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 Module
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. 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.
Summary:The questions are very close to real business and test how you can use SQL to analyze behavioral data or attribute metrics. Involves window functions, group by, having, and logical judgment.
Part 2: Machine Learning Module
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?
Summary:Machine learning questions will not let you write code, but will test whether you have engineering perspective. When answering the questions, you can combine the business example + model strategy, programhelp students will generally have a standard template prepared in advance, the examination can be quickly applied.
Part 3: Probability module
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.
Summary:This kind of questions mainly depends on whether you can use intuition + deduction to explain probabilistic events. Suggestion: give more examples + step-by-step reasoning, sometimes it is more important than formulas.
FAQ: Frequently Asked Questions about Indeed Data Post OA
Q1. Is this OA in the form of open brush or real-time submission?
A: It's a HackerRank platform, limited time to answer questions, no support for multiple submissions or skipping questions.
Q2. What language is used for the SQL part? Does it support window functions?
A: Standard SQL is used, supporting window functions, CTE, multi-level nested queries, etc.
Q3. Is there any coding in the ML section?
A: There's no coding, it's all multiple choice or short answer questions that examine your understanding of the model.
Q4. How many questions? How long will it take?
A: About 10 questions in total, 3-4 SQL, 4-5 ML, 2-3 probability, about 90 minutes.
Programhelp Student Recordings
This student contacted us on the day she received the OA, and time was tight, so we helped her with a quick SQL High Frequency questions + ML theory explanation combing.
On the day of answering the questions, we assisted her remotely through the on-line method without any trace, and the questions like Q2 and Q3, which are easy to step on the pitfalls, are all the sets of questions that we have sorted out in advance. In the end, she successfully passed the OA and got the VO opportunity!
Preparing for Indeed or other FAANG data post OA?
You may be experiencing these problems as well:
Unsure about the distribution of questions and inefficient revision?
SQL lags, machine learning answers are vague and uninspiring?
Nervous about making mistakes once you get to the exam room, you know how to do it but can't write it?
Programhelphelp you out once and for all!
We offer:
✅ High Frequency Question Predictions + Split Lecture Notes
✅ Practical training + Pacing guide for answering questions
✅ Traceless remote on-line assistance
Several students have already passed Indeed, Meta, Amazon and other OA sessions with our assistance and got the offers of their choice!
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