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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 571
A silhouette score close to zero indicates:
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Question 572
A silhouette score close to –1 indicates:
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Question 573
The elbow method helps identify:
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Question 574
A cluster with very high internal variance suggests:
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Question 575
A cluster with very low internal variance suggests:
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Question 576
K‑means++ improves K‑means by:
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Question 577
A cluster that contains only one point in K‑means likely indicates:
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Question 578
In hierarchical clustering, the choice of linkage method affects:
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Question 579
Complete linkage tends to produce clusters that are:
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Question 580
Single linkage tends to produce clusters that are:
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Question 581
Average linkage tends to produce clusters that are:
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Question 582
Clusters that are very close together but internally compact may indicate:
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Question 583
Clusters that are extremely compact but very close to each other may indicate:
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Question 584
Clusters that are very spread out but far apart may indicate:
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Question 585
A cluster that contains points with very high internal variance likely:
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Question 586
A cluster that contains points with extremely low internal variance likely:
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Question 587
A distance metric that works well for high‑dimensional sparse vectors is:
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Question 588
Clusters that change dramatically when features are rescaled indicate:
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Question 589
A cluster that contains points from multiple natural groups likely indicates:
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Question 590
A cluster that contains only one or two points in K‑means often indicates:
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Question 591
A cluster that contains points with very different scales across features suggests:
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Question 592
A cluster that is extremely elongated may be caused by:
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Question 593
A cluster that is extremely compact may be caused by:
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Question 594
A cluster that appears in a dendrogram but disappears when cutting at a certain height indicates:
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Question 595
A cluster that contains points with very high pairwise distances suggests:
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Question 596
A cluster that contains points with very low pairwise distances suggests:
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Question 597
A cluster that is extremely sensitive to small changes in initialization suggests:
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Question 598
A cluster that contains points with very different densities suggests:
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Question 599
A cluster that contains points with very different shapes suggests:
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Question 600
A cluster that contains points with very different orientations (e.g., diagonal vs horizontal) suggests:
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