KNOWN_WORDSlist is fixed, and the input phone numbers are valid." - Candidate: "Are there specific performance expectations, such as constraints on the size of
KNOWN_WORDSor the length of the phone numbers?" - Interviewer: "You can assume up to a few thousand words in
KNOWN_WORDS, and the phone numbers are typically 10 digits long, though there’s no hard limit."
With these clarifications, the candidate transitioned into outlining an initial solution, guided by another CSOAHELP suggestion: "Start with a brute-force idea, then discuss optimization strategies."
- Candidate: "My initial thought is to map each digit of the phone number to its corresponding set of letters and generate all possible combinations of letters. Then, I’d filter the results by checking which combinations exist in the
KNOWN_WORDSlist. However, this approach might be inefficient, as the number of combinations grows exponentially with the phone number's length. Instead, I propose iterating through theKNOWN_WORDSlist and checking each word to see if it matches the mapping of the input phone number."
The interviewer appeared intrigued and asked for further elaboration:
- Interviewer: "That sounds reasonable. How would you ensure that this approach is efficient?"
The candidate paused briefly, and CSOAHELP provided a timely keyword prompt: "Mention linear scanning and avoiding redundant computations." With this, the candidate confidently continued:
- Candidate: "Instead of generating all possible combinations, I would directly compare each word in
KNOWN_WORDSagainst the phone number’s digit-to-letter mapping. This way, I only process valid words and avoid unnecessary computations. Additionally, this approach keeps memory usage under control, as it doesn’t involve storing intermediate combinations."
The interviewer then tested the candidate’s understanding with a deeper question:
- Interviewer: "What would the time complexity of this approach be? And how would it handle large-scale inputs?"
CSOAHELP suggested: "Discuss time complexity as O(mn), where m is the number of words and n is the average word length. Highlight scalability options." The candidate responded promptly:
- Candidate: "The time complexity is O(mn), where m is the number of words in
KNOWN_WORDSand n is the average length of the words. Each word requires a full character-by-character comparison with the phone number mapping. For large-scale inputs, the approach remains efficient, as we avoid precomputing or storing all possible combinations."
The interviewer pushed further:
- Interviewer: "If
KNOWN_WORDSis very large, is there a way to optimize this process further?"
Another keyword prompt from CSOAHELP read: "Suggest using a Trie data structure for faster lookups." The candidate explained:
- Candidate: "To optimize for large
KNOWN_WORDSlists, we could preprocess the words into a Trie data structure. This would allow us to traverse the phone number digit-by-digit and efficiently check for matching words. By pruning invalid branches early, we can significantly reduce the number of unnecessary comparisons." - Interviewer: "What would the complexity of using a Trie be?"
- Candidate: "Building the Trie would take O(mn) time, where m is the number of words and n is the average length. Querying the Trie for a phone number would take O(k), where k is the length of the phone number. This makes it much faster than a linear scan for larger datasets."
As the discussion progressed, the interviewer introduced a new scenario:
- Interviewer: "How would your solution handle phone numbers with repeated digits, like 2222222?"
CSOAHELP’s immediate prompt—"Mention skipping redundant combinations by tracking repeated patterns."—helped the candidate respond smoothly:
- Candidate: "For phone numbers with repeated digits, we can optimize by skipping redundant combinations. For example, if consecutive digits map to the same set of letters, we can avoid reprocessing identical branches in the Trie or during direct comparisons."
Toward the end of the session, the interviewer asked the candidate to summarize the overall solution, including its strengths, weaknesses, and potential improvements. With CSOAHELP’s prompt—"Highlight simplicity, scalability, and Trie-based optimization while mentioning distributed systems for further scaling."—the candidate provided a structured response:
- Candidate: "The proposed solution is efficient for typical input sizes, with a time complexity of O(mn) for the basic approach or O(k) per query with a Trie. It avoids exponential growth in combinations by directly validating
KNOWN_WORDS. The main strength is its scalability for moderate-sized datasets, while its primary limitation lies in memory usage for extremely large datasets. For further optimization, we could distribute the workload across multiple systems to handle large-scale inputs efficiently."
This interview showcased the candidate’s ability to tackle a complex problem under pressure, made possible by CSOAHELP’s real-time keyword prompts. From clarifying requirements to addressing edge cases and optimizing solutions, CSOAHELP enabled the candidate to maintain a clear, logical flow and demonstrate both technical depth and adaptability. For international candidates navigating high-stakes interviews, CSOAHELP is the silent ally that ensures confidence and precision, making success a reachable goal.
经过csoahelp的面试辅助，候选人获取了良好的面试表现。如果您需要面试辅助或面试代面服务，帮助您进入梦想中的大厂，请随时联系我。
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