Amazon's online assessments are renowned for their challenging and thought-provoking questions designed to evaluate a candidate's problem-solving skills and technical knowledge. In this blog post, we will delve into two specific coding questions that frequently appear in Amazon's online assessments: Machine Learning Data Training and Profit & Loss Analysis.
Introduction
Online assessments are a crucial part of Amazon's hiring process, especially for technical roles. These assessments test a wide range of skills, from basic programming knowledge to complex problem-solving abilities. In this blog, we'll provide an overview of two typical questions from these assessments, discuss the key concepts involved, and outline strategies for solving them.
1. Code Question 1
AWS provides various trained machine learning models to aid data analysis. A developer wants to evaluate the data set for training a model.
More formally, given two arrays data_type and data_size each of size n, data_type[i] and data_size[i] denote the category and the size of the i-th data point. The experience gained by the model after training it with the i-th data point is the product of the data point's size and the number of different data types the model has been trained on so far. The overall experience after all training is the sum of these values.
Consider for example, data_type = [1, 2, 1, 2] and data_size = [4, 3, 2, 1]. For discussion, train the model using the data points in the order shown.
For i = 1, it is type 1 with size 4. One type is seen so far, (1). The experience gained by it is 1 * 4 = 4. For i = 2, it is type 2, size 3. Two types are seen so far, (1, 2). The experience gained is 2 * 3 = 6. For i = 3, type 1, size 2, two types seen (1, 2), experience gained 2 * 2 = 4. For i = 4, type 2, size 1, two types seen (1, 2), experience gained 2 * 1 = 2.
Overall, the experience gained is 4 + 6 + 4 + 2 = 16 using this order of training.
Arrange the data to maximize the experience after training the model with all data points. Return the maximum possible experience value.
Example Given n = 3, data_type = [1, 2, 1] and data_size = [2, 3, 2], the possible orderings are shown.
| Index | data_type | data_size | Training | Experience |
|---|---|---|---|---|
| 1,2,3 | [1, 2, 1] | [2, 3, 2] | 1 * 2 + 2 * 3 + 2 * 2 | 12 |
| 2,3,1 | [2, 1, 1] | [3, 2, 2] | 1 * 3 + 2 * 2 + 2 * 2 | 12 |
Question 1: Machine Learning Data Training
The first question revolves around maximizing the experience gained from training a machine learning model with given data points. Candidates are provided with two arrays representing data types and their sizes. The goal is to arrange these data points to maximize the total experience.
Detailed problem statement omitted
Key Concepts:
- Array Manipulation: Understanding how to manipulate and traverse arrays efficiently.
- Experience Calculation: Calculating experience based on given rules and ensuring the maximum experience is obtained.
- Optimization: Employing sorting or other optimization techniques to achieve the desired outcome.
Approach:
- Analyze the experience gain formula.
- Determine the optimal order of data points.
- Implement the solution using appropriate data structures for efficiency.
2. Code Question 2
A financial analyst is analyzing the profit and loss (PnL) for a given firm. The analyst needs to modify the given PnL values to achieve the maximum possible number of negative cumulative PnL values while ensuring that the overall cumulative PnL is positive.
Given an array of integers PnL where PnL[i] represents the profit or loss for the i-th month, the cumulative PnL for the i-th month is the sum of the PnL values from month 1 to month i. The operation allowed is to multiply a PnL value by -1 (i.e., change its sign).
Consider for example, PnL = [5, -3, 1, -2]. The possible cumulative PnL for the PnL = [3, -2, 5, -6, 1] is [3, 1, 6, 0, 1].
Example Consider, n = 4, and PnL = [5, 3, 1, 2].
Some of the possible arrays after performing the given operation some number of times:
| Modified PnL | Cumulative PnL | Number of negatives | Is Valid | Comments |
|---|---|---|---|---|
| [5, -3, -1, 2] | [5, 2, 1, 3] | 2 | Yes | The operation was performed on the second and third months (in bold). All the cumulative PnL are positive |
| [5, -3, -1, -2] | [5, 2, 1, -1] | 3 | No | The last cumulative PnL is negative, hence this is not valid |
| [5, -3, 1, -2] | [5, 2, 3, 1] | 2 | Yes | All the cumulative PnL are positive |
| [-5, 3, 1, 2] | [-5, -2, -1, 1] | 1 | No | The cumulative PnL for the first three months are negative |
There are many more ways to perform the operations but the maximum number of negative PnLs there can be, maintaining a positive cumulative PnL is 2. Report 2 as the answer.
Function Description Complete the function getMaxNegativePnL in the editor below.
getMaxNegativePnL has the following parameter:
int PnL[n]: an array of integers
Constraints
1 ≤ n ≤ 10^51 ≤ PnL[i] ≤ 10^9
Input Format For Custom Testing
- Sample Case 0:
- PnL[] size n = 4
- PnL = [1, 1, 1, 1]
- Sample Output
- 2
Explanation There are multiple possible PnLs such as [1, -1, -1, 1], [-1, 1, 1, -1], etc. However, it is optimal to modify the PnL to be [1, 1, -1, -1] or [1, -1, -1, 1].
Question 2: Profit & Loss Analysis
The second question focuses on modifying an array of monthly profit and loss values to maximize the number of months with negative cumulative profit and loss while ensuring the overall cumulative profit remains positive.
Detailed problem statement omitted
Key Concepts:
- Cumulative Sum: Calculating cumulative sums and understanding their impact on the overall result.
- Sign Change Operations: Strategically changing the signs of elements in the array to achieve the goal.
- Validation: Ensuring the resulting array meets the condition of positive overall cumulative profit.
Approach:
- Calculate cumulative sums and identify potential months for sign changes.
- Use a greedy algorithm or dynamic programming to determine the optimal set of operations.
- Validate the solution to ensure all conditions are met.
Strategies for Success
- Understand the Problem: Carefully read and comprehend the problem statement. Identify key requirements and constraints.
- Plan Your Solution: Before diving into coding, outline your approach. Consider edge cases and potential pitfalls.
- Optimize: Aim for efficiency in both time and space. Utilize data structures like heaps, hash maps, or dynamic arrays as needed.
- Practice: Regularly practice similar problems on platforms like LeetCode, HackerRank, and CodeSignal. Familiarize yourself with common patterns and algorithms.
Conclusion
Amazon's online assessments are designed to challenge and evaluate a candidate's technical prowess. By understanding the key concepts and practicing regularly, you can improve your chances of success. The questions on Machine Learning Data Training and Profit & Loss Analysis are just two examples of the types of problems you may encounter. With thorough preparation and a strategic approach, you can tackle these assessments with confidence.
Good luck with your preparation, and may you succeed in your journey to join one of the world's leading tech companies!
These two algorithmic problems highlight the importance of efficient data manipulation and optimization in real-world applications like server scaling and cart management. By understanding and applying the right algorithms, we can ensure optimal performance and user experience.
If you have any questions or would like to discuss these problems further, contact us!
经过我们的强力面试辅助，OA代写，候选人通过这些面试题的解析和沟通，面试官不仅了解了候选人的编程能力，也看到了我在解决问题过程中清晰的思路和有效的沟通技巧。这些不仅有助于应对 Meta 的面试，同时也能提升我们解决实际编程问题的能力。祝大家面试顺利！
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