FRM 2026 Part I Quantitative Analysis Data Analysis Free Quiz 1

Practice FRM 2026 Part I Quantitative Analysis MCQs from Data Analysis. Get instant results with Explanation.

General10 MCQs

1. What is data analysis?

A. Data analysis is only about data collectionB. Data analysis involves inspecting, cleaning, transforming, and modeling dataC. Data analysis is the same as data entryD. Data analysis is not used in business

2. What is data cleaning?

A. Data cleaning is not necessaryB. Data cleaning is only about removing outliersC. Data cleaning involves correcting errors and inconsistencies in dataD. Data cleaning is the same as data analysis

3. What is exploratory data analysis (EDA)?

A. EDA involves summarizing and visualizing data to understand its featuresB. EDA is only for hypothesis testingC. EDA is the same as data cleaningD. EDA is not used in practice

4. What is data visualization?

A. Data visualization is not importantB. Data visualization is only for presentationC. Data visualization is the same as data analysisD. Data visualization helps understand patterns and trends in data

5. What are outliers in data?

A. Outliers are always errorsB. Outliers are data points that differ significantly from othersC. Outliers are always excluded from analysisD. Outliers do not affect analysis

General10 MCQs

6. How is missing data typically handled in analysis?

A. Missing data should always be deletedB. Missing data should always be ignoredC. Missing data can be handled through deletion, imputation, or algorithmic methodsD. Missing data is not a problem

7. What is Principal Component Analysis (PCA)?

A. PCA is used for dimensionality reduction by creating uncorrelated componentsB. PCA is used for classificationC. PCA is the same as factor analysisD. PCA is not used in data analysis

8. What is clustering in data analysis?

A. Clustering is a supervised learning techniqueB. Clustering is used for predictionC. Clustering is the same as classificationD. Clustering groups similar data points together without predefined labels

9. What is the k-means clustering algorithm?

A. k-means is a supervised learning algorithmB. k-means partitions data into k clusters by minimizing within-cluster varianceC. k-means always finds the optimal number of clustersD. k-means is not used for clustering

10. What is the elbow method in clustering?

A. Elbow method is used to select the number of featuresB. Elbow method is used to choose the distance metricC. Elbow method is used to determine the optimal number of clusters in k-meansD. Elbow method is not used in practice

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