Reduce word variants to a common base

Convert text to numeric vectors

All unique tokens in the corpus

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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 241

Cosine similarity is most useful when:

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Question 242

Structured data typically refers to:

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Question 243

K-means clustering groups observations by:

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Question 244

ARCH effects occur when:

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Question 245

The complete collection of documents used for text analysis is called a:

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Question 246

Lowercasing text during preprocessing is mainly done to:

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Question 247

Removing words like “the,” “and,” and “in” is known as:

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Question 248

Tokenization refers to:

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Question 249

Stemming is used to:

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Question 250

Lemmatization differs from stemming because it:

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Question 251

A bag‑of‑words model:

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Question 252

A document‑term matrix typically has:

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Question 253

Term frequency (TF) is:

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Question 254

Document frequency (DF) measures:

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Question 255

Inverse document frequency (IDF) is used to:

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Question 256

TF‑IDF is highest for words that are:

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Question 257

A word cloud is primarily used to:

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Question 258

Removing HTML tags is part of:

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Question 259

Replacing all numbers with a generic token like “/number/” helps:

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Question 260

In sentiment analysis, the most informative tokens are usually:

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Question 261

Mutual information measures:

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Question 262

Tokens with low chi‑square values are:

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Question 263

Document‑term matrices are typically:

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Question 264

Sparse matrices occur in text data because:

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Question 265

Oversampling the minority class helps because it:

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Question 266

Hyperparameter tuning is used to:

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Question 267

A vocabulary is:

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Question 268

Text normalization includes:

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Question 269

A sparse token that appears in only one document is likely to:

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Question 270

The main goal of text preprocessing is to:

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Free CFA Quantitative Methods Practice Questions & Answers

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