Class 12 AI (843): Data Science Methodology MCQs
Our Data Science Methodology MCQs with Answers are carefully prepared based on the latest CBSE syllabus and previous years’ question patterns. Solving these questions will improve your conceptual understanding, help you recognize the style of CBSE questions, and boost your confidence, speed, and accuracy in the exam.
🚀 Let’s Dive into the latest MCQs of Data Science Methodology and get exam-ready with confidence.
Q1. What is the main purpose of Data Science Methodology?
a) To design websites
b) To organize and solve AI projects systematically
c) To create databases only
d) To write Python programs
Q2. Data Science Methodology consists of ______ iterative steps.
a) 5
b) 8
c) 10
d) 12
Q3. Who proposed the Data Science Methodology discussed in the chapter?
a) Andrew Ng
b) John Rollins
c) Geoffrey Hinton
d) Yann LeCun
Q4. Which is the first stage of Data Science Methodology?
a) Data Collection
b) AI Modelling
c) Business Understanding
d) Deployment
Q5. During Business Understanding, the team mainly tries to:
a) Train the model
b) Understand the customer’s problem
c) Evaluate the model
d) Deploy the solution
Q6. Which framework helps in deeply understanding the problem?
a) SWOT Analysis
b) 5W1H Problem Canvas
c) ER Diagram
d) IPO Chart
Q7. Which framework is also used during Business Understanding?
a) Agile Framework
b) Design Thinking
c) Waterfall Model
d) Spiral Model
Q8. Which analytical approach is suitable for predicting a numerical value?
a) Classification
b) Clustering
c) Regression
d) Recommendation
Q9. “Which category does the data belong to?” refers to:
a) Regression
b) Classification
c) Clustering
d) Recommendation
Q10. Grouping similar data points is known as:
a) Regression
b) Classification
c) Clustering
d) Recommendation
Q11. Detecting unusual patterns in data is called:
a) Classification
b) Recommendation
c) Anomaly Detection
d) Regression
Q12. Suggesting suitable products to customers is an example of:
a) Recommendation
b) Regression
c) Clustering
d) Classification
Q13. Which analytics focuses on summarizing historical data?
a) Predictive
b) Prescriptive
c) Descriptive
d) Diagnostic
Q14. Finding the reason behind a fall in company sales is an example of:
a) Predictive Analytics
b) Diagnostic Analytics
c) Descriptive Analytics
d) Prescriptive Analytics
Q15. Forecasting future sales using previous data is an example of:
a) Diagnostic Analytics
b) Predictive Analytics
c) Descriptive Analytics
d) Prescriptive Analytics
Q16. Which analytics recommends the best action to achieve a goal?
a) Predictive
b) Prescriptive
c) Diagnostic
d) Descriptive
Q17. Which statistical measure represents the average value?
a) Mode
b) Median
c) Mean
d) Range
Q18. Which stage identifies the type, format and source of required data?
a) Data Collection
b) Data Requirements
c) Data Preparation
d) Data Understanding
Q19. Data organized in rows and columns is called:
a) Semi-structured Data
b) Structured Data
c) Unstructured Data
d) Random Data
Q20. Which of the following is an example of unstructured data?
a) Customer database
b) Spreadsheet
c) Social media images
d) Excel table
Q21. Emails and XML files are examples of:
a) Structured Data
b) Semi-structured Data
c) Random Data
d) Tabular Data
Q22. Data collected directly through surveys or interviews is called:
a) Secondary Data
b) Structured Data
c) Primary Data
d) Processed Data
Q23. Which of the following is a secondary data source?
a) Questionnaire
b) Survey
c) Website
d) Interview
Q24. Which online platform is mentioned as a source of secondary data?
a) Canva
b) Kaggle
c) WhatsApp
d) Zoom
Q25. The main objective of Data Understanding is to:
a) Write Python code
b) Check whether collected data represents the problem
c) Train the model
d) Deploy the model
Q26. Which visualization technique is specifically mentioned for Data Understanding?
a) Pie Chart
b) Histogram
c) Flowchart
d) Line Graph
Q27. Which stage includes handling missing and duplicate values?
a) Data Collection
b) Data Preparation
c) Business Understanding
d) Deployment
Q28. Feature Engineering is a part of:
a) Data Collection
b) Data Preparation
c) Data Understanding
d) Feedback
Q29. Feature Engineering mainly involves:
a) Collecting data
b) Creating or modifying features to improve model performance
c) Deploying AI models
d) Testing software
Q30. According to the chapter, which stage is considered the most time-consuming?
a) Business Understanding
b) Data Preparation
c) Evaluation
d) Deployment
Q31. Which stage develops AI models according to the chosen analytical approach?
a) Data Collection
b) AI Modelling
c) Deployment
d) Feedback
Q32. Descriptive modelling mainly focuses on:
a) Predicting future values
b) Recommending actions
c) Understanding and summarizing existing data
d) Classifying images
Q33. Which of the following is a descriptive modelling technique?
a) Regression
b) Classification
c) Histogram
d) Decision Tree
Q34. Predictive modelling mainly uses:
a) Historical data
b) Random guesses
c) Future data
d) Unrelated data
Q35. Which technique is commonly used in predictive modelling?
a) Pie Chart
b) Regression
c) Histogram
d) Mean
Q36. A training dataset is mainly used to:
a) Evaluate the model
b) Deploy the model
c) Train the machine learning model
d) Collect data
Q37. Which stage checks whether the developed model answers the original problem?
a) Data Collection
b) Evaluation
c) Deployment
d) Feedback
Q38. Which dataset is mainly used to measure model performance?
a) Training Dataset
b) Validation Dataset
c) Test Dataset
d) Raw Dataset
Q39. Which of the following is an evaluation metric mentioned in the chapter?
a) Accuracy
b) Resolution
c) Brightness
d) Compression
Q40. The first phase of model evaluation is called:
a) Statistical Analysis
b) Diagnostic Measures
c) Data Cleaning
d) Deployment Testing
Q41. Statistical significance testing is mainly used to:
a) Collect data
b) Verify that the model correctly processes data
c) Train the model
d) Remove duplicate values
Q42. Deployment means:
a) Cleaning the dataset
b) Making the trained model available to users
c) Collecting feedback
d) Creating features
Q43. Before deploying a model, the data scientist should ensure that:
a) The dataset is small
b) The model has been evaluated successfully
c) Python is installed
d) Only training data is available
Q44. Which stage collects users’ opinions after deployment?
a) Data Understanding
b) Feedback
c) Modelling
d) Data Collection
Q45. Feedback helps in:
a) Increasing file size
b) Refining the model
c) Reducing programming languages
d) Removing stakeholders
Q46. Model Validation is performed:
a) Before data collection
b) After model training
c) Before business understanding
d) Before feature engineering
Q47. One major benefit of Model Validation is:
a) Increases duplicate data
b) Prevents overfitting and underfitting
c) Reduces training data
d) Removes features
Q48. Which validation technique divides data into training and testing datasets?
a) K-Means
b) Train-Test Split
c) PCA
d) Clustering
Q49. Which dataset is used to fit the machine learning model?
a) Test Dataset
b) Validation Dataset
c) Train Dataset
d) Random Dataset
Q50. Which is a commonly used Train-Test Split ratio?
a) 20:80
b) 80:20
c) 10:90
d) 40:60
Q51. Cross Validation is generally preferred for:
a) Very large datasets
b) Small datasets
c) Empty datasets
d) Image editing
Q52. In K-Fold Cross Validation, every data point:
a) Is always part of the training set only
b) Is never tested
c) Becomes part of both training and testing at different stages
d) Is removed after one iteration
Q53. If a dataset is divided into 5 equal folds, the value of k is:
a) 2
b) 4
c) 5
d) 10
Q54. Which evaluation metric uses TP, TN, FP and FN?
a) MAE
b) RMSE
c) Accuracy
d) Range
Q55. In a Confusion Matrix, True Positive means:
a) Predicted Yes, Actual Yes
b) Predicted No, Actual Yes
c) Predicted Yes, Actual No
d) Predicted No, Actual No
Q56. Precision measures:
a) Correctly predicted positives out of predicted positives
b) Correctly predicted negatives
c) Average prediction
d) Total predictions
Q57. Recall measures:
a) Correctly predicted positives out of actual positives
b) Correctly predicted negatives
c) Prediction speed
d) Average error
Q58. A perfect F1 Score is:
a) 0
b) 0.5
c) 1
d) 100
Q59. Which metric is mainly used to evaluate regression models?
a) Precision
b) Recall
c) MSE
d) Confusion Matrix
Q60. RMSE is preferred over MSE because:
a) It is always zero
b) It is easier to interpret as it has the same unit as the target variable
c) It uses percentages
d) It works only for classification
Q61. MSE stands for:
a) Mean Statistical Error
b) Maximum Squared Error
c) Mean Square Error
d) Model Standard Evaluation
Q62. RMSE stands for:
a) Root Mean Square Error
b) Relative Mean Square Error
c) Regression Mean Standard Error
d) Root Model Scoring Evaluation
Q63. False Negatives (FN) occur when:
a) Model predicted No, Actual was Yes
b) Model predicted Yes, Actual was No
c) Model predicted No, Actual was No
d) Model predicted Yes, Actual was Yes
Q64. What is the formula for Precision?
a) TP / (TP + FN)
b) TP / (TP + FP)
c) (TP + TN) / Total
d) TN / (TN + FP)
Q65. What proportion of actual positives is correctly classified is measured by:
a) Precision
b) Recall
c) Accuracy
d) Mean Absolute Error
Q66. What is the mathematical formula for Recall?
a) TP / (TP + FP)
b) TP / (TP + FN)
c) TN / (TN + FP)
d) FP / (TP + FP)
