想进Indeed的同学注意了，TA 家的数据岗位 OA 不是那种只刷一刷 SQL 就能搞定的类型，而是综合考察你在数据库操作、机器学习理论、以及概率推理上的理解深度。我们这次的同学就遇到了三大类题型，每一类都有难点，好在有我们实时助攻，最后顺利通过进入面试阶段！这篇 blog 给大家复盘一下她当时的 OA 题目内容，以及我们在答题过程中的知识点提示。
Part 1: SQL 模块
Q1. Compare Department Average Salary to Company Average
Given two tables (Employees, Departments), write a SQL query to compare the average salary of each department to the company’s overall average salary. The output should include department name and the comparison result as “higher”, “lower”, or “same”.
Q2. Quiet Students in All Exams
Write a query to report the students (student_id, student_name) who are “quiet” in ALL exams. A quiet student is one who participated in at least one exam but never got either the highest or the lowest score in any exam.
Q3. 3+ Consecutive Days with 100+ Visitors
Given a table of daily visitor counts, write a query to find all periods of at least 3 consecutive days where each day had 100 or more visitors.
Q4. Count Bank Visitors by Number of Transactions
Write an SQL query to count how many bank users visited the bank and did 0 transactions, 1 transaction, and so on.
总结：题型非常贴近真实业务，考查你如何用 SQL 分析行为数据或指标归因。涉及窗口函数、group by、having，以及逻辑判断等。
Part 2: Machine Learning 模块
Q1. Detecting EV Households from Hourly Electricity Usage
How would you detect households that own electric vehicles using hourly electricity consumption data?
Q2. Predicting Out-of-Stock Inventory
How would you predict which items are likely to go out-of-stock?
Q3. Regression & Classification Metrics
List various evaluation metrics for regression and classification tasks.
Q4. Pros & Cons of Mean Squared Error (MSE)
Explain the advantages and disadvantages of using Mean Squared Error as a loss function.
Q5. What Is Learning Rate in Gradient Boosting?
Define learning rate and describe its role in gradient boosting algorithms.
Q6. More Features than Rows – What’s the Impact?
What happens when the number of features exceeds the number of observations in a dataset? How can regularization methods like Lasso or Ridge help?
Q7. Effect of Multicollinearity on XGBoost Feature Importance
What’s the impact of high correlation between features on their importance ranking in XGBoost?
总结：机器学习题不会让你写代码，但会考你是否具备工程落地视角。答题时可以结合业务例子 + 模型策略，programhelp 学员一般会有标准模板提前准备好，考场上快速套用即可。
Part 3: Probability 模块
Q1. Explain Probability Distribution
What is a probability distribution? Describe its basic types and usage.
Q2. Generate Random 1–7 Using a Die
How would you generate a uniform random number between 1 and 7 using a single standard 6-faced die?
Q3. Normal to Uniform Sampling
If you draw from a normal distribution with known parameters, how do you generate samples from a uniform distribution?
Q4. Probability That Two Customers Are in the Same Partition
If 75 customers are randomly assigned to 3 equal partitions, what is the probability that Bob and Ben are in the same one?
Q5. Explain Bayesian Probability
What is the Bayesian approach to probability? Give a real-life application.
总结：此类题目主要看你能否用直觉 + 推导解释概率事件，建议：多举例 + 分步推理，有时比公式更重要。
FAQ：关于 Indeed 数据岗 OA 的常见问题
Q1. 这个 OA 是开放刷题形式还是实时提交？
A：是 HackerRank 平台，限时答题，不支持多次提交或跳题。
Q2. SQL 部分用什么语言？支持窗口函数吗？
A：用的是标准 SQL，支持窗口函数、CTE、多层嵌套查询等。
Q3. ML 部分有没有 coding？
A：没有 coding，都是选择题或简答题，考察你对模型理解。
Q4. 几道题？时间多久？
A：一共大概 10 道题，SQL 3–4 道，ML 4–5 道，概率 2–3 道，时间大概 90 分钟。
Programhelp 学员实录分享
这位同学是在收到 OA 的当天联系到我们，时间比较紧，我们帮她做了一个快速的 SQL 高频题精讲 + ML 理论讲解梳理。
答题当天我们通过无痕联机方式远程辅助，像 Q2、Q3 这种容易踩坑的题，都是我们提前梳理过的套路题型。最后她成功通过 OA，顺利拿到 VO 机会！
正在准备 Indeed 或其他 FAANG 数据岗 OA？
你可能也遇到这些问题：
不确定题型分布，复习效率低？
SQL 卡顿、机器学习答题空泛没思路？
一到考场就紧张出错，明明会但写不出来？
Programhelp帮你一次解决！
我们提供：
✅ 高频真题预测 + 拆解讲义
✅ 模拟实战训练 + 答题节奏指导
✅ 无痕远程联机协助
已经有多位学员在我们辅助下顺利通过 Indeed、Meta、Amazon 等 OA 环节，拿下心仪 offer！
想了解更多 OA 代写方式？后台私信「OA」或直接留言，我们来帮你定制上岸方案！
