MCQ Exercises

Numpy

Q1. Which of the following correctly creates a 2×3 NumPy array?

np.array([1, 2, 3, 4, 5, 6], shape=(2,3)) np.array([[1, 2, 3], [4, 5, 6]]) np.array((1, 2, 3), (4, 5, 6)) np.array([[1, 2], [3, 4], [5, 6]])

Q2. What does np.linspace(0, 10, 5) produce?

An array of integers from 0 to 10 An array of 5 evenly spaced values from 0 to 10 An array of values from 0 to 10 with step size 5 An array of 10 evenly spaced values from 0 to 5

Q3. If x = np.array([[1,2,3],[4,5,6]]), what is x.shape?

(3, 2) (2, 3) (6,) (1, 6)

Q4. Given a = np.array([1,2,3,4]), what does a.mean() return?

2 2.5 [2.5] It raises an error

Q5. Which NumPy function computes the square root of every element in an array?

np.sqrt() np.square() np.exp() np.root()

Q6. What does x.reshape(6,) do to a 2×3 array?

Converts it into a 3×2 array Flattens it into a 1D array of length 6 Creates a copy with reversed rows Raises an error

Q7. Which statement about flatten() and ravel() is correct?

Both return views of the original array Both return copies of the original array flatten() returns a copy; ravel() returns a view flatten() returns a view; ravel() returns a copy

Q8. Given x = np.array([[10,20,30],[40,50,60]]), what does x[:,1] return?

[10, 40] [20, 50] [30, 60] [[20], [50]]

Pandas

Q1. Which statement correctly describes a Pandas Series?

A Series must contain only numeric values A Series represents a single column of data A Series is always two‑dimensional A Series can only be created from a dictionary

Q2. Which command displays the last 5 rows of a DataFrame df?

df.head(10) df.show(5) df.tail(5) df.last(5)

Q3. Which function removes all rows containing at least one NA value?

df.remove_na() df.dropna() df.delete_na() df.clean()

Q4. Which command correctly extracts the columns ‘Age’ and ‘Gender’ as a DataFrame?

df(Age, Gender) df.select(["Age","Gender"]) df[["Age","Gender"]] df["Age","Gender"]

Q5. Which command correctly filters rows where age is over 40 and has not experienced heart attack?

df[df.Age > 40 & df.Heart_attack == "No"] df[(Age > 40) and (Heart_attack == "No")] df[(df["Age"] > 40) & (df["Heart_attack"] == "No")] df.filter(Age > 40 & Heart_attack == "No")

Q6. Which command correctly creates a new column storing age 5 years later?

df.age_5_years_later = Age + 5 df["age_5_years_later"] == df["Age"] + 5 df["age_5_years_later"] = df["Age"] + 5 df.new(age_5_years_later = Age + 5)

Q7. Which command correctly assigns ‘Male’ when Gender==1 and ‘Female’ otherwise?

df["gender_text"] = df["Gender"].ifelse("Male","Female") df["gender_text"] = df["Gender"].where(df["Gender"]==1,"Male","Female") df["gender_text"] = np.where(df["Gender"]==1, "Male", "Female") df["gender_text"] = Gender == 1 ? "Male" : "Female"

Q8. Which command computes mean BMI grouped by Heart_attack status?

df.groupby("BMI")["Heart_attack"].mean() df.group("Heart_attack").BMI.mean() df.groupby("Heart_attack")[["BMI"]].mean() df["BMI"].groupby("Heart_attack").summary()

Scikitlearn

Q1. From which module do you import LinearRegression?

sklearn.preprocessing sklearn.linear_model sklearn.metrics sklearn.model_selection

Q2. Which statement about preparing the outcome for logistic regression is correct?

It accepts text labels directly with no changes Binary outcomes must be encoded as 0/1 values It requires outcomes to be integers from 1 to 10 It automatically converts strings to numeric codes

Q3. What is the purpose of train_test_split()?

It trains the model on the entire dataset It always uses a 50:50 split It separates data into training and testing sets for model evaluation It only works for classification models

Q4. Which statement correctly compares LASSO and Ridge regression?

Both methods eliminate predictors completely LASSO can shrink some coefficients to zero, while Ridge generally does not Ridge always performs better than LASSO LASSO and Ridge produce identical models

Q5. Which statement correctly describes how KNN makes predictions?

KNN estimates coefficients like linear regression KNN requires predictors to be categorical KNN predicts based on the labels of the k closest training samples KNN cannot be used for classification

Matplotlib

Q1. Which statement correctly describes the relationship between a Figure and an Axes in Matplotlib?

An Axes is the entire window that contains multiple plots A Figure is the overall container; an Axes is the area where data is plotted A Figure and an Axes are the same object Axes objects can exist without being inside a Figure

Q2. Which command correctly creates a 2×2 grid of subplots?

plt.figure(2,2) creates a 2×2 grid of subplots plt.plot() automatically creates a 2×2 layout plt.subplots(2, 2) creates a Figure with a 2×2 grid of Axes You must manually add each Axes using add_axes()

Q3. Which command correctly creates a scatterplot on an Axes object?

ax.line(x, y) plt.scatter(ax, x, y) ax.scatter(x, y) fig.scatter(x, y)

Q4. Which command correctly creates a histogram of Age with 10 bins and visible bin edges?

ax.hist(df["Age"], bins="auto", color="none") ax.hist(df["Age"], bins=10, edgecolor="black") ax.hist(df["Age"], breaks=10) ax.histogram(df["Age"], bins=10)

Q5. Which statement correctly describes the components of a Matplotlib boxplot?

The box shows the mean and standard deviation The box spans Q1 to Q3, with a line at the median The whiskers always extend to the minimum and maximum values Boxplots cannot display means

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