MCQs

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Overall MCQs for Reference

Which type of moving average assigns equal weight to all data points in the window?
a. Weighted Moving Average
b. Simple Moving Average
c. Exponential Moving Average
d. Rolling Mean
Answer: b. Simple Moving Averagetestbook+2
What does an exponential moving average prioritize?
a. Oldest data points
b. Random data points
c. Recent data points
d. Middle data points
Answer: c. Recent data pointsarxiv+1
In time series forecasting, which moving average method quickly adapts to recent changes?
a. Weighted Moving Average
b. Simple Moving Average
c. Exponential Moving Average
d. Cumulative Moving Average
Answer: c. Exponential Moving Averagefiveable+1
What is the formula for a simple moving average with n data points?
a.
b.
c.
d.
Answer: a. alooba+2
Which model uses current and previous error terms for prediction?
a. ARIMA model
b. Moving Average model
c. Linear Regression
d. Logistic Regression
Answer: b. Moving Average modelwikipedia+1
In feature engineering, why are moving averages used?
a. To increase data variance
b. To overfit the model
c. To smooth data and reduce noise
d. To remove useful trends
Answer: c. To smooth data and reduce noisealooba+1
When would a weighted moving average be preferred over a simple moving average?
a. When all data points are equally important
b. When recent observations are more important
c. When oldest observations are more important
d. When none of the observations are important
Answer: b. When recent observations are more importanttestbook+1
Which moving average technique results in the highest lag?
a. Simple Moving Average
b. Weighted Moving Average
c. Exponential Moving Average
d. Gaussian Moving Average
Answer: a. Simple Moving Averageprojectpro+1
What is a major drawback of the simple moving average?
a. It is sensitive to outliers
b. It does not respond quickly to new trends
c. It is difficult to calculate
d. It can only be used for stationary data
Answer: b. It does not respond quickly to new trendsaionlinecourse+1
The moving average as used in machine learning is an example of:
a. Classification technique
b. Smoothing technique
c. Feature selection technique
d. Dimensionality reduction technique
Answer: b. Smoothing techniquearxiv+1
What does an MA(q) model represent in time series analysis?
a. Autoregressive model of order q
b. Moving Average model of order q
c. Linear Regression model with q variables
d. Exponential model with q components
Answer: b. Moving Average model of order qgeeksforgeeks+1
Which is most appropriate for detecting trends in noisy data?
a. Logistic Regression
b. Simple Moving Average
c. Random Forest
d. K-means Clustering
Answer: b. Simple Moving Averagegeeksforgeeks+1
What is the main purpose of using a moving average in predictive analytics?
a. Increasing data volatility
b. Smoothing out short-term fluctuations
c. Generating random numbers
d. Reducing model complexity
Answer: b. Smoothing out short-term fluctuationsfiveable+2
In a 3-point moving average, what is calculated?
a. Sum of three most recent data points
b. Average of three most recent data points
c. Maximum of three data points
d. Minimum of three data points
Answer: b. Average of three most recent data pointstestbook+1
Moving average is most commonly applied in which data structure?
a. Graphs
b. Trees
c. Time Series
d. Matrices
Answer: c. Time Serieswikipedia+2
Which moving average method smoothens data using freely assigned weights to all data points?
a. Simple Moving Average
b. Weighted Moving Average
c. Exponential Moving Average
d. Cumulative Moving Average
Answer: b. Weighted Moving Averagetestbook+1
In the calculation of exponential moving average, what does alpha ( ) represent?
a. Smoothing parameter
b. Lag parameter
c. Trend indicator
d. Mean value
Answer: a. Smoothing parameterarxiv+1
The moving average method can help identify:
a. Outliers
b. Hidden patterns or trends
c. Clusters
d. Data types
Answer: b. Hidden patterns or trendsalooba+2
For window size n in moving average, what happens as n increases?
a. Smoothing effect increases
b. Model becomes more volatile
c. Data becomes noisier
d. Trend detection becomes faster
Answer: a. Smoothing effect increasesprojectpro+1
Which statement about moving averages is true?
a. They amplify data noise
b. They are unsuitable for forecasting
c. They reduce short-term volatility
d. They eliminate long-term cycles
Answer: c. They reduce short-term volatilityfiveable+2
What is the principal limitation of moving averages in non-stationary time series?
a. They can introduce bias
b. They tend to overfit
c. They lack interpretability
d. They require more storage
Answer: a. They can introduce biaswikipedia+1
The difference between SMA and EMA is:
a. EMA assigns equal weight to all data points
b. SMA assigns higher weight to older data
c. EMA reacts faster to recent changes
d. SMA is not used in time series
Answer: c. EMA reacts faster to recent changesarxiv+1
What is the effect of using a very small window in moving average?
a. Model ignores data trends
b. Model is highly sensitive to noise
c. Model is highly smoothed
d. Model remains unchanged
Answer: b. Model is highly sensitive to noiseprojectpro+1
Which is the correct description for an MA(2) process?
a. Autoregressive of order 2
b. Moving Average of order 2
c. Logistic Regression model
d. Markov process of order 2
Answer: b. Moving Average of order 2geeksforgeeks+1
When using moving average as a feature in machine learning, what is commonly achieved?
a. Feature normalization
b. Dimensionality reduction
c. Smoothing time-dependent features
d. Increasing computational cost
Answer: c. Smoothing time-dependent featuresalooba+1
If a time series is trending upwards, what do moving averages typically show?
a. Downward spikes
b. Gradual increase following data trend
c. Random oscillations
d. Immediate jumps
Answer: b. Gradual increase following data trendfiveable+2
Why is weighted moving average more flexible than simple moving average?
a. Assigns equal weights to all data
b. Can assign custom importance to certain data points
c. Ignores most recent data
d. Is not applicable for noisy data
Answer: b. Can assign custom importance to certain data pointstestbook+1
How do you select window size for moving average in practice?
a. Arbitrarily
b. Based on seasonal patterns or business cycles
c. By maximizing model complexity
d. By minimizing feature count
Answer: b. Based on seasonal patterns or business cyclesprojectpro+1
Moving averages in machine learning are least suited for:
a. Trend analysis
b. Classification of categorical data
c. Feature engineering in time series
d. Smoothing numeric data
Answer: b. Classification of categorical dataalooba+1
What is a rolling mean another name for?
a. Exponential Moving Average
b. Simple Moving Average
c. Weighted Moving Average
d. ARIMA mean
Answer: b. Simple Moving Averagequantra.quantinsti+1
Feature created using moving average is especially useful in which machine learning task?
a. Image Recognition
b. Time Series Forecasting
c. Natural Language Processing
d. Object Detection
Answer: b. Time Series Forecastinggeeksforgeeks+1
In which forecasting scenario are moving averages least effective?
a. Highly volatile series
b. Seasonal series
c. Stationary series
d. Cyclical series
Answer: a. Highly volatile serieswikipedia+2
In MA models, the white noise errors are assumed to be:
a. Dependent and non-Gaussian
b. Independent and identically distributed
c. Autoregressive
d. Non-stationary
Answer: b. Independent and identically distributedwikipedia+1
Which moving average model is a component of ARIMA?
a. Exponential Moving Average
b. MA (Moving Average) model
c. Weighted Moving Average
d. Rolling Window Model
Answer: b. MA (Moving Average) modelgeeksforgeeks+1
Why do financial analysts prefer exponential moving averages?
a. Historical data is ignored
b. EMA is less computationally expensive
c. EMA responds faster to market changes
d. EMA removes all outliers
Answer: c. EMA responds faster to market changesluxalgo+2
The formula for updating an exponential moving average is:
a.
b.
c.
d.
Answer: a. testbook+2
Which application benefits from the noise-reduction property of moving averages?
a. Stock market prediction
b. Data encryption
c. Text classification
d. Image segmentation
Answer: a. Stock market predictionluxalgo+1
In the case of missing data values, moving average can be used to:
a. Ignore the dataset
b. Impute missing values by averaging available data
c. Remove entire column
d. Convert data to categorical
Answer: b. Impute missing values by averaging available dataquantra.quantinsti+1
Which time series component does moving average best capture?
a. Irregular component
b. Trend component
c. Seasonal component only
d. Noise component only
Answer: b. Trend componentfiveable+1
Which moving average is most sensitive to recent data jumps?
a. Simple Moving Average
b. Weighted Moving Average
c. Exponential Moving Average
d. Rolling Mean
Answer: c. Exponential Moving Averagetestbook+2
In machine learning, what does smoothing via moving average help mitigate?
a. High variance and random noise
b. Underfitting
c. Irregular sampling intervals
d. Loss of all information
Answer: a. High variance and random noisearxiv+2
What is usually the effect of increasing smoothing parameter alpha in EMA?
a. Makes EMA less responsive to new data
b. Makes EMA more responsive to new data
c. Removes all noise
d. Ignores old data completely
Answer: b. Makes EMA more responsive to new dataarxiv+1
Which of the following is NOT a use case of moving average in machine learning?
a. Time series forecasting
b. Reducing overfitting in neural networks
c. Smoothing sensor readings
d. Feature engineering
Answer: b. Reducing overfitting in neural networksalooba+1
A moving average filter in signal processing is used to:
a. Amplify high-frequency components
b. Remove low-frequency components
c. Reduce noise and smooth signals
d. Randomize signals
Answer: c. Reduce noise and smooth signalsalooba+1
In weighted moving averages, weights must sum to:
a. 0
b. 1
c. n
d. Infinity
Answer: b. 1testbook+1
Which is a limitation of all moving average methods?
a. Can remove underlying trend completely
b. May lag during rapid trend changes
c. Always fits perfectly
d. Requires labeled data
Answer: b. May lag during rapid trend changesaionlinecourse+2
In ARIMA models, moving average is combined with:
a. Neural networks
b. Autoregressive models
c. K-means clustering
d. Polynomial regression
Answer: b. Autoregressive modelswikipedia+1
Which moving average type is best for data with abrupt changes?
a. Simple Moving Average
b. Weighted Moving Average
c. Exponential Moving Average
d. Rolling Mean
Answer: c. Exponential Moving Averagefiveable+1
The use of moving averages in feature engineering helps:
a. Model overfitting
b. Data leakage
c. Extraction of time-dependent trends
d. Reduction in model performance
Answer: c. Extraction of time-dependent trendsarxiv+1
When evaluating a time series forecast, what is the primary effect of applying a moving average?
a. Increasing forecast volatility
b. Removing seasonality
c. Smoothing out random fluctuations
d. Introducing bias
Answer: c. Smoothing out random fluctuationsprojectpro+2

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