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Free CFA Quantitative Methods Practice Questions & Answers

1,200 exam-style Quantitative Methods questions. Pick your answer, hit Check answer, and see the worked solution — free to start, no signup.

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Question 511

A feature with a highly skewed distribution may benefit from:

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Question 512

A feature with many zero values is considered:

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Question 513

A feature with extremely high variance may:

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Question 514

A feature that is strongly correlated with many other features may:

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Question 515

A feature with many identical values and few unique values is:

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Question 516

A feature with many unique categories (e.g., thousands) is:

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Question 517

A feature that is highly skewed and contains outliers may benefit from:

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Question 518

A feature that is highly correlated with time (e.g., trending upward) may require:

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Question 519

A feature that is measured on a very different scale from others may require:

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Question 520

A feature that shows a strong nonlinear relationship with the target may benefit from:

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Question 521

A feature with many identical values and only a few distinct values is:

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Question 522

A feature that is highly correlated with the target but also highly correlated with time may require:

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Question 523

A feature that contains extreme outliers may be handled by:

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Question 524

A feature that is strongly correlated with the target but also perfectly correlated with another feature is:

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Question 525

A feature that varies widely in scale compared to others may distort:

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Question 526

A feature that is highly skewed and contains many zeros may benefit from:

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Question 527

A feature that is strongly correlated with the target but also strongly correlated with noise variables may:

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Question 528

A feature that is extremely sparse (mostly zeros) may be:

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Question 529

K‑means clustering requires the user to specify:

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Question 530

A key limitation of K‑means is that it:

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Question 531

The K‑means algorithm updates clusters by:

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Question 532

A centroid in K‑means represents:

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Question 533

Hierarchical clustering differs from K‑means because it:

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Question 534

Agglomerative clustering starts with:

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Question 535

Divisive clustering starts with:

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Question 536

A dendrogram is used to:

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Question 537

In a dendrogram, the height at which two branches merge represents:

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Question 538

A shorter branch height in a dendrogram indicates:

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Question 539

A distance matrix in clustering contains:

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Question 540

Euclidean distance is most appropriate when:

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Free CFA Quantitative Methods Practice Questions & Answers

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