MCQs

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Overall MCQs for Reference

1. Invalid data in data science is commonly known as:
   a. changing data
   b. noisy data
   c. outliers
   d. missing data
   Answer: b. noisy datamcqmate​
2. Which of the following is NOT an example of invalid data?
   a. Duplicate records
   b. Correct format values
   c. Outdated information
   d. Inconsistent entries
   Answer: b. Correct format valuesbrightdata​
3. What type of invalid data occurs when values are absent in a dataset?
   a. Incomplete data
   b. Duplicate data
   c. Consistent data
   d. Accurate data
   Answer: a. Incomplete dataencord+1​
4. A phone number field containing alphabetic characters instead of digits is considered:
   a. Outdated data
   b. Incorrect data
   c. Duplicate data
   d. Consistent data
   Answer: b. Incorrect dataedgedelta​
5. Invalid data due to multiple entries for the same record is referred to as:
   a. Outlier data
   b. Duplicate data
   c. Clean data
   d. Consistent data
   Answer: b. Duplicate databrightdata+2​
6. Which approach is NOT suitable for handling invalid data?
   a. Deleting erroneous entries
   b. Ignoring errors completely
   c. Replacing with default values
   d. Correcting entries
   Answer: b. Ignoring errors completelylinkedin​
7. If a dataset contains values with the wrong date format, the data is:
   a. Consistent
   b. Invalid
   c. Duplicate
   d. Accurate
   Answer: b. Invalidlinkedin+1​
8. What is one effect of invalid data in model training?
   a. Improved accuracy
   b. Biased results
   c. Decreased model performance
   d. Random output
   Answer: c. Decreased model performancegeeksforgeeks+1​
9. Bad data refers to:
   a. Data that meets requirements
   b. Inaccurate, irrelevant, or inconsistent data
   c. Highly accurate data
   d. Properly formatted data
   Answer: b. Inaccurate, irrelevant, or inconsistent dataedgedelta​
10. Which type of error involves attributes missing entirely from data records?
    a. Annotation error
    b. Missing data
    c. Duplicate error
    d. Outdated data
    Answer: b. Missing datafirsteigen+1​
11. Consistent data means:
    a. Values follow expected patterns
    b. Contains invalid records
    c. Missing important fields
    d. Data is duplicated
    Answer: a. Values follow expected patternsedgedelta​
12. Which statement about invalid data is correct?
    a. It improves model accuracy
    b. It leads to unreliable analysis
    c. It makes predictions more precise
    d. It always causes overfitting
    Answer: b. It leads to unreliable analysisfirsteigen​
13. The presence of invalid data may require:
    a. Data cleansing
    b. Ignoring analysis
    c. Data encryption
    d. Feature scaling
    Answer: a. Data cleansinggeeksforgeeks​
14. Which is NOT a common cause of invalid data?
    a. Manual entry errors
    b. Software bugs
    c. Strong validations
    d. System glitches
    Answer: c. Strong validationsedgedelta​
15. Outdated data may be considered invalid when:
    a. It reflects current facts
    b. It is no longer relevant
    c. It is used for forecasting
    d. It has duplicate entries
    Answer: b. It is no longer relevantbrightdata​
16. The FIRST step in dealing with invalid data is:
    a. Model deployment
    b. Identifying errors
    c. Feature extraction
    d. Visualization
    Answer: b. Identifying errorsdatawars+1​
17. Data values not aligning with the logical table mapping are classified as:
    a. Valid data
    b. Invalid data
    c. Processed data
    d. Normalized data
    Answer: b. Invalid dataibm​
18. Which technique is appropriate for handling missing values?
    a. Imputation
    b. Ignoring all data
    c. Incorrect mapping
    d. Removing correct records
    Answer: a. Imputationkaggle​
19. Which is considered a systematic approach to bad data?
    a. Ignore data errors
    b. Clean or request new data
    c. Always use original data
    d. Drop correct data
    Answer: b. Clean or request new datatowardsdatascience​
20. Annotation errors during labeling may cause:
    a. Consistent datasets
    b. Invalid or incorrect data
    c. Duplicate data
    d. No change in results
    Answer: b. Invalid or incorrect dataencord​
21. Which bad data type leads to redundancy in predictions?
    a. Non-numeric data
    b. Duplicate data
    c. Clean data
    d. Incomplete data
    Answer: b. Duplicate databrightdata+1​
22. What should be done when encountering entire rows of missing data?
    a. Ignore the row
    b. Delete or impute missing parts
    c. Copy data to backup
    d. Encrypt data
    Answer: b. Delete or impute missing partstowardsdatascience+1​
23. Incorrect forced-choice answer responses result in:
    a. Structured data
    b. Invalid data
    c. Verified data
    d. Normal data
    Answer: b. Invalid datatowardsdatascience​
24. Data collected with the wrong sample is classified as:
    a. Clean data
    b. Invalid data
    c. Outdated data
    d. Annotated data
    Answer: b. Invalid datatowardsdatascience​
25. Which of the following best describes “data error”?
    a. Correct labeling
    b. Mistakes during input, validation, or processing
    c. Supervised learning step
    d. Normal data accuracy
    Answer: b. Mistakes during input, validation, or processingencord​
26. Duplication in records can compromise:
    a. Accuracy
    b. Redundancy
    c. Reliability of analysis
    d. All of the above
    Answer: d. All of the abovefirsteigen+2​
27. Handling invalid data typically involves:
    a. Identifying outliers
    b. Cleaning and transforming entries
    c. Ignoring missing data
    d. Compressing valid data
    Answer: b. Cleaning and transforming entrieslinkedin+1​
28. Which parameter controls how invalid data is handled in table mapping?
    a. CSDATAVALIDATEAC
    b. DATAENTRY
    c. CHECKTABLE
    d. DATATYPEVALIDATOR
    Answer: a. CSDATAVALIDATEACibm​
29. Data that does NOT agree with mapping constraints is called:
    a. Correct
    b. Invalid
    c. Verified
    d. Secure
    Answer: b. Invalidibm​
30. Which is a direct consequence of using invalid data in analytics?
    a. Valid predictions
    b. Misleading results
    c. Data privacy breaches
    d. Enhanced efficiency
    Answer: b. Misleading resultslinkedin+2​
31. Inappropriate field types, such as string in a numeric column, cause:
    a. No issue
    b. Invalid data
    c. Duplicate entries
    d. Consistency in records
    Answer: b. Invalid dataibm​
32. A major reason for invalid data during integration is:
    a. Improper field mapping
    b. Correct schema definitions
    c. Normalized entries
    d. Verified records
    Answer: a. Improper field mappingibm​
33. Undetected invalid data causes model:
    a. Generalization
    b. Overfitting
    c. Data leakage
    d. Degradation
    Answer: d. Degradationfirsteigen+1​
34. Which is NOT a strategy for invalid data handling?
    a. Delete
    b. Correct
    c. Replace
    d. Ignore always
    Answer: d. Ignore alwayskaggle+1​
35. Invalid data in feature columns affects:
    a. Data integrity
    b. ML model accuracy
    c. Analytics reliability
    d. All of the above
    Answer: d. All of the abovelinkedin+2​
36. The term for invalid data that does not fit project aims is:
    a. Clean data
    b. Unsound data
    c. Reliable data
    d. Reference data
    Answer: b. Unsound datatowardsdatascience​
37. Which is NOT a potential impact of invalid data?
    a. Data loss
    b. Biased analysis
    c. Faulty predictions
    d. Consistent outcomes
    Answer: d. Consistent outcomesfirsteigen+1​
38. To minimize invalid data, organizations should:
    a. Enhance validation checks
    b. Reduce user input
    c. Disable logs
    d. Increase error tolerance
    Answer: a. Enhance validation checksedgedelta​
39. The process of identifying invalid data before analysis is:
    a. Data cleaning
    b. Feature selection
    c. Data storage
    d. Data encryption
    Answer: a. Data cleaninggeeksforgeeks​
40. False representation of values, like a negative age, is:
    a. Valid data
    b. Invalid data
    c. Duplicate
    d. Accurate
    Answer: b. Invalid datatowardsdatascience+1​
41. Having inconsistent units (e.g., mixing cm & inches without transformation) leads to:
    a. Valid data
    b. Invalid data
    c. Reliable results
    d. Consistent entries
    Answer: b. Invalid databrightdata+1​
42. Outliers due to invalid measurements are:
    a. Always ignored
    b. Invalid data
    c. Confirmed predictions
    d. Processed features
    Answer: b. Invalid datamcqmate+1​
43. During parsing, handling invalid data may require:
    a. Robust error handling
    b. Ignoring all entries
    c. Reformatting correct data
    d. Disabling parsing
    Answer: a. Robust error handlinglabex​
44. Data entry errors result in:
    a. Incorrect, incomplete, or duplicate data
    b. Consistent labeling
    c. Accurate entries
    d. Unbiased analysis
    Answer: a. Incorrect, incomplete, or duplicate dataedgedelta​
45. Aggregated data with missing values must be:
    a. Imputed or removed
    b. Left unchanged
    c. Increased in volume
    d. Ignored forever
    Answer: a. Imputed or removeddatawars+1​
46. When identification fails, invalid data may be:
    a. Integrated
    b. Overlooked
    c. Both a and b
    d. None of the above
    Answer: c. Both a and blinkedin+1​
47. How can invalid data affect business decision-making?
    a. Accurate decisions
    b. Misleading outcomes
    c. Reduced reliability
    d. Both b and c
    Answer: d. Both b and cfirsteigen+1​
48. What commonly causes invalid data during migration?
    a. Schema mismatch
    b. Record duplication
    c. Proper validation
    d. Data normalization
    Answer: a. Schema mismatchibm+1​
49. In data science, invalid data should be:
    a. Detected and addressed
    b. Always ignored
    c. Encrypted before analysis
    d. Used for predictions
    Answer: a. Detected and addresseddatawars+1​
50. Which is a best practice while handling invalid data?
    a. Validate before analysis
    b. Perform early cleaning steps
    c. Automate detection processes
    d. All of the above
    Answer: d. All of the abovedatawars+2​

1. https://mcqmate.com/discussion/15226/incorrect-or-invalid-data-is-known-as
2. https://www.linkedin.com/advice/0/what-best-strategies-identifying-handling-invalid-b7u6e
3. https://www.ibm.com/docs/en/idr/11.4.0?topic=source-handling-invalid-data
4. https://towardsdatascience.com/a-systematic-approach-to-bad-data-6c7e2f86e5ef/
5. https://brightdata.com/blog/web-data/bad-data-explained
6. https://encord.com/glossary/data-error-definition/
7. https://www.geeksforgeeks.org/data-analysis/data-cleansing-introduction/
8. https://firsteigen.com/blog/10-common-data-quality-issues-and-how-to-solve-them/
9. https://edgedelta.com/company/blog/what-is-bad-data
10. https://www.datawars.io/data-science-project/880b6272-practice-identifying-and-dealing-with-invalid-values
11. https://www.kaggle.com/code/jkanthony/various-ways-to-handle-missing-and-invalid-data
12. https://labex.io/tutorials/python-how-to-manage-invalid-data-in-parsing-451205
13. https://www.interviewbit.com/data-science-mcq/
14. https://www.sanfoundry.com/data-science-questions-answers-basics/
15. https://interviewkickstart.com/blogs/articles/data-science-mcq-answers
16. https://www.gyansetu.in/blog/data-science-objective-type-mcq-quiz-interview-preparation/
17. https://www.placementpreparation.io/mcq/data-science/page/3/
18. https://testbook.com/objective-questions/mcq-on-data-analysis–5eea6a0d39140f30f369e1bb
19. https://mentr-me.com/question/7-most-important-data-science-mcq-with-answers-pdf
20. https://www.youtube.com/watch?v=ExSaPFQ73Kw