Technology & Programming

Practical Data Science Quiz: Implementation & Evaluation

Answer 9 questions that test working knowledge of data science techniques, focusing on applied scenarios.

#machine learning#statistics#data analysis#modeling#intermediate#Python

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Total Questions

Difficulty Level

medium

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Quiz Questions

Answer all questions below and test your knowledge.

1
In 2026, your team is building a binary classification model. You have a dataset with severe class imbalance. Which performance metric is most appropriate for the initial evaluation, independent of the chosen decision threshold?
Question 1
2
During feature engineering for a time-series regression model, you suspect a predictor has a non-linear relationship with the target that changes over the seasons. What is a sound, modern approach to automatically capture this interaction effect?
Question 2
3
A colleague fits a linear regression model. The diagnostic plot of residuals versus fitted values shows a distinct 'U-shape' (convex pattern). What does this most directly indicate?
Question 3
4
You are tasked with reducing the dimensionality of a large set of text embeddings before clustering. The goal is to preserve as much of the variance in the original data as possible while ensuring the new features are orthogonal. Which technique is most suitable?
Question 4
5
When performing hyperparameter tuning for a Random Forest model using RandomizedSearchCV in scikit-learn, which of the following parameter combinations would you realistically search over?
Question 5
6
You are building a recommendation system for a streaming service using collaborative filtering. For a user with very few watched items, what is the primary technical challenge?
Question 6
7
In a large-scale A/B test comparing two webpage layouts, you calculate a p-value of 0.04 for the difference in conversion rate. What is the most statistically rigorous interpretation?
Question 7
8
You are preparing a dataset for a model predicting equipment failure. The 'maintenance_date' column contains sporadic, future dates for scheduled maintenance on currently healthy machines. How should you best handle this column for a training set built from historical data?
Question 8
9
What is the primary computational purpose of using stochastic gradient descent (SGD) instead of standard (batch) gradient descent when training a deep neural network?
Question 9

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Practical Data Science Quiz: Implementation & Evaluation

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Quiz Questions

In 2026, your team is building a binary classification model. You have a dataset with severe class imbalance. Which performance metric is most appropriate for the initial evaluation, independent of the chosen decision threshold?

During feature engineering for a time-series regression model, you suspect a predictor has a non-linear relationship with the target that changes over the seasons. What is a sound, modern approach to automatically capture this interaction effect?

A colleague fits a linear regression model. The diagnostic plot of residuals versus fitted values shows a distinct 'U-shape' (convex pattern). What does this most directly indicate?

You are tasked with reducing the dimensionality of a large set of text embeddings before clustering. The goal is to preserve as much of the variance in the original data as possible while ensuring the new features are orthogonal. Which technique is most suitable?

When performing hyperparameter tuning for a Random Forest model using RandomizedSearchCV in scikit-learn, which of the following parameter combinations would you realistically search over?

You are building a recommendation system for a streaming service using collaborative filtering. For a user with very few watched items, what is the primary technical challenge?

In a large-scale A/B test comparing two webpage layouts, you calculate a p-value of 0.04 for the difference in conversion rate. What is the most statistically rigorous interpretation?

You are preparing a dataset for a model predicting equipment failure. The 'maintenance_date' column contains sporadic, future dates for scheduled maintenance on currently healthy machines. How should you best handle this column for a training set built from historical data?

What is the primary computational purpose of using stochastic gradient descent (SGD) instead of standard (batch) gradient descent when training a deep neural network?

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