Preview Questions Data Analyst | Role Specific Skill

Question 1: When visualizing financial data trends in Tableau, which chart type is most effective for displaying multiple trend lines over time for different financial products?

Which action should you take?

Choose only one option

Scatter Plot

Pie Chart

Bar Chart

Line Chart

Question 2: In a financial institution, when managing a database with high read and write operations, which database design consideration is most critical?

Which action should you take?

Choose only one option

Using complex joins to aggregate information

Using a combination of normalized and denormalized tables

Using full-text search for all textual data

Storing data in memory for faster access

Question 3: What is the primary purpose of indexing in relational databases, and how does it impact query performance in large-scale BFSI data systems?

Which action should you take?

Choose only one option

To enforce data integrity and constraints

To manage user permissions and access controls

To reduce the storage space required for the database

To speed up data retrieval operations by providing a quick lookup mechanism

Question 4: When performing time series analysis, how do you handle seasonality in financial data such as monthly loan originations?

Which action should you take?

Choose only one option

I ignore seasonality and rely only on trend analysis

I detrend the data by removing any seasonal components

I apply a simple moving average to smooth the data

I apply decomposition techniques or use models like ARIMA that account for seasonality

Question 5: In logistic regression used for predicting default on loans, what is the primary purpose of the Hosmer-Lemeshow test?

Which action should you take?

Choose only one option

To determine the statistical significance of individual coefficients

To assess the linearity of the logit

To check for multicollinearity among predictors

To evaluate the goodness-of-fit of the model

Question 6: Which of the following techniques is used to handle missing data in a dataset where values are missing randomly and not systematically?

Which action should you take?

Choose only one option

Fill missing values with a constant

Use multiple imputation methods

Drop rows with missing values

Impute missing values using the mean