Data-Driven Problem Solving

Q1. What is data-driven problem solving?

1. A.Guessing the answer without facts
2. B.Solving problems without any data
3. C.Using data to guide all decisions✓ Correct
4. D.Relying only on gut intuition

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Q2. Purpose of data collection?

1. A.Filling databases with random entries
2. B.Wasting time gathering useless info
3. C.Creating charts for presentations only
4. D.Gather info to understand the problem✓ Correct

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Q3. What is a dataset?

1. A.A programming language to learn
2. B.Structured collection of related data✓ Correct
3. C.A single data point value only
4. D.A server hosting the application

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Q4. Mean of 2,4,6,8,10?

1. A.6✓ Correct
2. B.5
3. C.8
4. D.4

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Q5. Median of 1,3,5,7,9?

1. A.3
2. B.5✓ Correct
3. C.7
4. D.9

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Q6. Bar chart for?

1. A.Showing trends over time periods
2. B.Comparing quantities across categories✓ Correct
3. C.Displaying geographic location data
4. D.Showing parts of a whole circle

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Q7. What is filtering?

1. A.Selecting data meeting criteria✓ Correct
2. B.Changing existing data values
3. C.Adding more data to the set
4. D.Deleting all data from storage

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Q8. Pie chart for?

1. A.Showing trends over time periods
2. B.Displaying individual data points
3. C.Proportions of parts to a whole✓ Correct
4. D.Comparing many different categories

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Q9. Sorting data for?

1. A.Organizing for analysis and search✓ Correct
2. B.Deleting duplicate data entries
3. C.Encrypting data for security
4. D.Making data set size smaller

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Q10. What is data visualization?

1. A.Graphical representation of patterns✓ Correct
2. B.An algorithm for data sorting
3. C.Raw numbers in a spreadsheet
4. D.A database query for records

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Q11. Data preprocessing?

1. A.It is entirely unnecessary to do
2. B.Only formatting the output display
3. C.Deleting data that is not needed
4. D.Clean, transform, and prepare data✓ Correct

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Q12. Standard deviation?

1. A.The average of the dataset values
2. B.The range of the data values
3. C.Measure of spread from the mean✓ Correct
4. D.The most common value in data

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Q13. Outlier detection?

1. A.Points significantly different from rest✓ Correct
2. B.Sorting the data in ascending order
3. C.Counting all the elements in data
4. D.Finding the average of all values

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Q14. Data normalization?

1. A.Deleting abnormal data entries
2. B.Making all values exactly equal
3. C.Sorting data in a specific order
4. D.Scaling values to standard range✓ Correct

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Q15. Scatter plot for?

1. A.Displaying hierarchical data structures
2. B.Showing only time-based trend data
3. C.Relationship between two numeric variables✓ Correct
4. D.Comparing different categories shown

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Q16. Hypothesis testing?

1. A.Ignoring all evidence and results
2. B.Making assumptions without any data
3. C.Statistical method to check a claim✓ Correct
4. D.Guessing the answer randomly given

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Q17. Feature selection?

1. A.Remove all features from the data
2. B.Add random features to the dataset
3. C.Choose the most relevant attributes✓ Correct
4. D.Select all features without filtering

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Q18. A/B testing?

1. A.Alphabetical sorting of data values
2. B.Compare two versions using real data✓ Correct
3. C.A debugging technique for finding bugs
4. D.Testing all possible combinations made

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Q19. Data aggregation?

1. A.Splitting data into smaller pieces
2. B.Formatting data for the display
3. C.Combining data into summary values✓ Correct
4. D.Deleting data from the database

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Q20. Cross-validation?

1. A.Validating data only twice total
2. B.Checking data types for correctness
3. C.Removing all invalid data entries
4. D.Multiple train/test splits for assessment✓ Correct

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Q21. Dimensionality reduction?

1. A.Reduce features, preserve information✓ Correct
2. B.Compressing files on the filesystem
3. C.Deleting rows from the data table
4. D.Reducing disk storage size used

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Q22. What is overfitting?

1. A.Having too little training data
2. B.A model achieving perfect accuracy
3. C.Learns noise, fails on new data✓ Correct
4. D.A model that is far too simple

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Q23. Bias-variance tradeoff?

1. A.Balance simplicity and complexity✓ Correct
2. B.A network optimization technique
3. C.A database optimization approach
4. D.A hiring process consideration

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Q24. What is MapReduce?

1. A.A database type for storing data
2. B.A visualization tool for charts
3. C.Parallel map and reduce for big data✓ Correct
4. D.A sorting algorithm for arrays

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Q25. Simpson's Paradox?

1. A.Trend reverses when groups combined✓ Correct
2. B.A sorting algorithm special case
3. C.A code error in the implementation
4. D.A normal expected statistical result

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Q26. Curse of dimensionality?

1. A.Having too few data dimensions
2. B.A graphics rendering limitation
3. C.More features makes data sparser✓ Correct
4. D.Simple linear scaling of the data

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Q27. Time series analysis?

1. A.Sorting data by alphabetical name
2. B.Counting occurrences in a dataset
3. C.Analyzing static unchanging data only
4. D.Temporal data for trends and forecasts✓ Correct

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Q28. Ensemble learning?

1. A.A data cleaning preprocessing step
2. B.Using a single model for predictions
3. C.A database optimization technique used
4. D.Combining multiple models for accuracy✓ Correct

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Q29. Data lineage?

1. A.Formatting data for the display
2. B.Deleting data after processing it
3. C.The age of the data in storage
4. D.Tracking origin and transformations✓ Correct

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Q30. Streaming vs batch?

1. A.Streaming: real-time; batch: scheduled✓ Correct
2. B.Streaming is always much slower
3. C.They are exactly the same approach
4. D.Batch processing is always real-time

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