You walk in with your answer sheet and they just ask you to explain what you wrote.
Structure your answer as a clear, step-by-step narrative of how you tackle coding problems on paper, emphasizing your thought process, communication, and verification. Highlight how you break down the problem, consider edge cases, and test your solution manually. Show that you can think critically and write clean, correct code even without an IDE.
Pro tip: Verbalize your assumptions and trade-offs as you go; interviewers value clear reasoning over silent coding. Also, always manually trace through your code with a sample input to catch off-by-one errors.
Restate the problem in your own words and ask clarifying questions about input/output, constraints, and edge cases. This ensures you and the interviewer are aligned before you start.
Outline a high-level strategy, including the algorithm and data structures you'll use. Discuss time and space complexity trade-offs and consider alternative approaches.
Translate your plan into clear, structured pseudocode or actual code on paper. Keep it organized, use meaningful variable names, and comment on complex logic.
Manually trace through your code with a sample input, including edge cases. Verify that the logic produces the correct output and fix any bugs.
Reflect on your solution: can you improve time/space complexity? Are there any redundant steps? Discuss potential optimizations with the interviewer.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Model the problem as a state transition where each break increases the total number of pieces by 2. Start from 1 piece and determine which totals are reachable by adding 2 repeatedly, then identify the impossible parity.
Pro tip: Clarify the initial state: assume you start with one whole noodle. If the interviewer allows multiple noodles, the answer changes, so state your assumption explicitly.
Confirm that you start with one noodle and that each break splits one existing piece into exactly 3 pieces, increasing the total piece count by 2.
Note that the total number of pieces always has the same parity as the starting number of pieces (1), so it must be odd.
List achievable totals: 1, 3, 5, 7, ... by repeatedly adding 2. Any even number is impossible.
Conclude that any even total number of pieces is impossible to achieve, and if asked for a specific number, provide the smallest even number greater than 1 (i.e., 2) or the even number in question.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Model the stack height as a linear function: total height = base height + (number of glasses - 1) * increment per additional glass. Use the two given data points (8 glasses = 42 cm, 2 glasses = 18 cm) to solve for the base height and increment, then compute the height for 6 glasses. Verify the result by checking consistency with the given data.
Pro tip: After solving, briefly mention that this is a linear interpolation problem and that you would validate the model with a quick sanity check (e.g., 6 glasses should be between 18 cm and 42 cm). This shows attention to detail and mathematical maturity.
Let h be the height of the base glass and d be the additional height added by each extra glass. Then the height of a stack of n glasses is H(n) = h + (n-1)d.
For 8 glasses: h + 7d = 42. For 2 glasses: h + d = 18. Write these as a system of two linear equations.
Subtract the second equation from the first to eliminate h: (h+7d) - (h+d) = 42 - 18 => 6d = 24 => d = 4. Then substitute d back to find h: h + 4 = 18 => h = 14.
For 6 glasses, use H(6) = h + 5d = 14 + 5*4 = 34 cm.
Check that 34 cm is between 18 cm and 42 cm, and that the increment per glass (4 cm) is consistent with the difference between 8 and 2 glasses (24 cm over 6 extra glasses).
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Craft a concise narrative that connects your technical background to Bloomberg's engineering culture, emphasizing adaptability and problem-solving in ambiguous situations. Focus on recent, relevant experiences and how they've prepared you for the challenges of a fast-paced financial technology environment.
Pro tip: Research Bloomberg's engineering principles and recent projects, then subtly align your past experiences with their needs—showing you've done your homework and can hit the ground running.
Start with your current role and a high-level summary of your technical expertise, highlighting languages and domains most relevant to Bloomberg.
Briefly walk through 1-2 previous roles or projects that demonstrate adaptability, such as navigating unclear requirements or shifting priorities.
Share a specific accomplishment where you solved a complex problem or delivered impact under ambiguity, using metrics if possible.
Explain why you're interested in Bloomberg and this role, tying your skills to their mission and engineering challenges.
Conclude with what you hope to contribute and learn, showing enthusiasm for the opportunity.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
This is the classic prefix-suffix product problem.
Clarify the constraints (e.g., array size, integer overflow, zeros) and then present a solution using prefix and suffix products to achieve O(n) time and O(n) space. If asked for optimization, mention the O(1) space approach that reuses the output array.
Pro tip: Explicitly discuss how to handle zeros and integer overflow, as these are common pitfalls that interviewers look for. Also, mention the trade-off between time and space complexity to demonstrate deeper understanding.
Ask about input size, constraints, and expected output format. Confirm that division is not allowed and discuss edge cases like zeros and negative numbers.
Start with a brute-force O(n^2) solution, then improve to O(n) using prefix and suffix products. Mention that division is disallowed, so you avoid that approach.
Explain the prefix-suffix method: compute prefix products in one pass, suffix products in another, and multiply them. Alternatively, use the output array to store prefix products and then multiply by suffix products in a second pass to achieve O(1) extra space.
State that the time complexity is O(n) and space complexity is O(n) for the basic approach, or O(1) extra space if optimized. Discuss trade-offs.
Discuss how the solution handles zeros (e.g., if there are two or more zeros, all outputs are zero; if one zero, only that position gets the product of others) and potential integer overflow (suggest using long or BigInteger if needed).
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Sort by start time, then iterate and merge.
Start by clarifying the problem (e.g., whether intervals are inclusive, if input is sorted, and expected output format). Then propose sorting intervals by start time and merging in a single pass, explaining the O(n log n) time and O(n) space complexity. Walk through an example to demonstrate correctness.
Pro tip: Mention edge cases like empty input, single interval, and intervals that are adjacent but not overlapping (e.g., [1,2] and [2,3]) to show thoroughness. Also, discuss how you would handle large inputs or streaming data if relevant.
Ask about input format, whether intervals are sorted, inclusivity of endpoints, and expected output. Confirm if the result should be sorted.
Propose sorting intervals by start time, then iterating and merging overlapping intervals into a result list. Explain why sorting is necessary.
Describe the merge condition: if the current interval's start <= last merged interval's end, update the end to the max of both ends; otherwise, add the current interval to the result.
State time complexity O(n log n) due to sorting, and space complexity O(n) for the output (or O(1) extra if sorted in-place and output not counted).
Walk through a sample input like [[1,3],[2,6],[8,10],[15,18]] to show the merging process and verify edge cases.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Use a hash map where the key is a canonical representation of each anagram (e.g., sorted string or character count), and the value is a list of strings that share that key. Iterate through the input list, compute the key for each string, and append the string to the corresponding list. Finally, return all the lists from the hash map.
Pro tip: Discuss the trade-offs between sorting each string (O(n * k log k)) and using character counts (O(n * k)), and mention that character counts can be more efficient for long strings or large alphabets. Also, clarify assumptions about input (e.g., lowercase letters only) and handle edge cases like empty strings.
Ask about input size, character set, case sensitivity, and whether the output order matters. This shows attention to detail and helps choose the optimal approach.
Decide on a representation that uniquely identifies anagrams, such as the sorted string or a character frequency tuple. Explain why it works and its complexity.
Use a hash map to group strings by their canonical key. Each key maps to a list of anagrams.
Loop through the input list, compute the key for each string, and append the string to the corresponding list in the hash map.
Collect all the lists from the hash map and return them as the grouped anagrams. Optionally, discuss output format.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the problem and edge cases, then propose a sliding window approach using a hash map to track characters and their indices. Walk through the algorithm step-by-step, analyze time and space complexity, and optionally discuss alternative solutions like brute force for comparison.
Pro tip: Mention that the sliding window approach can be optimized to O(n) time by storing the last seen index of each character and moving the left pointer directly to max(left, lastSeen[char] + 1). This shows you understand both the algorithm and its optimization.
Ask clarifying questions: Is the string ASCII or Unicode? Should we consider case sensitivity? What should be returned if the string is empty? Confirm that we need the length, not the substring itself.
Briefly mention that a brute force solution would check all substrings for uniqueness, resulting in O(n^3) time complexity, which is inefficient. This sets the stage for a better solution.
Explain that we can use a sliding window defined by two pointers (left and right) and a hash map to store the last seen index of each character. Expand the window by moving right, and if a duplicate is found, move left to the maximum of its current position and the last seen index of the duplicate plus one.
Trace the algorithm on a sample string like 'abcabcbb' to demonstrate how the window expands and contracts, and how the maximum length is updated.
State that the time complexity is O(n) since each character is visited at most twice, and space complexity is O(min(n, m)) where m is the size of the character set. Discuss edge cases like empty string, all unique characters, and all same characters.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.