How to Improve AI Written Test Scores: A 4-Step Template of Problem Analysis—Listing Steps—Boundaries—Complexity.

In competitive AI recruitment, algorithmic coding tests are often the initial filter; yet, many skilled candidates fail not from a lack of knowledge, but from logical confusion and "blanking out" under high pressure. Facing strict proctoring systems and ever-changing past exam question resources, blindly grinding problems or seeking shortcuts is no longer a winning strategy; mastering a universal problem-solving mental model is key to success. The standardized problem-solving process presented here aims to help candidates rapidly establish a complete loop upon receiving a prompt: from analyzing problem constraints, pseudocode logic construction, and checking boundary conditions to complexity optimization verification. This is not merely a practical technique for improving AI coding test scores, but an engineering mindset capable of transforming vague requirements into precise code. By strictly executing this Standard Operating Procedure (SOP), candidates can avoid logical dead ends or Time Limit Exceeded (TLE) errors caused by rushing to code, ensuring that even without an optimal solution, they secure key partial credit through clear algorithmic architecture. Rather than panicking from anxiety, candidates should internalize this universal algorithm test template, breaking down complex problems into controllable steps to demonstrate the logical acumen and stability expected of senior engineers.

Core Strategy: A Universal 4-Step Mental Model for AI Written Tests

When facing algorithmic written tests for AI positions at major tech companies, the biggest enemy for many candidates is not the difficulty of the questions themselves, but the "Blank Page Syndrome" experienced under high pressure. Instead of panicking and attempting to find cheating tools during the exam (which carries extremely high risks in modern proctoring systems), it is better to master a universal "white hat" problem-solving framework.

This mental model aims to quickly translate ambiguous problem requirements into executable code logic, applicable to 90% of algorithmic problems and technical Q&A. Even if you cannot completely derive the optimal solution, following these steps can earn you substantial partial credit by demonstrating clear logical thinking.

Universal 4-Step Problem-Solving Method (The 4-Step Method)

To quickly get into the zone during time-critical written tests, it is recommended to adopt the following four steps as your Standard Operating Procedure (SOP):

1. Analyze Requirements & Constraints (Clarify Inputs & Constraints)
   Do not rush to write code. First, clarify the scale of input data (e.g., $N < 10^5$), data types, and potential hidden conditions. This step determines the upper limit of time complexity your algorithm needs to satisfy.
2. Pseudocode & Algorithm Selection (Structure & Logic)
   Match algorithm prototypes based on constraints (e.g., finding combinations usually corresponds to backtracking, finding the shortest path corresponds to BFS). First, write down the logic flow using comments or pseudocode to ensure a closed-loop thought process. As described in Framework Thinking for Learning Data Structures and Algorithms, once you master the framework structure, the rest is just filling in the code for specific problems.
3. Handling Boundaries & Special Cases (Edge Cases)
   Considering "extreme cases" is key to avoiding a 0% pass rate. Check for empty inputs, maximum/minimum values, duplicate elements, or illegal parameters. The difficulty of exhaustion often lies in "no omissions," and missing boundary conditions is a common cause of incorrect answers.
4. Complexity & Optimization Verification (Complexity & Optimization)
   Finally, check if your algorithm will time out (TLE) in the worst-case scenario. If there is redundancy in the logic, it may slow down execution speed; confirm whether the space and time complexity are within the range allowed by the problem (usually 1 second/256MB).

Through the forced guidance of these four steps, you can break down a complex problem into manageable sub-tasks, fundamentally eliminating the anxiety of not knowing where to start. The following sections will break down the specific execution techniques for each step in detail.

Step 1: Problem Analysis and Constraint Clarification (Clarify Inputs & Constraints)

In the high-pressure environment of AI written tests, the first mistake many candidates make is starting to write code immediately after reading the problem. This "rushing for success" mentality often leads to two fatal consequences: first, hitting a logical dead end halfway through; second, passing the sample cases but encountering Time Limit Exceeded (TLE) upon submission.

Analyzing the problem is not just about understanding the "story background" of the question; it is the process of translating natural language into technical constraints. The core task of this step is to excavate the "hidden information" in the problem, thereby locking in the correct time complexity and data types.

1. Data Scale Determines Algorithm Complexity (Scale Analysis)

This is the most critical link in problem analysis. Most online judge systems (such as Nowcoder, LeetCode, HackerRank) typically limit C++ runtime to 1 second, with slightly more leniency for Java/Python. This means your program's total operation count usually cannot exceed $10^7 \sim 10^8$.

You need to deduce the acceptable time complexity based on the input scale $N$ given in the problem. This helps you directly rule out incorrect algorithmic directions:

• If $N \le 20$: Usually implies $O(2^N)$ or $O(N!)$ complexity. This is often a signal for Backtracking or State Compression DP.
• If $N \le 1000$: Allows $O(N^2)$ complexity. You can use simple nested loops or 2D Dynamic Programming.
• If $N \le 10^5$: Must control within $O(N \log N)$ or $O(N)$. This means you need to use Sorting, Binary Search, Heap, or linear Greedy/Two Pointers strategies. In this case, an $O(N^2)$ brute force solution will definitely fail.
• If $N > 10^8$: Usually requires an $O(\log N)$ or $O(1)$ solution, such as mathematical formula derivation or efficient bit manipulation.

As mentioned in Framework Thinking for Learning Data Structures and Algorithms, although the essence of computer problem-solving is exhaustive search, it must be a "smart exhaustive search" within the range of "allowable complexity."

2. Data Range and Type Pitfalls (Data Types & Overflow)

In addition to focusing on the size of $N$, you must also check the range of specific numerical values. This is a common cause of Hidden Case errors.

• Integer Overflow: If intermediate or final results might reach the $10^{10}$ level (e.g., cumulative sums, permutations, and combinations), the standard 32-bit int will overflow. Be sure to use long (Java) or long long (C++).
• Boundary Values: Pay attention to cases where $N=0$, $N=1$, or the array is empty.
• Special Attributes: Does the problem mention "sorted," "no duplicates," or "positive integers"?
• • "Sorted" often hints at Binary Search.
  • "Find shortest/least" usually points to BFS or Dynamic Programming.
  • "Find all combinations" usually points to DFS/Backtracking.

3. The "Three-Point Checklist" Before Coding

Before typing the first line of code (or even #include), force yourself to complete the following checks:

1. Input Scale Check: What is $N$? Will my algorithm complexity cause TLE?
2. Return Type Check: Will the result exceed 2.1 billion (int limit)? Is modulo required?
3. Extreme Case Rehearsal: If the input is an empty array or maximum values, will my logic crash?

By performing this analysis, you are essentially using the constraints provided by the problem to "cheat"—the limitations themselves are telling you which algorithm to use. Do not wait until the code is finished to discover it times out; the cost of refactoring at that point is enormous.

Step 3: Pseudocode and Algorithm Selection (Structure & Logic)

After completing the constraint analysis in Step 1, a common mistake among candidates is to start writing specific syntax code immediately (e.g., for (int i=0;...)). This habit of "thinking while writing" easily leads to logical dead ends, and once a flaw in the thought process is discovered, the cost of tearing it down and starting over is extremely high.

The core of the second step lies in "planning before acting": lock in the algorithm model based on constraints and build a skeleton using pseudocode or comments. This not only clarifies your train of thought but also demonstrates your logical consistency to the interviewer or manual review process if the code fails to run completely, helping you earn "step points" (partial credit).

1. Establish a "Feature-Algorithm" Mapping Table (The If-Then Mapping)

Based on the input size and problem features extracted in Step 1, you need to quickly retrieve the corresponding algorithm template in your mind. Algorithm selection often relies not on inspiration, but on "conditioned reflex."

You can refer to the following "Feature-Algorithm" Mapping Table to quickly locate the direction for solving the problem:

As mentioned in Framework Thinking for Learning Data Structures and Algorithms, the essence of computer problem-solving is often exhaustive search. Your task is to choose a "smart exhaustive method" (such as backtracking or dynamic programming) based on the problem features, rather than blindly applying complex mathematical formulas.

2. "Comment Skeleton Method" (The Comment Skeleton)

Before writing any executable code, write down your logic flow using comments. This is an effective means to combat "blank page anxiety" and also forces you to think about boundaries.

Steps:

1. Write down the 3-5 core steps of the main function in comments.
2. Define the meaning of key variables (e.g., what dp[i] represents).
3. Fill in the specific code below the comments.

Example:

def solve(nums):
    # 1. Boundary check: if the array is empty, return 0 directly
    if not nums: return 0

# 2. Define dp array, where dp[i] represents the maximum subarray sum ending at i
    # Initialize dp[0] = nums[0]

# 3. Iterate through the array (from index 1 to n)
    # State transition equation: dp[i] = max(nums[i], dp[i-1] + nums[i])

# 4. Record and return the maximum value in the dp array
    pass

The advantage of this method is that even if you don't finish the code due to lack of time, or compilation fails due to syntax errors, in a manual review stage (such as an interview review or certain written tests that allow checking code logic), a clear logical skeleton proves that your thought process is correct, which can often salvage critical impression points.

3. Fallback Strategy: Prioritize Brute Force Solutions

If you cannot find the optimal solution (e.g., an $O(n)$ solution) via the mapping table within 5 minutes, immediately downgrade your strategy and prioritize implementing a logically clear brute force solution.

• Do not seek perfection: For a difficult problem, writing an $O(n^2)$ brute force solution can usually pass 20%-40% of the test cases, securing a guaranteed score.
• Avoid over-engineering: Do not force the use of unfamiliar algorithms just to show off skills. A naive algorithm that runs is always better than a complex algorithm that is only half-written.

Remember, written tests assess the ability to solve problems within a limited time. Structured logic (knowing clearly what you are doing) is more reliable than occasional flashes of brilliance.

Step 3: Edge Case Analysis and Robustness Check

In AI written tests or interviews with big tech companies, getting the logic to work is just the baseline; Robustness is the key to getting an AC (Accepted). Many candidates only pass 80% of the test cases after submitting their code, and the remaining 20% are often failed due to neglecting edge cases. Platforms like LeetCode or Niuke use "hidden test cases" specifically to catch these loopholes.

Before or after writing the core logic, be sure to quickly scan through the following "Safety Checklist":

1. Empty & Minimal Inputs
This is the most common "killer". If the input is an empty array [], an empty string "", or a null pointer, will your code throw an exception or return a default value?

• Linked List Problems: The head node head is null, or the linked list has only one node.
• Array/String Problems: Cases where length is 0 or 1. For example, in Sliding Window problems, if the window size is larger than the array length, will the code go out of bounds?

2. Data Overflow & Numerical Boundaries
When the problem involves integer arithmetic, be wary of Integer.MAX_VALUE or Integer.MIN_VALUE.

• Addition Overflow: Adding two large integers might result in a negative number. For example, calculating the midpoint mid = (left + right) / 2 in binary search may overflow; it should be optimized to mid = left + (right - left) / 2.
• Negative Number Handling: Can the index be negative? Does the maximum subarray sum allow for a negative result?

3. "Off-by-One Error"
This is the most insidious Bug in loop logic.

• Typical Scenarios: When processing array intervals, is it left-closed right-open [a, b) or left-closed right-closed [a, b]?
• Self-Check Method: Substitute a minimal array of length 2 and manually simulate the loop termination condition. If the loop condition is i < n but the logic accesses i+1, an error will occur at the last element.

Practical Advice: Don't wait until submission errors occur to make corrections. When listing steps on scratch paper, you should note on the side: "Note: Input might be empty", "Note: Result might overflow int". This habit can significantly reduce debugging time.

Step 4: Complexity Analysis and Optimization

In the interview evaluation systems of "big tech" companies like ByteDance and Tencent, engineering maturity is an important indicator. Interviewers not only look at whether you can solve the problem but also whether your solution is "expensive". When submitting code or presenting a solution to the interviewer, you must proactively perform complexity analysis.

1. Time Complexity
Use Big O notation to evaluate the growth trend of the algorithm's running time relative to the data scale (N).

• Prediction Standard: General Online Judge (OJ) systems have a limit of about 1 second, which means the program can execute approximately $10^8$ basic operations.
• • If $N = 10^5$, your solution must be $O(N)$ or $O(N \log N)$. If it is $O(N^2)$, it will definitely Time Limit Exceeded (TLE).
  • If $N = 1000$, then $O(N^2)$ is usually acceptable.
• Common Pitfalls: Beware of implicit complexity. For example, using list.pop(0) or element in list in Python are $O(N)$ operations themselves; nesting them in a loop will directly cause the total complexity to degrade to $O(N^2)$.

2. Space-Time Trade-off
This is the core strategy of optimization. When you find that the time complexity is too high, consider whether you can consume extra memory to speed it up.

• Hash Map: Optimize $O(N)$ lookups to $O(1)$. For example, when solving Two Sum type problems, avoid double loops by storing the indices of traversed elements.
• Prefix Sum/Preprocessing: For problems involving frequent interval sum queries, reduce single queries to $O(1)$ through an $O(N)$ preprocessing array.

3. Space Complexity and Recursion Depth
Besides defined arrays, do not ignore the space occupied by the Recursion Stack. Excessively deep recursion (such as the worst case of an unbalanced tree) can lead to $O(N)$ space consumption or even trigger a Stack Overflow. When analyzing, be sure to explicitly state: "Although no extra array was allocated, the recursion depth is N in the worst case, so the space complexity is O(N)."

Step 4: Complexity Analysis & Optimization

In AI written tests and algorithm interviews, it is not enough for code to merely "pass" the test cases. The Online Judge systems of major tech giants (such as ByteDance, Tencent, Meituan, etc.) usually have strict limits on runtime and memory usage. The core goal of this step is to ensure your solution remains efficient when facing massive data, demonstrating sound engineering professionalism.

1. Anticipate Time Complexity

Before writing code, or after listing steps on scratch paper, you must estimate time complexity. This is a key indicator to assess if the solution will time out (Time Limit Exceeded, TLE).

• Big O Notation: Focus on the trend of algorithm runtime growth with data scale $N$.
• • $O(1)$: Hash table lookups, array index access.
  • $O(\log N)$: Binary search, Binary Search Tree operations.
  • $O(N)$: Single traversal of arrays, linked lists, two-pointer algorithms.
  • $O(N \log N)$: Standard sorting algorithms (Merge Sort, Quick Sort), Heap operations.
  • $O(N^2)$: Double loops (Bubble Sort, simple Dynamic Programming).

Rule of Thumb:
Generally, C++ or Java can execute about $10^7$ to $10^8$ basic operations in 1 second. Based on the data range $N$ given in the problem, you can reverse-engineer the allowed complexity:

• If $N \le 1000$, $O(N^2)$ solutions are usually allowed.
• If $N \le 10^5$ (common in written tests), the solution must be optimized to $O(N)$ or $O(N \log N)$.
• If $N > 10^8$, usually only $O(\log N)$ or $O(1)$ solutions can be used.

2. Space-Time Tradeoff Strategy

When you find that the time complexity of a Brute Force solution is too high, the most common optimization method is "trading space for time." This requires us to be proficient in data structures and use extra memory to store intermediate states, thereby reducing repetitive calculations.

• Hash Map: This is the most common optimization tool. For example, when solving high-frequency problems like "Two Sum" or LRU Cache Mechanism, using a hash table can reduce lookup time from $O(N)$ to $O(1)$.
• Auxiliary Array/Prefix Sum: When dealing with array interval queries, pre-processing a prefix sum array can reduce query complexity to $O(1)$.
• Memoization: In recursion or dynamic programming, use arrays or dictionaries to cache calculated results to avoid exponential repetitive calculations.

3. Why Do Big Tech Companies Value Complexity?

As seen in the CodeTop Interview Questions Summary, high-frequency questions from big tech (such as LRU, Top K, Longest Substring Without Repeating Characters) often have clear requirements for optimal solutions. Interviewers are not just testing if you can solve it, but whether you possess Engineering Maturity.

• Resource Awareness: In actual production environments, algorithms must not only be correct but also save server resources. An $O(N^2)$ algorithm might cause a service avalanche when user volume surges.
• Boundary Sensitivity: Being able to actively analyze complexity shows that the candidate has a clear understanding of code performance bottlenecks, rather than blindly piling up logic.

Self-Check List:
Before submitting code, quickly ask yourself:

1. How many layers of loops are in my algorithm? How many times will it run in the worst case?
2. What is the maximum data scale $N$? Is my complexity within the safe range?
3. Have I allocated an excessively large array causing memory overflow (Memory Limit Exceeded)?

Through the analysis in this step, you can not only avoid the trap of TLE but also demonstrate your professional grasp of performance optimization to the interviewer through verbal analysis during the interview.

Practical Drill: Deconstructing a Real Exam Question Using the Template

To demonstrate the power of this "4-Step Template" in actual exams, we have selected a classic problem that consistently tops the charts on the CodeTop Interview Questions Summary and appears with high frequency: "Longest Substring Without Repeating Characters". This problem is very typical in medium-to-hard written tests: it looks simple, but without structured thinking, it is easy to write code that fails performance standards.

❌ Before: Intuitive (Messy) Solution

Many candidates, when in a nervous state, start coding immediately upon seeing the problem. Their thought path is usually like this:

1. Dive right in: Immediately write two nested loops to enumerate all possible substrings.
2. Patch while writing: After writing the loops, realize the need to check if the substring has repeating characters, so add a check logic inside (potentially leading to $O(N^3)$).
3. Encounter errors: After submission, discover "Time Limit Exceeded" or errors with empty string inputs.
4. Mental collapse: Looking at the red error messages, start blindly modifying boundary conditions like i < n or i <= n, making the code messier.

This "intuitive" approach is not only verbose but also extremely prone to missing edge cases, which is the main reason for failing written tests.

✅ After: Templated (Structured) Solution

Applying our Clarify—Steps—Edge Cases—Complexity 4-step template, the problem-solving process becomes clear and controllable.

Step 1: Clarify Constraints

Do not rush to write the for loop; first confirm key information on scratch paper or in comments:

• Input Type: Does the string contain only English letters and numbers, or does it include symbols and spaces? (This decides how large an array or hash map we need).
• Character Set Range: Is it ASCII or Unicode?
• Return Value: Do we need to return the length of the longest substring or the specific substring content?
• Data Scale: What is the string length $N$? If it is $10^5$, an $O(N^2)$ solution will definitely fail; it must be optimized to $O(N)$.

Step 2: List Steps and Algorithm Selection

Based on the problem characteristic "finding the longest... substring", this usually corresponds to Sliding Window or Dynamic Programming.

• Core Logic: Maintain a window [left, right]. right actively expands to the right; when a repeating character is encountered, left passively contracts.
• Data Structure: Need a Hash Map or integer array to record the last position of a character for quick jumping of the left pointer.
• Pseudo-code Draft:

1. 1. Initialize left = 0, maxLen = 0, map.
   2. Traverse right from 0 to the end.
   3. If s[right] exists in map, update left to skip the repeating position.
   4. Calculate current length right - left + 1 and update maxLen.
   5. Store the latest position of s[right] into map.

Step 3: Edge Case Analysis

Before the code takes shape, anticipate "killer" test cases:

• Empty Input: s = "", length is 0, should directly return 0.
• Single Character: s = "a", does the loop enter? Does logic correctly return 1?
• All Repeating: s = "bbbbb", does the left pointer move correctly without an infinite loop?
• No Repeating: s = "abcde", ensure maxLen updates until the very end.

Step 4: Complexity Check

• Time Complexity: We used a sliding window; left and right pointers traverse the string at most once each, so it is $O(N)$. This meets the performance requirements of big tech written tests for $10^5$ data volume.
• Space Complexity: Depends on the character set size. If ASCII, space is $O(1)$ (fixed 128 or 256 size array); if a general character set, it is $O(min(N, M))$, where $M$ is the character set size.

Summary of Effect Comparison

Through this drill on a LeetCode Hot 100 level real question, we can see that the template's role is not just solving the problem, but forcing you to slow down and use engineering thinking to break down the problem. This strategy of "slow is fast" is the shortcut to high scores in written tests.

Anti-Cheating Mechanisms in Big Tech Written Tests vs. Risks of Auxiliary Tools

When preparing for written tests for AI positions, search engines are flooded with various AI auxiliary tools claiming to be "invisible" or "anti-detection." These tools often promise to bypass proctoring systems via technical means to help candidates obtain answers. However, as a technical job seeker, you must clearly realize: the iteration speed of anti-cheating technology in big tech companies far exceeds the update speed of cheating tools. Relying on these tools not only risks invalidating the exam on the spot but also brings the professional risk of being permanently blacklisted by top-tier internet companies.

"Multi-Dimensional" Monitoring Technologies in Modern Written Test Systems

Current online written test platforms (such as Nowcoder, ACMcoder, and enterprise self-developed systems) no longer rely solely on simple "screen switch detection." Instead, they have built comprehensive behavioral analysis systems based on AI. According to Nowcoder's Anti-Cheating Guide, intelligent proctoring can now track the entire process from micro-expressions to the device environment:

1. System-Level Behavior Monitoring:

• • Focus and Screen Switch Detection: Even if tools claim "interface hidden" or use "invisible mode," the proctoring system can still detect the loss of window focus through the browser's underlying blur event or system-level APIs. Once the mouse cursor moves out of the exam area or background processes become abnormally active, the system automatically records a violation.
  • Code Input Feature Analysis: The real programming process involves pauses for thinking, modifications, and debugging. The system records the candidate's Keystroke Dynamics. If large chunks of code are "pasted" in a very short time or input at a constant speed (simulated typing), backend algorithms will directly flag it as an anomaly.

1. Biometric and Environmental Monitoring:

• • Eye-tracking: Real-time analysis of the candidate's eye movement trajectories via camera. Frequent large deviations from the screen, wandering eyes, or staring at a specific area outside the screen (such as viewing a secondary screen or mobile phone) for a long time will trigger an AI anomaly detection alert.
  • Three-Way Audio/Video Monitoring: Serious written test scenarios usually require a "dual-camera" setup—the computer camera monitors the front, and a mobile phone camera monitors the desktop and screen from the side-rear. From this perspective, any "screen invisibility" tools or physical cheats (such as teleprompters) have nowhere to hide.

1. Code Style Consistency Verification:
   Some big tech companies compare the code from the written test with the code written live during the interview. If the written test code has perfect logic, detailed comments, and uses obscure advanced syntax, while the code written during the interview has a vastly different style, interviewers can easily identify suspicion of cheating.

Risk Reality Check: Short-Term Gains vs. Career Path

Using auxiliary tools may seem like a shortcut to an Offer, but it actually places your career at high risk. Top companies like Tencent and ByteDance possess a shared "Integrity Blacklist" mechanism. Once determined to have cheated, your resume may be locked, making it impossible to apply for any position at that enterprise for several years.

The table below compares the real risk-reward of mastering a solution template versus using cheating tools:

Instead of wasting energy on "how not to get caught," it is better to invest time in establishing a solid problem-solving methodology. through a standardized process of analyzing the question, listing steps, and defining boundaries, you can not only compliantly achieve a high score but also demonstrate the professional quality expected of technical talent.

Preparation Resources and Daily Training Strategies

Facing the market's massive 8TB question banks and thousands of pages of PDF materials, job seekers easily fall into the trap of "collecting without reading" or "blindly grinding problems due to anxiety." Efficient preparation does not rely on a flood of questions, but is built on high-quality resource selection and a scientific training mode. Instead of pinning hopes on high-risk auxiliary tools, it is better to internalize the general "4-step problem-solving template" into muscle memory through the following strategies.

Curated Question Banks: Breaking Out of the "Sea of Questions"

The focus of written tests at major tech companies often has continuity, so "practicing the right questions" is more critical than "practicing more questions." It is recommended to concentrate energy on verified high-frequency real questions rather than general algorithm books.

• Target by Company and Frequency: Use tools like CodeTop to check recent high-frequency questions for specific target companies (such as ByteDance, Meituan, Tencent). These questions often reflect the company's current testing preferences (e.g., some departments prefer dynamic programming, while others prefer graph theory).
• Adapt to the Domestic Written Test Ecosystem: Unlike LeetCode's core code mode (writing only the function body), written tests at domestic tech giants (such as Huawei, Alibaba) often adopt the ACM mode, requiring you to handle complex input and output yourself. It is recommended to use Nowcoder for targeted practice to familiarize yourself with reading operations using Scanner or sys.stdin, avoiding point loss due to I/O format errors.
• Categorized Specialized Breakthroughs: Refer to the 2024 Top Internet Company Campus Recruitment Algorithm Question Focus, and conduct modular training based on data structures (linked lists, binary trees, graphs) or algorithmic thoughts (DFS/BFS, dynamic programming, two pointers). After conquering a module, summarize a set of "template variants" applicable to that type of question.

Training Core: Quality over Quantity

A common preparation trap is "thinking you know it just because you understood the answer." Reading the solutions to 10 questions often yields less benefit than completely and painfully "grinding" out one question.

The "1 > 10" Deep Training Method is recommended:

1. Force Template Application: When practicing each question, force yourself to write down the thought process on paper or in a document following the "Analyze—List Steps—Boundaries—Complexity" 4-step flow, rather than just writing code directly.
2. Review Sticking Points: If you get stuck, record whether it was specifically due to "deriving the state transition equation" or "handling boundary conditions."
3. Multiple Solutions for One Problem: For classic questions, try to implement them using both brute force and optimized methods, and compare their time and space complexity.

Full Simulation: Leaving the Comfort Zone of IDEs

In actual written test environments, code editors are often rudimentary, lacking intelligent completion (IntelliSense), syntax highlighting, or even debugging capabilities. Relying on IDE plugin "code crutches" during daily practice will lead to helplessness in the exam room.

• "Whiteboard" Programming Training: Try writing code in Notepad or a plugin-free Web editor. This forces you to check for syntax errors (such as missing semicolons or misspelled APIs) with your brain rather than a compiler.
• Strict Time Limits: Major company written tests usually require completing 3-4 questions within 60-90 minutes. It is recommended to strictly limit the time for a medium-difficulty question to 15-20 minutes during daily practice. Turning on a countdown timer can simulate the urgency of the exam room, training your decision-making ability to make quick trade-offs under high pressure (e.g., writing a brute force solution first to secure points).

Through this "high-simulation" daily training, you will not only accumulate reusable problem-solving patterns but also build psychological immunity to the real exam environment. This is the safest "shortcut" to handling written tests at major tech companies.