2026

Technology & Programming

Techniques for Deploying and Sustaining Data Science Models

This quiz examines practical decision-making and problem-solving for intermediate-level data science scenarios.

#programming#machine learning#statistics#data analysis#model evaluation#2026#data wrangling

Quiz Intelligence

Total Questions

7

Difficulty Level

medium

Progress

0/0

0%

Quiz Questions

Answer all questions below and test your knowledge.

1
When analyzing a binary classification model, which evaluation metric is most appropriate if the primary concern is minimizing the cost of false positives, even at the expense of many false negatives?
Question 1
2
In a typical cross-validation procedure for model tuning and evaluation, which step best ensures an unbiased estimate of model performance on unseen data?
Question 2
3
A dataset contains a categorical feature with over 100 unique, high-cardinality values, and many appear infrequently. Which encoding method is generally most suitable to avoid creating a large, sparse feature matrix while retaining useful signal?
Question 3
4
For deploying a machine learning model as a REST API that must handle variable request loads, which primary component is responsible for converting the model object into a service that can receive HTTP requests and return predictions?
Question 4
5
When performing feature selection using a LASSO (L1) regression model, what is the direct mathematical mechanism that results in some feature coefficients becoming exactly zero?
Question 5
6
A data scientist observes their Random Forest model is overfitting the training data. Which single adjustment is most likely to reduce overfitting without fundamentally changing the algorithm?
Question 6
7
In the context of A/B testing for a web feature, what is the primary purpose of calculating statistical power before running the experiment?
Question 7

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Techniques for Deploying and Sustaining Data Science Models

Quiz Intelligence

Quiz Questions

When analyzing a binary classification model, which evaluation metric is most appropriate if the primary concern is minimizing the cost of false positives, even at the expense of many false negatives?

In a typical cross-validation procedure for model tuning and evaluation, which step best ensures an unbiased estimate of model performance on unseen data?

A dataset contains a categorical feature with over 100 unique, high-cardinality values, and many appear infrequently. Which encoding method is generally most suitable to avoid creating a large, sparse feature matrix while retaining useful signal?

For deploying a machine learning model as a REST API that must handle variable request loads, which primary component is responsible for converting the model object into a service that can receive HTTP requests and return predictions?

When performing feature selection using a LASSO (L1) regression model, what is the direct mathematical mechanism that results in some feature coefficients becoming exactly zero?

A data scientist observes their Random Forest model is overfitting the training data. Which single adjustment is most likely to reduce overfitting without fundamentally changing the algorithm?

In the context of A/B testing for a web feature, what is the primary purpose of calculating statistical power before running the experiment?