The AI helper is there but I'd be careful leaning on it too hard.
Start by clarifying the problem requirements and edge cases with the interviewer, then outline your approach in plain English before writing any SQL. Use the AI helper to check syntax or explain functions, but focus on demonstrating your problem-solving process and ability to write correct, efficient SQL.
Pro tip: Verbalize your thought process as you write the query, explaining why you chose certain joins or functions. This shows the interviewer how you think and makes it easier for them to follow along.
Ask questions to understand the exact problem, expected output, and any constraints (e.g., table schemas, edge cases).
Outline the steps your query will take, such as which tables to join, filters, aggregations, and ordering.
Construct the SQL query step by step, using the AI helper for syntax checks or function explanations as needed.
Walk through the query with sample data or edge cases to ensure it produces the correct result.
Discuss potential performance improvements and explain your query's logic clearly.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
This is the part where I felt the time pressure more.
Start by thoroughly exploring the dataset to understand its structure, distributions, and potential issues. Then preprocess the data (handle missing values, encode categorical variables, scale features) and engineer new features that could improve model performance. Finally, select an appropriate model, train it, and evaluate using cross-validation, ensuring to communicate your reasoning and trade-offs at each step.
Pro tip: Always establish a baseline model (e.g., logistic regression or simple decision tree) before trying complex models, and document your assumptions and decisions clearly—this demonstrates structured thinking and practical judgment.
Examine data types, summary statistics, distributions, and correlations. Identify missing values, outliers, and class imbalance.
Handle missing values (impute or drop), encode categorical variables (one-hot or label encoding), and scale numerical features if needed.
Create new features based on domain knowledge or interactions (e.g., polynomial features, binning, aggregations) to improve model performance.
Choose a suitable model (e.g., logistic regression, random forest, gradient boosting) based on the problem type and data size. Split data into train/validation sets.
Evaluate model using appropriate metrics (accuracy, F1, AUC, etc.) and cross-validation. Iterate on feature engineering and hyperparameters to improve results.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.