MCQs

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Overall MCQs for Reference

1. Which metric is used to evaluate the performance of a classification model by calculating the overall percentage of correct predictions?
   a. Precision
   b. Accuracy
   c. Recall
   d. F1 Score
   Answer: b. Accuracylearncbse+1​
2. What does precision measure in an AI classification task?
   a. The fraction of true negatives over all negatives
   b. The fraction of true positives over all predicted positives
   c. The fraction of false positives over all positives
   d. The fraction of true positives over all actual positives
   Answer: b. The fraction of true positives over all predicted positivesversion1+1​
3. What metric is used to balance precision and recall in a single value?
   a. Accuracy
   b. AUROC
   c. F1 Score
   d. R-squared
   Answer: c. F1 Scorelearncbse+1​
4. Which two evaluation metrics are combined to calculate the F1 score?
   a. Precision & Accuracy
   b. Precision & Recall
   c. Accuracy & Recall
   d. Precision & F1 score
   Answer: b. Precision & Recalllearncbse​
5. Which metric is especially important for imbalanced datasets?
   a. Accuracy
   b. Precision
   c. Recall
   d. F1 Score
   Answer: d. F1 Scorefutureskillsacademy+1​
6. What evaluation metric is suitable for regression models to measure the average absolute difference between predicted and actual values?
   a. Mean Squared Error (MSE)
   b. F1 Score
   c. Precision
   d. Recall
   Answer: a. Mean Squared Error (MSE)examveda+1​
7. What evaluation metric refers to the ratio of true positives to the sum of true positives and false negatives?
   a. Accuracy
   b. Precision
   c. Recall
   d. Specificity
   Answer: c. Recallversion1+1​
8. Which metric is the ratio of true positives to all positive predictions made by the model?
   a. Specificity
   b. Precision
   c. Recall
   d. Accuracy
   Answer: b. Precisionversion1+1​
9. What does a confusion matrix provide in model evaluation?
   a. Insights into data distribution
   b. Summary of correct and incorrect predictions by type
   c. Data normalization statistics
   d. Algorithm efficiency score
   Answer: b. Summary of correct and incorrect predictions by typelearncbse​
10. When is AUROC (Area Under the Receiver Operating Characteristic Curve) most useful?
    a. Comparing precision values
    b. Evaluating regression error
    c. Assessing model performance across thresholds
    d. Calculating feature importance
    Answer: c. Assessing model performance across thresholdssanfoundry+1​
11. Which metric measures the proportion of actual negatives that are correctly identified?
    a. Recall
    b. Precision
    c. Specificity
    d. F1 Score
    Answer: c. Specificitynature+1​
12. What is the best metric to consider when false negatives are very costly?
    a. Accuracy
    b. Precision
    c. Recall
    d. Specificity
    Answer: c. Recallscribd+1​
13. Which evaluation metric is commonly used for ranking or recommendation systems?
    a. Precision@K
    b. Mean Squared Error
    c. Confusion Matrix
    d. ROC Curve
    Answer: a. Precision@Kevidentlyai​
14. In the confusion matrix, what does a “True Negative” represent?
    a. Model correctly predicts positive
    b. Actual value is positive and predicted as negative
    c. Model correctly predicts negative
    d. Both prediction and reality are positive
    Answer: c. Model correctly predicts negativelearncbse​
15. What does R-squared measure in regression models?
    a. Explained variance
    b. Classification accuracy
    c. Precision-recall balance
    d. F1 harmony
    Answer: a. Explained variancenature​
16. Which metric is mostly used when classes are highly imbalanced in classification problems?
    a. Precision
    b. Recall
    c. F1 Score
    d. R-squared
    Answer: c. F1 Scoreinterviewkickstart+1​
17. What is the main purpose of evaluation metrics in AI?
    a. To measure model efficiency
    b. To compare algorithm speed
    c. To quantify model performance
    d. To build feature sets
    Answer: c. To quantify model performancescribd+1​
18. What does “bias” as an evaluation metric imply in AI systems?
    a. Positive class prediction rate
    b. Systemic error or unfairness in outcomes
    c. Total data points
    d. Model’s speed of execution
    Answer: b. Systemic error or unfairness in outcomesconfident-ai​
19. Which metric combines both the positive and negative predictive values for a balanced assessment?
    a. Accuracy
    b. Recall
    c. Precision
    d. MSE
    Answer: a. Accuracyversion1+1​
20. When is Mean Absolute Error (MAE) typically used?
    a. In binary classification
    b. In regression model evaluation
    c. To compare feature distributions
    d. As a cluster validation metric
    Answer: b. In regression model evaluationexamveda+1​
21. What does “Recall” measure?
    a. Fraction of actual positives identified correctly
    b. Fraction of negatives misclassified
    c. Average predicted scores
    d. Number of features used
    Answer: a. Fraction of actual positives identified correctlyversion1+1​
22. What is the purpose of ROC-AUC in model evaluation?
    a. Determine speed of algorithm
    b. Evaluate feature importance
    c. Quantify the trade-off between true positive rate and false positive rate
    d. Analyze data size
    Answer: c. Quantify the trade-off between true positive rate and false positive ratesanfoundry+1​
23. Which metric is inappropriate for evaluating an imbalanced binary classification problem?
    a. F1 Score
    b. Precision
    c. Accuracy
    d. Recall
    Answer: c. Accuracyfutureskillsacademy+1​
24. What does “False Positive Rate” (FPR) mean?
    a. Ratio of negative samples predicted as positive out of actual negatives
    b. Ratio of positive samples predicted as negative out of all positives
    c. Ratio of correct negative predictions over all predictions
    d. Ratio of positives predicted correctly out of all positive predictions
    Answer: a. Ratio of negative samples predicted as positive out of actual negativesnature​
25. Which metric best describes the average squared difference between predicted and actual values?
    a. F1 Score
    b. ROC-AUC
    c. Mean Squared Error (MSE)
    d. Precision@K
    Answer: c. Mean Squared Error (MSE)examveda+1​
26. What is “Matthews Correlation Coefficient” useful for?
    a. Binary classification evaluation (balanced/unbalanced)
    b. Clustering validation
    c. Regression error
    d. Ranking tasks
    Answer: a. Binary classification evaluation (balanced/unbalanced)nature​
27. Which evaluation method is most useful for multi-class classification problems?
    a. Hamming loss
    b. Precision@K
    c. R-squared
    d. ROC-AUC
    Answer: a. Hamming lossnature​
28. What metric evaluates a model’s ability to rank the positive instances higher than negative ones?
    a. Precision
    b. ROC-AUC
    c. Specificity
    d. MAE
    Answer: b. ROC-AUCnature​
29. Which is NOT a typical regression evaluation metric?
    a. Mean Absolute Error
    b. R-squared
    c. Precision
    d. Root Mean Squared Error
    Answer: c. Precisionexamveda+1​
30. Which metric measures the error as a percentage of the true value in regression?
    a. MAPE (Mean Absolute Percentage Error)
    b. F1 Score
    c. Recall
    d. ROC-AUC
    Answer: a. MAPEnature​
31. Which refers to the fraction of retrieved instances that are relevant?
    a. Recall
    b. Precision
    c. F1 Score
    d. Specificity
    Answer: b. Precisionlearncbse+1​
32. If a model outputs “yes” and the actual label is “yes”, what is this termed?
    a. False Positive
    b. True Positive
    c. True Negative
    d. False Negative
    Answer: b. True Positivelearncbse​
33. Which metric is used for measuring the diversity in recommendations?
    a. Precision@K
    b. Serendipity
    c. F1 Score
    d. R-squared
    Answer: b. Serendipityevidentlyai​
34. The fraction of positive examples correctly classified is known as:
    a. Precision
    b. Recall
    c. Specificity
    d. MAE
    Answer: b. Recallversion1+1​
35. Which is more suitable for multi-label classification tasks?
    a. Hamming loss
    b. Precision
    c. F1 Score
    d. R-squared
    Answer: a. Hamming lossnature​
36. What metric measures the average log loss incurred by predicted probabilities for true labels?
    a. Logarithmic loss (Log Loss)
    b. F1 Score
    c. R-squared
    d. ROC-AUC
    Answer: a. Logarithmic loss (Log Loss)nature​
37. Which metric is useful for object detection to evaluate overlap with ground truth?
    a. IoU (Intersection over Union)
    b. Recall
    c. R-squared
    d. MAE
    Answer: a. IoU (Intersection over Union)nature​
38. What does “balanced accuracy” account for?
    a. Imbalanced classes by averaging sensitivity and specificity
    b. Only true negatives
    c. Number of features in the data
    d. Test execution time
    Answer: a. Imbalanced classes by averaging sensitivity and specificitynature​
39. What metric captures how similar predicted rankings are to actual rankings?
    a. NDCG (Normalized Discounted Cumulative Gain)
    b. Recall
    c. ROC-AUC
    d. R-squared
    Answer: a. NDCG (Normalized Discounted Cumulative Gain)evidentlyai​
40. What is the ideal value of F1 Score for a perfect model?
    a. 0
    b. 0.5
    c. 1
    d. Cannot be calculated
    Answer: c. 1scribd+1​
41. Which metric determines the ranking quality for recommender systems?
    a. Precision@K
    b. RMSE
    c. Recall
    d. Log Loss
    Answer: a. Precision@Kevidentlyai​
42. Which metric is best used for text generation or summarization model evaluation?
    a. BLEU (Bilingual Evaluation Understudy)
    b. ROC-AUC
    c. Precision
    d. MAE
    Answer: a. BLEU (Bilingual Evaluation Understudy)confident-ai​
43. Which metric is best for measuring “correctness” in LLM (Language Model) outputs?
    a. Semantic similarity
    b. R-squared
    c. RMSE
    d. Hamming loss
    Answer: a. Semantic similarityconfident-ai​
44. Which metric penalizes larger errors more than smaller ones in regression?
    a. RMSE (Root Mean Squared Error)
    b. Accuracy
    c. Recall
    d. ROC-AUC
    Answer: a. RMSE (Root Mean Squared Error)nature​
45. When the model’s predictions don’t reflect actual real-world outcomes, what evaluation gap does this suggest?
    a. Bias and fairness
    b. High recall
    c. R-squared
    d. NDCG metric
    Answer: a. Bias and fairnessconfident-ai+1​
46. If a binary classifier has high precision but low recall, what does this indicate?
    a. Many false negatives but few false positives
    b. High false positives
    c. Model overfitting
    d. High specificity
    Answer: a. Many false negatives but few false positivesversion1​
47. For a model predicting house prices, which metric is more intuitive for stakeholders?
    a. MAE (Mean Absolute Error)
    b. F1 Score
    c. Precision
    d. ROC-AUC
    Answer: a. MAE (Mean Absolute Error)examveda+1​
48. For evaluating generative models like LLMs, what new metric methods are favored?
    a. LLM-as-a-judge
    b. ROC Curve
    c. Specificity
    d. RMSE
    Answer: a. LLM-as-a-judgeconfident-ai​
49. What metric detects if a model generates offensive or biased content?
    a. Responsible metrics (toxicity, bias)
    b. Specificity
    c. RMSE
    d. NDCG
    Answer: a. Responsible metrics (toxicity, bias)confident-ai​
50. What is the last stage of an AI project life cycle regarding model building?
    a. Data Preparation
    b. Modeling
    c. Evaluation
    d. Deployment
    Answer: c. Evaluationlearncbse​

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4. https://interviewkickstart.com/blogs/articles/machine-learning-mcqs-answers
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12. https://www.confident-ai.com/blog/llm-evaluation-metrics-everything-you-need-for-llm-evaluation
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14. https://www.scribd.com/document/701694994/04-evaluation-important-questions-answers
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17. https://learn.microsoft.com/en-us/ai/playbook/technology-guidance/generative-ai/working-with-llms/evaluation/list-of-eval-metrics
18. https://www.cliffsnotes.com/study-notes/23641847
19. https://www.geeksforgeeks.org/machine-learning/metrics-for-machine-learning-model/
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