Preview Questions Data Analyst | Role Specific Skill

Question 1: In the BFSI sector, what is the primary purpose of using a confusion matrix when evaluating classification models for credit scoring?

Which action should you take?

Choose only one option

To calculate the overall accuracy of the model

To determine the optimal threshold for classification

To understand the types of errors made by the model (false positives and false negatives)

To visualize the distribution of predictions

Question 2: When performing time series analysis, what is the main purpose of decomposition?

Which action should you take?

Choose only one option

To reduce noise and improve model accuracy

To identify trends, seasonality, and residual components

To smooth out the data for easier analysis

To predict future values using autoregression

Question 3: In the BFSI sector, what does the Akaike Information Criterion (AIC) help determine when comparing multiple statistical models?

Which action should you take?

Choose only one option

The model that best fits the data without overfitting

The model with the highest complexity is preferred

The model with the most predictors

The model that maximizes the likelihood regardless of complexity

Question 4: When conducting a statistical test on financial market data, how would you handle the issue of autocorrelation in residuals?

Which action should you take?

Choose only one option

Use a Newey-West standard error correction

Apply a log transformation

Use a fixed-effects model

Discard the correlated data points

Question 5: How do you interpret the results of a chi-square test when analyzing categorical financial data?

Which action should you take?

Choose only one option

A significant result suggests a very strong relationship between the variables

A significant result indicates that there is a dependency between the variables, while a non-significant result means they are independent

A significant result suggests no relationship between the variables

A non-significant result indicates that the data is flawed

Question 6: How do you deal with outliers in a dataset when preparing data for machine learning in BFSI?

Which action should you take?

Choose only one option

Normalize the data to reduce the impact of outliers

Apply logarithmic transformation to outliers

Impute outlier values with the mean of the dataset

Remove all values above a certain threshold