MCQs

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Overall MCQs for Reference

1. What is a label in data science?
   a. A type of input feature
   b. The target variable to predict
   c. A model parameter
   d. Noise in the dataset
   Answer: b. The target variable to predicttestbook+1​
2. In supervised learning, what is required for training?
   a. Only input features
   b. Only output labels
   c. Both input features and output labels
   d. None of the above
   Answer: c. Both input features and output labelsaimcqs+1​
3. Which learning type uses labeled data?
   a. Unsupervised learning
   b. Supervised learning
   c. Reinforcement learning
   d. None
   Answer: b. Supervised learninggyansetu+1​
4. What is the main role of labels in classification tasks?
   a. Identify clusters
   b. Assign categories to data points
   c. Normalize data
   d. Remove outliers
   Answer: b. Assign categories to data pointsscribd+1​
5. In a regression problem, what do the labels represent?
   a. Categorical outputs
   b. Continuous values
   c. Features
   d. Clusters
   Answer: b. Continuous valueseasyexamnotes+1​
6. Which ML task requires labeled data for training?
   a. Clustering
   b. Classification
   c. Dimensionality reduction
   d. Association rule mining
   Answer: b. Classificationaimcqs+1​
7. What is data annotation?
   a. Adding labels to data
   b. Removing noise
   c. Computing error metrics
   d. Model tuning
   Answer: a. Adding labels to datausebraintrust+1​
8. Which algorithm typically does NOT require labels?
   a. k-Means
   b. Decision Tree
   c. SVM
   d. Logistic Regression
   Answer: a. k-Meanssanfoundry+1​
9. What is the consequence of missing labels in supervised training?
   a. Improved model accuracy
   b. Inability to train the model
   c. Faster training
   d. More features required
   Answer: b. Inability to train the modelplacementpreparation+1​
10. Which process transforms categorical features into numerical labels?
    a. Binarization
    b. Label Encoding
    c. Normalization
    d. Standardization
    Answer: b. Label Encodingscribd​
11. In a labeled dataset, what do the labels correspond to?
    a. Input features
    b. Target output values
    c. Model weights
    d. Epochs
    Answer: b. Target output valueseasyexamnotes​
12. What does ground truth mean in data labeling?
    a. The input feature
    b. The actual real-world value
    c. Regularization method
    d. Training loss
    Answer: b. The actual real-world valueusebraintrust​
13. In NLP, what could a label represent?
    a. Word frequency
    b. Sentiment category
    c. Part of speech tag
    d. Lemma
    Answer: b. Sentiment categoryindeed​
14. For image classification, labels commonly indicate:
    a. File format
    b. Image dimensions
    c. Object category depicted
    d. Image resolution
    Answer: c. Object category depictedusebraintrust​
15. What is label noise?
    a. High model accuracy
    b. Incorrectly assigned labels
    c. Missing data points
    d. Redundant features
    Answer: b. Incorrectly assigned labelsusebraintrust​
16. When evaluating a model, labels are used to:
    a. Generate input features
    b. Measure prediction accuracy
    c. Set learning rate
    d. Choose a loss function
    Answer: b. Measure prediction accuracyindeed​
17. Unlabeled data is used in which task?
    a. Classification
    b. Regression
    c. Clustering
    d. Forecasting
    Answer: c. Clusteringsanfoundry+1​
18. Which step involves labeling in the data science pipeline?
    a. Data collection
    b. Data annotation
    c. Feature engineering
    d. Model deployment
    Answer: b. Data annotationindeed+1​
19. In binary classification, how many labels are present?
    a. One
    b. Two
    c. Three
    d. Four
    Answer: b. Twoeasyexamnotes​
20. What is label imbalance?
    a. Equal distribution of classes
    b. Unequal representation of classes
    c. Balanced feature weights
    d. Feature redundancy
    Answer: b. Unequal representation of classesusebraintrust​
21. Which data type requires labels for sentiment analysis?
    a. Images
    b. Text
    c. Audio
    d. Video
    Answer: b. Textusebraintrust​
22. Which ML task predicts numerical labels?
    a. Classification
    b. Regression
    c. Clustering
    d. Summarization
    Answer: b. Regressioneasyexamnotes​
23. How are labels assigned in manual annotation?
    a. Automatically by script
    b. Manually by humans
    c. Randomly by computer
    d. Based on noisy data
    Answer: b. Manually by humansusebraintrust​
24. Why is consistency important in data labeling?
    a. Improves annotation speed
    b. Ensures model learns correct patterns
    c. Reduces feature count
    d. Decreases computational cost
    Answer: b. Ensures model learns correct patternsusebraintrust​
25. In kNN, the label for a new data point is:
    a. Most common label among k neighbors
    b. Mean of feature values
    c. Randomly assigned
    d. Highest value feature
    Answer: a. Most common label among k neighborsgeeksforgeeks​
26. What is label propagation?
    a. Unsupervised clustering technique
    b. Spreading label information to unlabeled data
    c. Overfitting removal method
    d. Feature selection algorithm
    Answer: b. Spreading label information to unlabeled datausebraintrust​
27. A model trained only on correct labels will likely:
    a. Overfit the noise
    b. Generalize better
    c. Increase feature count
    d. Reduce accuracy
    Answer: b. Generalize betterusebraintrust​
28. Which regularization method addresses noisy labels?
    a. Lasso
    b. Label Smoothing
    c. Ridge
    d. Dropout
    Answer: b. Label Smoothingusebraintrust​
29. Which tool is often used for image labeling?
    a. Jupyter Notebook
    b. LabelImg
    c. NumPy
    d. Pandas
    Answer: b. LabelImgusebraintrust​
30. What does multi-label classification mean?
    a. Each instance has one label
    b. Each instance can have multiple labels
    c. Only binary classes
    d. Clustering based labels
    Answer: b. Each instance can have multiple labelsusebraintrust​
31. Why is label validation necessary?
    a. To optimize hyperparameters
    b. To ensure annotation quality
    c. To improve model speed
    d. To avoid overfitting
    Answer: b. To ensure annotation qualityindeed​
32. In data science, label mapping refers to:
    a. Assigning model weights
    b. Matching labels to corresponding data points
    c. Binarizing features
    d. Cross-validating results
    Answer: b. Matching labels to corresponding data pointsusebraintrust​
33. Which ML paradigm uses rewards as labels?
    a. Supervised learning
    b. Reinforcement learning
    c. Clustering
    d. Linear regression
    Answer: b. Reinforcement learningtestbook​
34. What is the impact of ambiguous labels?
    a. Improved model generalization
    b. Reduced model accuracy
    c. Increased data annotation speed
    d. None
    Answer: b. Reduced model accuracyusebraintrust​
35. In classification tasks, label encoding helps to:
    a. Sort labels alphabetically
    b. Convert categories to numerical labels
    c. Drop duplicate labels
    d. Merge similar labels
    Answer: b. Convert categories to numerical labelsscribd​
36. For time series forecasting, labels are often:
    a. Category tags
    b. Numeric future values
    c. Feature names
    d. Group IDs
    Answer: b. Numeric future valuestestbook​
37. What is label leakage?
    a. Features are correlated with labels inappropriately
    b. Labels are correctly annotated
    c. Model hyperparameter tuning
    d. Regularization method
    Answer: a. Features are correlated with labels inappropriatelyplacementpreparation​
38. Which is NOT a best practice for data labeling?
    a. Following guidelines
    b. Performing quality control
    c. Ignoring annotation feedback
    d. Ensuring annotator consistency
    Answer: c. Ignoring annotation feedbackindeed+1​
39. For audio data, a label could represent:
    a. Duration
    b. Speaker identity
    c. File name
    d. Bitrate
    Answer: b. Speaker identityusebraintrust​
40. Multi-class classification assigns:
    a. Multiple labels to each instance
    b. One label out of many classes to each instance
    c. Only binary labels
    d. No labels
    Answer: b. One label out of many classes to each instanceexamveda​
41. Why do models underperform with poor annotation?
    a. They learn irrelevant data patterns
    b. They generalize better
    c. They increase compute cost
    d. None
    Answer: a. They learn irrelevant data patternsusebraintrust​
42. Label imbalance can be addressed by:
    a. Over-sampling minority classes
    b. Under-sampling majority classes
    c. Weighted loss functions
    d. All of the above
    Answer: d. All of the aboveusebraintrust​
43. Which ML library provides LabelEncoder for label transformation?
    a. NumPy
    b. scikit-learn
    c. TensorFlow
    d. Matplotlib
    Answer: b. scikit-learnaimcqs​
44. What is active learning in labeling context?
    a. Training without any labels
    b. Model requests labels for most informative data points
    c. Random label assignment
    d. Annotating all data points
    Answer: b. Model requests labels for most informative data pointsusebraintrust​
45. What does one-hot encoding do to labels?
    a. Merges labels
    b. Converts labels into binary vectors
    c. Drops labels
    d. Sorts labels
    Answer: b. Converts labels into binary vectorsscribd​
46. Labeling for anomaly detection usually involves:
    a. Assigning normal/anomaly tags
    b. Input feature selection
    c. Output normalization
    d. Label smoothing
    Answer: a. Assigning normal/anomaly tagsusebraintrust​
47. What distinguishes training data in supervised learning?
    a. It is unlabeled
    b. It contains both features and labels
    c. Only labels are present
    d. Only features are present
    Answer: b. It contains both features and labelsexamveda+1​
48. What is a label index in a dataset?
    a. Index of features
    b. Index corresponding to class labels
    c. Model parameter for learning rate
    d. Feature scaling metric
    Answer: b. Index corresponding to class labelssanfoundry​
49. In object detection, annotations typically consist of:
    a. Bounding boxes and labels
    b. Model weights alone
    c. Only image pixels
    d. Color histogram
    Answer: a. Bounding boxes and labelsusebraintrust​
50. What is the role of annotation guidelines for labeling?
    a. Reduce annotation costs
    b. Ensure labels are applied consistently and accurately
    c. Drop irrelevant features
    d. None
    Answer: b. Ensure labels are applied consistently and accuratelyindeed+1​

1. https://testbook.com/question-answer/which-of-the-following-learnings-uses-manual-label–637c60c180cd5960906e2b13
2. https://easyexamnotes.com/machine-learning-fundamentals-mcqs/
3. https://aimcqs.com/supervised-learning
4. https://www.sanfoundry.com/1000-machine-learning-questions-answers/
5. https://www.gyansetu.in/blog/machine-learning-mcq-interview-preparation/
6. https://www.scribd.com/document/856379688/Data-Science-100-MCQs
7. https://www.usebraintrust.com/hire/interview-questions/data-annotators
8. https://in.indeed.com/career-advice/interviewing/data-annotation-interview-questions
9. https://www.placementpreparation.io/mcq/data-science/page/3/
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12. https://www.sanfoundry.com/data-science-questions-answers-pandas-data-structure/
13. https://www.gyansetu.in/blog/data-science-objective-type-mcq-quiz-interview-preparation/
14. https://easyexamnotes.com/data-science-mcqs/
15. https://www.youtube.com/watch?v=FugpqOEPuBg
16. https://www.wecreateproblems.com/tests/data-annotation-specialist-assessment-test
17. https://www.placementpreparation.io/mcq/machine-learning/
18. https://www.expertratinginc.com/testsyllabus.aspx?examid=15186&catid=269
19. https://www.scribd.com/document/513570416/ACFrOgCJFUigNteKuu50LJxpBGcbPjZqsROcKBB5PyM5KE-Q-oAs-YZJ3IADaoXffYVg95KJOlVhH3Y7i3EhDpSdahDSH67nvSoQ0jRcRPfnxznydI9KqW1NG5BlF4MYakTohHZdry6b99-9xv5H
20. https://www.gyansetu.in/blog/data-analytics-mcq-quiz-interview-preparation/