MCQs

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Overall MCQs for Reference

Which type of data is best represented as “date values” in machine learning?
a. Categorical
b. Numerical
c. Temporal
d. Boolean
Answer: c. Temporalexamveda+1
What is the primary reason for extracting features like year, month, or weekday from a date column in a dataset?
a. To increase column count
b. To capture time-based patterns
c. To reduce dataset size
d. To anonymize data
Answer: b. To capture time-based patternsdeveloper.nvidia+1
Which function would be used in pandas to extract the month from a DateTime column?
a. df[‘date’].get_month()
b. df[‘date’].month()
c. df[‘date’].dt.month
d. df[‘date’].extract_month()
Answer: c. df[‘date’].dt.monthstackoverflow
Why is it usually a bad idea to directly use raw date values (e.g., “2025-10-31”) in most machine learning models?
a. They are too long
b. They are non-numeric
c. They have no relationship to time intervals
d. All of the above
Answer: d. All of the abovedeveloper.nvidia+1
Seasonality in date/time values is best captured by extracting which feature?
a. Row index
b. Month or week
c. Label encoding
d. Image encoding
Answer: b. Month or weekexamveda
Which technique is recommended for encoding cyclical date features like hours or days?
a. One-hot encoding
b. Sine and cosine transformation
c. Linear scaling
d. Dropping the feature
Answer: b. Sine and cosine transformationkaggle+2
What is a possible issue with one-hot encoding month values in time series forecasting?
a. High cardinality
b. Loss of cyclic relationship
c. Duplication of information
d. Alphabetical sorting
Answer: b. Loss of cyclic relationshipstackoverflow+1
In pandas, which method converts a string column to datetime?
a. pd.to_datetime()
b. pd.as_datetime()
c. pd.string_to_date()
d. pd.date_convert()
Answer: a. pd.to_datetime()stackoverflow
What is the most common base or “origin” date in Unix timestamp formats?
a. 2000-01-01
b. 2020-01-01
c. 1970-01-01
d. 1960-01-01
Answer: c. 1970-01-01stackoverflow
What are “lags” in time series feature engineering?
a. Numerical representations of labels
b. Previous time steps used as features
c. Clusters of date features
d. Outliers
Answer: b. Previous time steps used as featuresitfeature+1
Which date-based feature is most useful for capturing weekly seasonality?
a. Month
b. Quarter
c. Day of week
d. Year
Answer: c. Day of weekdeveloper.nvidia+1
Why should you avoid label encoding for date components such as months?
a. Causes memory leaks
b. Introduces ordinal relationships not present
c. Slows down training
d. Removes missing dates
Answer: b. Introduces ordinal relationships not presentgeeksforgeeks+1
What is “windowing” in time series preprocessing?
a. Renaming columns
b. Plotting date features
c. Creating rolling aggregates over time windows
d. Sorting by index
Answer: c. Creating rolling aggregates over time windowsgithub+1
Which date part is retrieved by df[‘datetime’].dt.weekday in pandas?
a. Week number
b. Month
c. Day of week (0=Monday)
d. Year
Answer: c. Day of week (0=Monday)stackoverflow
How do cyclic transformations help in encoding time features like hour and month?
a. They add randomness
b. They reduce memory
c. They encode proximity (e.g., Dec–Jan)
d. They drop duplicates
Answer: c. They encode proximity (e.g., Dec–Jan)kaggle+1
Which two columns are typically created by cyclic encoding for a single time feature?
a. Max and min
b. Sine and cosine
c. Mean and variance
d. Lag and lead
Answer: b. Sine and cosinekaggle+2
What is the purpose of converting a datetime to a “timestamp” (integer) value?
a. To randomize times
b. To enable plotting
c. To obtain a continuous numeric representation
d. To remove time zone
Answer: c. To obtain a continuous numeric representationstackoverflow
Which feature may NOT be useful if the process is not time-dependent?
a. Year
b. Product ID
c. Category
d. Name
Answer: a. Yearstackoverflow
When handling missing date values, what is a suitable imputation technique?
a. Drop rows only
b. Impute with forward fill
c. Encode as 0
d. Convert to category
Answer: b. Impute with forward fillsanfoundry
What is meant by “frequency” in a time series context?
a. Number of features
b. Number of rows
c. The interval between observations
d. Maximum date value
Answer: c. The interval between observationsexamveda
For a dataset with hourly timestamps, what is the cardinality of the hour feature?
a. 7
b. 12
c. 24
d. 31
Answer: c. 24stackoverflow
Which time-based feature is LEAST likely to be cyclic?
a. Day of week
b. Minute
c. Year
d. Month
Answer: c. Yearkaggle
What is the result of extracting only the date part (without time) from a datetime column?
a. Aggregation
b. Feature scaling
c. Truncation of time
d. Data leakage
Answer: c. Truncation of timestackoverflow
In pandas, which accessor is used to extract date features from datetime columns?
a. .as_
b. .str_
c. .dt_
d. .date_
Answer: c. .dt_stackoverflow
What is the main concern when using date differences (e.g., time between events) as features?
a. Units mismatch
b. Value scaling
c. Data sparsity
d. Temporal leakage
Answer: d. Temporal leakagestackoverflow
A dataset records sales every day with a ‘date’ column. To check for missing observations, you should:
a. Sort by product ID
b. Find gaps in days
c. Normalize all columns
d. Remove the date column
Answer: b. Find gaps in daysexamveda
What is the output of df[‘date’].dt.dayofyear?
a. Returns month number
b. Returns day of week
c. Returns day count in year
d. Returns week number
Answer: c. Returns day count in yearstackoverflow
Why might encoding time-of-day as an integer (0–23) fail for learning purposes?
a. Uses too much memory
b. No temporal meaning
c. Disregards the cyclic nature at boundaries
d. Is too slow
Answer: c. Disregards the cyclic nature at boundariesdeveloper.nvidia+1
Why do tree-based models sometimes perform well even with integer-encoded dates?
a. They model monotonic relationships
b. They randomize time
c. They treat all features as cyclical
d. They split data into dates automatically
Answer: a. They model monotonic relationshipsdeveloper.nvidia+1
What is the best strategy to handle leap years in date features?
a. Remove all leap years
b. Assign February 29th to March 1st
c. Account for them in day-of-year calculations
d. Ignore leap years
Answer: c. Account for them in day-of-year calculationsstackoverflow
In cyclic encoding, which formula is used for the ‘sin’ representation of a feature x with max_val possible values?
a. sin( )
b. sin( )
c. sin( )
d. sin( )
Answer: c. sin( )kaggle+2
Which feature would you generally AVOID using directly for forecasting future events?
a. Is holiday?
b. Day of month
c. Full timestamp
d. Date difference
Answer: c. Full timestampstackoverflow
What is the term for regular, repeating patterns in time series data?
a. Residual
b. Seasonality
c. Impact factor
d. Decomposition
Answer: b. Seasonalityexamveda
Which step should always follow after converting date strings to datetime objects in pandas?
a. Drop all date columns
b. Feature extraction
c. Label encoding
d. Downcasting to float
Answer: b. Feature extractionstackoverflow
To create a feature capturing the trend in values over time you might use:
a. Cumulative sum
b. Day name
c. Category count
d. Random sample
Answer: a. Cumulative sumitfeature+1
Which of the following is NOT a typical component in time series analysis?
a. Trend
b. Seasonality
c. Noise
d. Foreign key
Answer: d. Foreign keyexamveda
What is “timestamp” data type useful for in machine learning?
a. Storing images
b. Indexing time series data
c. Fitting categorical models
d. Calculating means
Answer: b. Indexing time series datastackoverflow
Which function retrieves the week number from a datetime column in pandas?
a. .dt.week
b. .get_week()
c. .extract_week()
d. .return_week()
Answer: a. .dt.weekstackoverflow
What is feature “lag” in time series context used for?
a. Random sampling
b. Creating future values
c. Using past values to predict current value
d. Sorting
Answer: c. Using past values to predict current valuegithub+1
Why is cyclic encoding necessary for features like hours or months in deep learning models?
a. Makes data binary
b. Preserves order and proximity
c. Removes nulls
d. Reduces feature count
Answer: b. Preserves order and proximitydeveloper.nvidia+1
Which method is suitable to handle missing timestamps in a time series dataset?
a. Interpolation
b. Dropping all rows
c. Label encoding
d. Replacing with 0
Answer: a. Interpolationsanfoundry
Which of the following is NOT a direct advantage of transforming date-time columns?
a. Better feature interpretation
b. Improved model performance
c. Removal of duplicates
d. Enhanced temporal analysis
Answer: c. Removal of duplicatesdeveloper.nvidia+1
Which is the default time unit for pandas .to_datetime() method when parsing integer timestamps?
a. hour
b. day
c. second
d. millisecond
Answer: c. secondstackoverflow
How does improper handling of date values affect machine learning model accuracy?
a. No effect at all
b. May cause information loss or model bias
c. Guarantees overfitting
d. Increases precision
Answer: b. May cause information loss or model biasdeveloper.nvidia+1
For a retail sales dataset, what feature best captures end-of-month effects?
a. Day of week
b. Month-end flag
c. Product name
d. Store address
Answer: b. Month-end flagstackoverflow
Which type of encoding technique should be used for public holiday features?
a. Sine-cosine encoding
b. Binary encoding (is holiday or not)
c. Mean encoding
d. Hash encoding
Answer: b. Binary encoding (is holiday or not)stackoverflow
What common error occurs if time zones are ignored in datetime processing?
a. Increased cardinality
b. Duplicate data
c. Incorrect calculations or alignment
d. Overfitting
Answer: c. Incorrect calculations or alignmentstackoverflow
For an hourly time series, how should you encode “hour” to preserve cyclic properties?
a. Integer (0–23)
b. One-hot encoding
c. Sine and cosine transformations
d. String encoding
Answer: c. Sine and cosine transformationskaggle+1
Which visualization would help to analyze seasonality in date-based features?
a. Histogram
b. Scatter plot over time
c. Box plot
d. Correlation heatmap
Answer: b. Scatter plot over timeexamveda
When preparing machine learning data, which is the first prerequisite for extracting date features?
a. Ensure all timestamps are unique
b. Convert the column to datetime object
c. Normalize date values
d. Standardize the dataset
Answer: b. Convert the column to datetime objectstackoverflow

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