Free CFA Quantitative Methods Practice Questions & Answers
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Question 481
A sparse token that appears in only one document is likely to:
Select an option first.
Correct answer: A — Increase overfitting risk
Explanation: Why A is right: Rare tokens often capture noise and cause memorization. Why others are wrong: B/C/D: Not typical outcomes.
Question 482
The main goal of text preprocessing is to:
Select an option first.
Correct answer: A — Convert raw text into structured, usable features
Explanation: Why A is right: Preprocessing prepares text for modeling by structuring it. Why others are wrong: B: Not the goal. C/D: Not always appropriate.
Question 483
A histogram is most useful for:
Select an option first.
Correct answer: A — Showing the distribution of a single numeric variable
Explanation: Why A is right: Histograms visualize how a numeric variable is distributed. Why others are wrong: B: Use scatterplots. C: Use bar charts. D: Not a histogram use.
Question 484
A box plot is best used to identify:
Select an option first.
Correct answer: A — Outliers
Explanation: Why A is right: Box plots highlight quartiles and outliers clearly. Why others are wrong: B: Bar charts are better. C: Use correlation matrices. D: Not the purpose.
Question 485
A scatterplot is most appropriate for:
Select an option first.
Correct answer: A — Examining relationships between two numeric variables
Explanation: Why A is right: Scatterplots reveal relationships, clusters, and trends between two numeric variables. Why others are wrong: B: Use bar charts. C: Use frequency plots. D: Not the primary use.
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Correct answer: A — Convert categorical variables into binary columns
Explanation: Why A is right: One‑hot encoding converts categories into 0/1 indicator columns. Why others are wrong: B/C/D: Not related.
Question 487
Dimensionality reduction techniques like PCA are used to:
Select an option first.
Correct answer: A — Combine features into fewer components
Explanation: Why A is right: PCA compresses information into fewer dimensions. Why others are wrong: B/C/D: Not PCA’s purpose.
Question 488
A key benefit of dimensionality reduction is:
Select an option first.
Correct answer: A — Lower model complexity
Explanation: Why A is right: Fewer features → simpler, faster models. Why others are wrong: B/C/D: Not benefits.
Question 489
A density plot is used to:
Select an option first.
Correct answer: A — Smoothly visualize a variable’s distribution
Explanation: Why A is right: Density plots show smoothed distributions. Why others are wrong: B: Use bar charts. C/D: Not density plots.
Question 490
A bar chart is most appropriate for:
Select an option first.
Correct answer: A — Categorical variables
Explanation: Why A is right: Bar charts show category frequencies. Why others are wrong: B: Use histograms. C/D: Not bar charts.
Question 491
A multi‑box plot is useful for:
Select an option first.
Correct answer: A — Comparing distributions across groups
Explanation: Why A is right: Multi‑box plots compare distributions across categories. Why others are wrong: B/C/D: Not the purpose.
Question 492
A line chart is most appropriate for:
Select an option first.
Correct answer: A — Time‑series data
Explanation: Why A is right: Line charts show trends over time. Why others are wrong: B/C/D: Not ideal.
Question 493
Normalization rescales numeric features to:
Select an option first.
Correct answer: A — A common range
Explanation: Why A is right: Normalization puts features on a comparable scale. Why others are wrong: B: Opposite. C/D: Not normalization.
Question 494
Winsorization is used to:
Select an option first.
Correct answer: A — Limit extreme values by capping them
Explanation: Why A is right: Winsorization caps extreme values to reduce their influence. Why others are wrong: B: It doesn’t remove them; it caps them. C/D: Not related.
Question 495
A missing‑value heat map helps identify:
Select an option first.
Correct answer: A — Patterns in missingness
Explanation: Why A is right: Heat maps visually show where missing values cluster. Why others are wrong: B/C/D: Not the purpose.
Question 496
Imputing missing values with the mean is appropriate when the variable is:
Select an option first.
Correct answer: A — Numeric
Explanation: Why A is right: Mean imputation applies only to numeric variables. Why others are wrong: B: Use mode. C: Not meaningful. D: Use mode or separate category.
Question 497
Imputing missing values with the mode is appropriate for:
Select an option first.
Correct answer: A — Categorical variables
Explanation: Why A is right: Mode imputation is ideal for categories. Why others are wrong: B: Mean/median is better. C/D: Not appropriate.
Question 498
A scatterplot matrix is used to:
Select an option first.
Correct answer: A — Visualize pairwise relationships among several numeric variables
Explanation: Why A is right: Scatterplot matrices show relationships across many numeric variables. Why others are wrong: B/C/D: Not the purpose.
Question 499
Feature scaling is important because:
Select an option first.
Correct answer: A — Many models are sensitive to feature magnitude
Explanation: Why A is right: Models like KNN, SVM, and gradient descent rely on scale. Why others are wrong: B/C/D: Not true.
Question 500
Outliers can distort:
Select an option first.
Correct answer: A — Mean and standard deviation
Explanation: Why A is right: Outliers heavily influence mean and variance. Why others are wrong: B/C/D: Not affected.
Question 501
A log transformation is useful when:
Select an option first.
Correct answer: A — Data is highly skewed
Explanation: Why A is right: Log transforms reduce right‑skew. Why others are wrong: B: Not applicable. C: Doesn’t fix missingness. D: Normalization ≠ transformation.
Question 502
A derived feature such as “income_above_100k” is an example of:
Select an option first.
Correct answer: A — Binning
Explanation: Why A is right: Binning converts continuous values into categories. Why others are wrong: B/C/D: Not applicable.
Question 503
A pairplot is useful for:
Select an option first.
Correct answer: A — Visualizing multiple variable relationships
Explanation: Why A is right: Pairplots show scatterplots for all variable pairs. Why others are wrong: B/C/D: Not the purpose.
Question 504
A categorical variable with many unique values may cause:
Select an option first.
Correct answer: A — High dimensionality
Explanation: Why A is right: One‑hot encoding creates many columns. Why others are wrong: B/C/D: Not true.
Question 505
A feature strongly correlated with another feature is:
Select an option first.
Correct answer: A — Potentially redundant
Explanation: Why A is right: Redundant features can cause instability in models. Why others are wrong: B: Not always harmful. C/D: Correlation doesn’t imply type.
Question 506
A time‑based feature such as “day_of_week” is an example of:
Select an option first.
Correct answer: A — Feature engineering
Explanation: Why A is right: Extracting time components is a classic engineered feature. Why others are wrong: B/C/D: Not applicable.
Question 507
Removing features with extremely high cardinality helps reduce:
Select an option first.
Correct answer: A — Overfitting
Explanation: Why A is right: High‑cardinality categorical features (e.g., thousands of categories) can create huge sparse matrices that overfit. Why others are wrong: B: Tokenization is unrelated. C: Cardinality doesn’t affect missingness. D: Normalization applies to numeric features.
Question 508
A violin plot is used to show:
Select an option first.
Correct answer: A — Distribution and density of a variable
Explanation: Why A is right: Violin plots combine box plots with density curves. Why others are wrong: B: Not shown. C: Use missingness heat maps. D: Use correlation matrices.
Question 509
A derived feature such as “price_per_unit” is created by:
Select an option first.
Correct answer: A — Combining existing features
Explanation: Why A is right: Feature engineering often combines variables to create more meaningful ones. Why others are wrong: B/C/D: Not how derived features are created.
Question 510
A scatterplot with a fitted line helps identify:
Select an option first.
Correct answer: A — Linear relationships
Explanation: Why A is right: The fitted line shows whether a linear trend exists. Why others are wrong: B: Use missingness maps. C: Use bar charts. D: Use accuracy metrics.
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