Technical interviews can be intimidating, especially when you’re competing for positions at prestigious tech companies. While many candidates focus on memorizing algorithms and data structures, they often overlook a crucial aspect: the structure of their solutions. A well structured solution not only demonstrates your technical abilities but also showcases your problem solving methodology and communication skills.<\/p>\n
In this comprehensive guide, we’ll explore why proper structure matters in technical interviews, identify common structural pitfalls, and provide actionable strategies to transform your coding solutions from chaotic to coherent.<\/p>\n
When interviewers evaluate candidates, they’re looking beyond whether the solution works. They’re assessing how you think, how you communicate, and how you organize complex ideas. Here’s why structure is a critical component of successful technical interviews:<\/p>\n
A structured solution reflects structured thinking. When you present a well organized solution, you demonstrate that you can break down complex problems into manageable components, a skill that’s invaluable in real world software development.<\/p>\n
Code that’s easy to read is easy to maintain. By structuring your solution clearly, you show that you can write code that others can understand and build upon, a crucial skill in collaborative environments.<\/p>\n
Well structured code makes it easier to identify and fix bugs. It also simplifies the process of writing tests, which is increasingly important in modern software development practices.<\/p>\n
Professional developers adhere to coding standards and best practices. By presenting a structured solution, you signal that you’re familiar with industry standards and ready to contribute to production code.<\/p>\n
A clear structure helps the interviewer follow your thought process. This makes it easier for them to provide guidance if you get stuck and to evaluate your approach accurately.<\/p>\n
Before we discuss how to structure your solutions effectively, let’s identify the common pitfalls that can undermine your performance:<\/p>\n
Many candidates start coding immediately without planning their approach. This often leads to backtracking, inefficient solutions, and confusion for both the candidate and the interviewer.<\/p>\n
For example, consider a problem asking you to find the kth largest element in an array. Diving straight into a sorting solution without considering heap based approaches might lead to a less optimal solution.<\/p>\n
Writing monolithic functions that handle multiple responsibilities makes your code difficult to understand and test. This approach also makes it harder to reuse components of your solution.<\/p>\n
Consider this example of a poorly modularized solution for validating a binary search tree:<\/p>\n
function isValidBST(root) {\n \/\/ A single function that tries to do everything\n if (!root) return true;\n \n \/\/ Complex logic all in one place\n if ((root.left && root.left.val >= root.val) || \n (root.right && root.right.val <= root.val))\n return false;\n \n \/\/ Recursive calls without clear separation of concerns\n return isValidBST(root.left) && isValidBST(root.right);\n}\n<\/code><\/pre>\nInconsistent Naming Conventions<\/h3>\n
Using unclear or inconsistent variable and function names makes your code harder to follow. This is particularly problematic in interviews where you need to clearly communicate your thought process.<\/p>\n
Absence of Comments or Documentation<\/h3>\n
Without appropriate comments, interviewers may struggle to understand your intentions, especially for complex algorithms or edge cases.<\/p>\n
Neglecting Edge Cases<\/h3>\n
Failing to structure your solution to handle edge cases (empty inputs, boundary conditions, etc.) suggests incomplete thinking and can lead to incorrect solutions.<\/p>\n
Disorganized Approach to Problem Solving<\/h3>\n
Jumping between different approaches or mixing implementation details with high level design creates confusion and indicates a lack of methodical thinking.<\/p>\n
The Ideal Structure for Technical Interview Solutions<\/h2>\n
Now that we understand the importance of structure and common pitfalls, let’s outline an ideal approach to structuring your technical interview solutions:<\/p>\n
1. Understand the Problem<\/h3>\n
Begin by ensuring you fully understand the problem statement. This includes:<\/p>\n
\n- Clarifying the inputs and expected outputs<\/li>\n
- Asking questions about constraints and edge cases<\/li>\n
- Discussing assumptions with the interviewer<\/li>\n<\/ul>\n
For example, if asked to implement a function to reverse a linked list, you might ask:<\/p>\n
\n- Should I reverse it in place or create a new list?<\/li>\n
- What should happen if the list is empty or has only one node?<\/li>\n
- Are there any time or space complexity requirements?<\/li>\n<\/ul>\n
2. Plan Your Approach<\/h3>\n
Before writing any code, outline your solution strategy:<\/p>\n
\n- Discuss multiple approaches if applicable<\/li>\n
- Analyze time and space complexity<\/li>\n
- Select the most appropriate approach based on the constraints<\/li>\n
- Sketch the high level algorithm steps<\/li>\n<\/ul>\n
For instance, when solving a problem like finding all pairs in an array that sum to a target value, you might outline:<\/p>\n
\n- Approach 1: Nested loops (O(n\u00b2) time, O(1) space)<\/li>\n
- Approach 2: Hash map approach (O(n) time, O(n) space)<\/li>\n
- Decision: Use the hash map approach for better time complexity<\/li>\n<\/ul>\n
3. Structure Your Code<\/h3>\n
Organize your code implementation with these principles:<\/p>\n
\n- Use descriptive variable and function names<\/li>\n
- Break down complex logic into helper functions<\/li>\n
- Follow consistent indentation and formatting<\/li>\n
- Group related functionality together<\/li>\n<\/ul>\n
Here’s an example of a well structured solution for finding the maximum depth of a binary tree:<\/p>\n
\/**\n * Finds the maximum depth of a binary tree\n * @param {TreeNode} root - The root node of the binary tree\n * @return {number} - The maximum depth\n *\/\nfunction maxDepth(root) {\n \/\/ Base case: empty tree has depth 0\n if (root === null) return 0;\n \n \/\/ Recursive case: compute depth of subtrees\n const leftDepth = maxDepth(root.left);\n const rightDepth = maxDepth(root.right);\n \n \/\/ Return the larger depth plus 1 for the current node\n return Math.max(leftDepth, rightDepth) + 1;\n}\n<\/code><\/pre>\n4. Handle Edge Cases Explicitly<\/h3>\n
Always include explicit handling of edge cases:<\/p>\n
\n- Empty or null inputs<\/li>\n
- Minimum and maximum values<\/li>\n
- Single element inputs<\/li>\n
- Invalid inputs (if applicable)<\/li>\n<\/ul>\n
5. Test Your Solution<\/h3>\n
Demonstrate thoroughness by testing your solution:<\/p>\n
\n- Walk through with a simple example<\/li>\n
- Test edge cases<\/li>\n
- Verify the solution against the expected output<\/li>\n
- Discuss potential optimizations<\/li>\n<\/ul>\n
6. Analyze Time and Space Complexity<\/h3>\n
Conclude by analyzing the efficiency of your solution:<\/p>\n
\n- Provide a clear time complexity analysis<\/li>\n
- Explain the space complexity<\/li>\n
- Discuss any tradeoffs made<\/li>\n<\/ul>\n
Communication Techniques That Enhance Solution Structure<\/h2>\n
Effective communication is integral to presenting a well structured solution. Here are techniques to enhance your communication during technical interviews:<\/p>\n
Think Aloud Strategically<\/h3>\n
Sharing your thought process helps interviewers follow your reasoning, but do so strategically:<\/p>\n
\n- Articulate high level approaches before diving into details<\/li>\n
- Explain why you’re choosing certain data structures or algorithms<\/li>\n
- Verbalize your reasoning when making tradeoffs<\/li>\n<\/ul>\n
For example: “I’m considering using a hash map here because we need O(1) lookups, which will help us achieve a linear time solution overall.”<\/p>\n
Use Visual Aids<\/h3>\n
For complex problems, visual representations can clarify your thinking:<\/p>\n
\n- Draw diagrams to illustrate data structures<\/li>\n
- Use tables to track algorithm progress<\/li>\n
- Sketch execution flow for recursive solutions<\/li>\n<\/ul>\n
Establish Clear Transitions<\/h3>\n
Signal when you’re moving between different phases of your solution:<\/p>\n
\n- “Now that I understand the problem, let me outline my approach…”<\/li>\n
- “Before implementing, let me analyze the time complexity…”<\/li>\n
- “Let me start by handling the edge cases…”<\/li>\n<\/ul>\n
Seek Validation at Key Points<\/h3>\n
Check in with the interviewer to ensure you’re on the right track:<\/p>\n
\n- “Does this approach make sense so far?”<\/li>\n
- “Are there any constraints I should be considering?”<\/li>\n
- “Is this the level of detail you’re looking for?”<\/li>\n<\/ul>\n
Acknowledge Limitations<\/h3>\n
Demonstrate self awareness by recognizing the limitations of your solution:<\/p>\n
\n- “This approach works well for small inputs but might be inefficient for very large datasets.”<\/li>\n
- “An alternative approach would be X, which would improve Y at the cost of Z.”<\/li>\n<\/ul>\n
Language Specific Structuring Best Practices<\/h2>\n
Different programming languages have their own conventions and best practices for code structure. Here are guidelines for some commonly used languages in technical interviews:<\/p>\n
Python<\/h3>\n
Python emphasizes readability and simplicity:<\/p>\n
\n- Use 4 spaces for indentation (not tabs)<\/li>\n
- Follow PEP 8 style guidelines<\/li>\n
- Use docstrings for function documentation<\/li>\n
- Leverage list comprehensions for concise operations<\/li>\n
- Use meaningful variable names in snake_case<\/li>\n<\/ul>\n
def two_sum(nums, target):\n \"\"\"\n Find indices of two numbers that add up to target.\n \n Args:\n nums: List of integers\n target: Integer target sum\n \n Returns:\n Tuple of two indices\n \"\"\"\n num_to_index = {} # value -> index mapping\n \n for i, num in enumerate(nums):\n complement = target - num\n if complement in num_to_index:\n return (num_to_index[complement], i)\n num_to_index[num] = i\n \n return None # No solution found\n<\/code><\/pre>\nJava<\/h3>\n
Java code typically follows more formal structuring:<\/p>\n
\n- Use camelCase for variables and methods<\/li>\n
- Use PascalCase for class names<\/li>\n
- Include access modifiers (public, private, etc.)<\/li>\n
- Structure code with clear class hierarchies<\/li>\n
- Use Javadoc comments for documentation<\/li>\n<\/ul>\n
\/**\n * Solution for the Two Sum problem.\n *\/\npublic class TwoSum {\n \n \/**\n * Finds indices of two numbers that add up to target.\n * \n * @param nums Array of integers\n * @param target Target sum\n * @return Array containing the two indices\n *\/\n public int[] twoSum(int[] nums, int target) {\n \/\/ Using a map to store value -> index mapping\n Map<Integer, Integer> numToIndex = new HashMap<>();\n \n for (int i = 0; i < nums.length; i++) {\n int complement = target - nums[i];\n \n \/\/ Check if complement exists in map\n if (numToIndex.containsKey(complement)) {\n return new int[] {numToIndex.get(complement), i};\n }\n \n \/\/ Store current number and its index\n numToIndex.put(nums[i], i);\n }\n \n \/\/ No solution found\n return new int[] {-1, -1};\n }\n}\n<\/code><\/pre>\nJavaScript<\/h3>\n
JavaScript offers flexibility but benefits from consistent structuring:<\/p>\n
\n- Use camelCase for variables and functions<\/li>\n
- Use PascalCase for classes and constructors<\/li>\n
- Apply consistent semicolon usage<\/li>\n
- Leverage ES6+ features (arrow functions, destructuring)<\/li>\n
- Use JSDoc for documentation<\/li>\n<\/ul>\n
\/**\n * Finds indices of two numbers that add up to target.\n * @param {number[]} nums - Array of integers\n * @param {number} target - Target sum\n * @return {number[]} - Array containing the two indices\n *\/\nfunction twoSum(nums, target) {\n \/\/ Using a map to store value -> index mapping\n const numToIndex = new Map();\n \n for (let i = 0; i < nums.length; i++) {\n const complement = target - nums[i];\n \n \/\/ Check if complement exists in map\n if (numToIndex.has(complement)) {\n return [numToIndex.get(complement), i];\n }\n \n \/\/ Store current number and its index\n numToIndex.set(nums[i], i);\n }\n \n \/\/ No solution found\n return null;\n}\n<\/code><\/pre>\nPractical Examples: Before and After Restructuring<\/h2>\n
Let’s examine some practical examples of poorly structured solutions and how they can be improved:<\/p>\n
Example 1: Finding Palindromes<\/h3>\n
Before (Poorly Structured):<\/strong><\/p>\nfunction isPalindrome(s) {\n s = s.toLowerCase().replace(\/[^a-z0-9]\/g, '');\n for (let i = 0; i < s.length \/ 2; i++)\n if (s[i] !== s[s.length - 1 - i]) return false;\n return true;\n}\n<\/code><\/pre>\nAfter (Well Structured):<\/strong><\/p>\n\/**\n * Determines if a string is a palindrome, considering only alphanumeric characters\n * and ignoring case.\n * \n * @param {string} s - The input string\n * @return {boolean} - True if the string is a palindrome, false otherwise\n *\/\nfunction isPalindrome(s) {\n \/\/ Edge case: empty string is considered a palindrome\n if (s.length === 0) return true;\n \n \/\/ Preprocess: convert to lowercase and remove non-alphanumeric characters\n const cleanString = preprocessString(s);\n \n \/\/ Check if the string reads the same forwards and backwards\n return checkPalindrome(cleanString);\n}\n\n\/**\n * Converts string to lowercase and removes non-alphanumeric characters\n *\/\nfunction preprocessString(s) {\n return s.toLowerCase().replace(\/[^a-z0-9]\/g, '');\n}\n\n\/**\n * Checks if a clean string is a palindrome using two-pointer technique\n *\/\nfunction checkPalindrome(s) {\n let left = 0;\n let right = s.length - 1;\n \n while (left < right) {\n if (s[left] !== s[right]) {\n return false;\n }\n left++;\n right--;\n }\n \n return true;\n}\n<\/code><\/pre>\nExample 2: Merge Sort Implementation<\/h3>\n
Before (Poorly Structured):<\/strong><\/p>\nfunction mergeSort(arr) {\n if (arr.length <= 1) return arr;\n const mid = Math.floor(arr.length \/ 2);\n const left = mergeSort(arr.slice(0, mid));\n const right = mergeSort(arr.slice(mid));\n let result = [], i = 0, j = 0;\n while (i < left.length && j < right.length) {\n if (left[i] < right[j]) result.push(left[i++]);\n else result.push(right[j++]);\n }\n return result.concat(left.slice(i)).concat(right.slice(j));\n}\n<\/code><\/pre>\nAfter (Well Structured):<\/strong><\/p>\n\/**\n * Sorts an array using the merge sort algorithm.\n * \n * @param {number[]} arr - The array to sort\n * @return {number[]} - The sorted array\n * Time Complexity: O(n log n)\n * Space Complexity: O(n)\n *\/\nfunction mergeSort(arr) {\n \/\/ Base case: arrays of length 0 or 1 are already sorted\n if (arr.length <= 1) {\n return arr;\n }\n \n \/\/ Divide the array into two halves\n const mid = Math.floor(arr.length \/ 2);\n const leftHalf = arr.slice(0, mid);\n const rightHalf = arr.slice(mid);\n \n \/\/ Recursively sort both halves\n const sortedLeft = mergeSort(leftHalf);\n const sortedRight = mergeSort(rightHalf);\n \n \/\/ Merge the sorted halves\n return merge(sortedLeft, sortedRight);\n}\n\n\/**\n * Merges two sorted arrays into a single sorted array.\n * \n * @param {number[]} left - First sorted array\n * @param {number[]} right - Second sorted array\n * @return {number[]} - Merged sorted array\n *\/\nfunction merge(left, right) {\n const result = [];\n let leftIndex = 0;\n let rightIndex = 0;\n \n \/\/ Compare elements from both arrays and add the smaller one to the result\n while (leftIndex < left.length && rightIndex < right.length) {\n if (left[leftIndex] < right[rightIndex]) {\n result.push(left[leftIndex]);\n leftIndex++;\n } else {\n result.push(right[rightIndex]);\n rightIndex++;\n }\n }\n \n \/\/ Add any remaining elements\n return result\n .concat(left.slice(leftIndex))\n .concat(right.slice(rightIndex));\n}\n<\/code><\/pre>\nUnderstanding the Interviewer’s Perspective<\/h2>\n
To structure your solutions effectively, it helps to understand what interviewers are looking for:<\/p>\n
Problem Solving Process<\/h3>\n
Interviewers value how you approach problems more than whether you get the perfect answer immediately:<\/p>\n
\n- They want to see systematic thinking<\/li>\n
- They evaluate how you break down complex problems<\/li>\n
- They assess your ability to consider different approaches<\/li>\n<\/ul>\n
Code Quality Signals<\/h3>\n
Your code structure sends important signals about your professional capabilities:<\/p>\n
\n- Clean, well organized code suggests attention to detail<\/li>\n
- Modular design indicates software engineering maturity<\/li>\n
- Consistent naming shows commitment to readability<\/li>\n
- Explicit edge case handling demonstrates thoroughness<\/li>\n<\/ul>\n
Collaboration Potential<\/h3>\n
How you structure and communicate your solution indicates how you might work with others:<\/p>\n
\n- Clear explanations suggest you can communicate technical concepts effectively<\/li>\n
- Receptiveness to feedback shows you can work collaboratively<\/li>\n
- Structured thinking indicates you can contribute to system design discussions<\/li>\n<\/ul>\n
Effective Practice Methods to Improve Solution Structure<\/h2>\n
Improving your solution structure requires deliberate practice. Here are effective methods to enhance your structuring skills:<\/p>\n
Code Review Practice<\/h3>\n
Regularly review and refactor your own code:<\/p>\n
\n- Solve a problem, then put it aside for a day<\/li>\n
- Return to your solution and evaluate its structure<\/li>\n
- Identify areas for improvement and refactor<\/li>\n
- Compare your solution with well structured examples<\/li>\n<\/ul>\n
Template Development<\/h3>\n
Create personal templates for common problem types:<\/p>\n
\n- Develop a standard approach for array problems<\/li>\n
- Create templates for tree traversals<\/li>\n
- Establish patterns for dynamic programming solutions<\/li>\n<\/ul>\n
For example, a template for binary search might look like:<\/p>\n
\/**\n * Binary search template\n *\/\nfunction binarySearch(nums, target) {\n \/\/ Initialize search boundaries\n let left = 0;\n let right = nums.length - 1;\n \n \/\/ Continue searching while boundaries are valid\n while (left <= right) {\n \/\/ Calculate middle index\n const mid = Math.floor((left + right) \/ 2);\n \n \/\/ Found target\n if (nums[mid] === target) {\n return mid;\n }\n \n \/\/ Adjust boundaries based on comparison\n if (nums[mid] < target) {\n left = mid + 1; \/\/ Target in right half\n } else {\n right = mid - 1; \/\/ Target in left half\n }\n }\n \n \/\/ Target not found\n return -1;\n}\n<\/code><\/pre>\nMock Interviews with Structure Focus<\/h3>\n
Practice with peers or online platforms, specifically focusing on structure:<\/p>\n
\n- Ask your mock interviewer to evaluate your solution structure<\/li>\n
- Record your mock interviews to review your communication<\/li>\n
- Set specific goals for structural improvements in each session<\/li>\n<\/ul>\n
Study Well Structured Code<\/h3>\n
Analyze high quality code from open source projects or solution repositories:<\/p>\n
\n- Identify structuring patterns used by experienced developers<\/li>\n
- Note how complex functionality is broken down<\/li>\n
- Observe documentation and commenting practices<\/li>\n<\/ul>\n
Verbalization Practice<\/h3>\n
Practice explaining your code structure out loud:<\/p>\n
\n- Explain your solution as if teaching someone else<\/li>\n
- Practice articulating your structural choices<\/li>\n
- Record yourself and identify areas where your explanation lacks clarity<\/li>\n<\/ul>\n
Advanced Structuring Techniques for Complex Problems<\/h2>\n
For particularly complex interview problems, consider these advanced structuring techniques:<\/p>\n
State Management Abstraction<\/h3>\n
For problems with complex state tracking:<\/p>\n
\n- Encapsulate state variables in classes or objects<\/li>\n
- Create methods that update state according to specific rules<\/li>\n
- Separate state manipulation from algorithm logic<\/li>\n<\/ul>\n
Example of state management in a solution for the “Game of Life” problem:<\/p>\n
class GameOfLife {\n constructor(board) {\n this.board = board;\n this.rows = board.length;\n this.cols = board[0].length;\n }\n \n \/**\n * Advances the game by one generation\n *\/\n nextGeneration() {\n const nextState = this.computeNextState();\n this.updateBoard(nextState);\n }\n \n \/**\n * Computes the next state without modifying the current board\n *\/\n computeNextState() {\n const nextState = Array(this.rows).fill()\n .map(() => Array(this.cols).fill(0));\n \n for (let r = 0; r < this.rows; r++) {\n for (let c = 0; c < this.cols; c++) {\n const liveNeighbors = this.countLiveNeighbors(r, c);\n nextState[r][c] = this.applyCellRules(this.board[r][c], liveNeighbors);\n }\n }\n \n return nextState;\n }\n \n \/**\n * Counts live neighbors for a cell\n *\/\n countLiveNeighbors(row, col) {\n \/\/ Implementation details\n }\n \n \/**\n * Applies game rules to determine next cell state\n *\/\n applyCellRules(currentState, liveNeighbors) {\n \/\/ Implementation details\n }\n \n \/**\n * Updates the board with the new state\n *\/\n updateBoard(nextState) {\n for (let r = 0; r < this.rows; r++) {\n for (let c = 0; c < this.cols; c++) {\n this.board[r][c] = nextState[r][c];\n }\n }\n }\n}\n<\/code><\/pre>\nAlgorithm Decomposition<\/h3>\n
For algorithms with multiple phases:<\/p>\n
\n- Divide the algorithm into distinct phases<\/li>\n
- Implement each phase as a separate function<\/li>\n
- Create clear interfaces between phases<\/li>\n<\/ul>\n
Example of algorithm decomposition for a solution to find all permutations:<\/p>\n
\/**\n * Generates all permutations of a given array\n *\/\nfunction permute(nums) {\n const result = [];\n \n \/\/ Phase 1: Initialize the backtracking process\n backtrack(nums, [], new Set(), result);\n \n return result;\n}\n\n\/**\n * Phase 2: Backtracking to build permutations\n *\/\nfunction backtrack(nums, currentPermutation, used, result) {\n \/\/ Base case: permutation is complete\n if (currentPermutation.length === nums.length) {\n \/\/ Phase 3: Store the result\n savePermutation(currentPermutation, result);\n return;\n }\n \n \/\/ Try each number that hasn't been used yet\n for (let i = 0; i < nums.length; i++) {\n if (used.has(i)) continue;\n \n \/\/ Phase 4: Make a choice\n used.add(i);\n currentPermutation.push(nums[i]);\n \n \/\/ Recurse\n backtrack(nums, currentPermutation, used, result);\n \n \/\/ Phase 5: Undo the choice\n currentPermutation.pop();\n used.delete(i);\n }\n}\n\n\/**\n * Phase 3: Save a completed permutation\n *\/\nfunction savePermutation(permutation, result) {\n \/\/ Create a deep copy to avoid reference issues\n result.push([...permutation]);\n}\n<\/code><\/pre>\nDesign Pattern Integration<\/h3>\n
Applying appropriate design patterns can elevate your solution structure:<\/p>\n
\n- Strategy pattern for different algorithm approaches<\/li>\n
- Factory pattern for creating different types of objects<\/li>\n
- Observer pattern for event based problems<\/li>\n
- Iterator pattern for custom traversal logic<\/li>\n<\/ul>\n
While full pattern implementations might be excessive for interviews, showing awareness of these patterns demonstrates advanced software design knowledge.<\/p>\n
Conclusion: Elevating Your Technical Interview Performance<\/h2>\n
Proper solution structure is not merely a cosmetic concern but a fundamental aspect of demonstrating your capabilities as a software engineer. By implementing the strategies outlined in this guide, you can transform your interview solutions from disorganized attempts to professional quality code that showcases your technical abilities and problem solving methodology.<\/p>\n
Remember these key takeaways:<\/p>\n
\n- Structure reflects thinking; clear code structure demonstrates clear thinking<\/li>\n
- Planning before coding leads to more coherent solutions<\/li>\n
- Modularization improves readability and maintainability<\/li>\n
- Consistent naming and formatting enhances communication<\/li>\n
- Explicit edge case handling shows thoroughness<\/li>\n
- Language specific best practices signal professionalism<\/li>\n<\/ul>\n
As you prepare for technical interviews, dedicate time specifically to improving your solution structure. This investment will not only enhance your interview performance but also develop skills that translate directly to real world software development.<\/p>\n
The difference between a candidate who merely solves problems and one who presents well structured solutions can be the deciding factor in competitive technical interviews. By mastering proper solution structure, you position yourself as a candidate who not only knows algorithms but also understands how to write professional quality code that others can read, maintain, and build upon.<\/p>\n
Start implementing these structuring techniques in your practice today, and watch as your technical interview performance reaches new heights.<\/p>\n","protected":false},"excerpt":{"rendered":"
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