This question evaluates the candidate's understanding of best practices for handling missing values in a dataset. It assesses knowledge of data preprocessing techniques, such as removing columns or rows with significant or insignificant numbers of missing values, and identifying and treating extremely irrelevant data as missing values.

This question evaluates the candidate's understanding of different imputation methods suitable for dealing with missing data in categorical variables.

This question evaluates the candidate's knowledge of different approaches to treat outliers. The main skill assessed is understanding outlier treatment methods in data analysis. The candidate should be aware of common practices used to handle outliers in data, and identify the preferred approach.

This question evaluates the candidate's understanding of exploratory data analysis in the context of analyzing a dataset. It tests knowledge of statistical concepts, correlation and causation, different methods of analyzing numerical variables, as well as popular techniques used in multivariate analysis.

This question evaluates the candidate's understanding of categorical variables and their ordering. It requires the ability to differentiate between different types of categorical variables and identify which one does not fit the criteria of being an ordered variable.

This question evaluates the candidate's understanding of data visualization techniques, specifically in the context of bivariate analysis. It assesses their familiarity with different types of plots and their ability to choose the most suitable plot for conveying specific details of the data.

This question evaluates the candidate's understanding of dimensional modeling and the relationships between dimension tables and a fact table. It requires knowledge of data warehousing concepts, particularly identifying the type of relationship that can be expressed between dimensions using a fact table.

This question evaluates the candidate's understanding of database normalization and the different normal forms. It requires knowledge of the concept of singleton candidate keys and their implications on normalization. The candidate must be able to identify the normal form that a relation consisting only of singleton candidate keys will always satisfy.

This question evaluates the candidate's knowledge of database schema design and relationships. It tests their ability to correctly map a binary 1:N relationship type into relations using the cross reference approach.

This question evaluates the candidate's understanding of model evaluation techniques and selecting the most appropriate technique to measure the overall correctness achieved by a model in a positive prediction environment. The candidate must be familiar with F1 Score, Recall, Confusion Matrix, and Precision and understand how each technique is used to evaluate model performance.

This question assesses the candidate's skills in SQL querying, specifically in retrieving data based on specific conditions and aggregating results. It evaluates understanding of JOIN operations, subqueries, and filtering data based on maximum values.

This question assesses the candidate's ability to work with datasets, perform calculations, and understand basic statistical concepts related to average and comparison operations.