Troubleshooting & Problem Resolution Flashcards

An ETL job fails because a date column contains values like '31/02/2024'. What is the best resolution?

Answer: Validate and clean or reject invalid dates during data preparation

Invalid dates should be detected by validation rules and corrected, flagged, or excluded.

After joining two tables, the row count is much larger than expected. What is the most likely cause?

Answer: Duplicate keys causing a many-to-many join

Non-unique join keys on both sides multiply rows.

A model fails to train because of NaN values in numeric features. Which is an appropriate first step?

Answer: Impute or remove missing values after analyzing why they are missing

Missing values must be handled with a method suited to the missingness pattern before training.

Distance-based algorithms such as k-NN give poor results when one feature is measured in dollars and another in fractions. What should be done?

Answer: Scale or normalize the features

Features on large scales dominate distance calculations unless scaled.

Outliers are distorting the mean of a skewed income variable. Which approach is most robust?

Answer: Use the median or apply a transformation

The median and log transforms are less sensitive to extreme values.

A categorical feature with 5,000 unique values makes one-hot encoding impractical. What is a reasonable alternative?

Answer: Group rare levels or use target/frequency encoding

Grouping rare categories or using compact encodings controls dimensionality.

Training and scoring data show different column orders and a missing category level, causing prediction errors. What is the best preventive practice?

Answer: Apply the same saved preprocessing pipeline to both

Reusing the fitted preprocessing pipeline keeps training and scoring inputs consistent.