CompTIA DataX DY0-001 (V1) Practice Question

With smooth_idf=True, the inverse document frequency is

idf(t) = ln[(N + 1)/(df + 1)] + 1.

Computations for each token (N = 10,000):

1. kernel: idf = ln[(10,001)/(30 + 1)] + 1 ≈ ln(322.6) + 1 ≈ 5.776 + 1 = 6.776. tf-idf = 8 × 6.776 ≈ 54.2.
2. segmentation: idf = ln[(10,001)/(120 + 1)] + 1 ≈ ln(82.65) + 1 ≈ 4.412 + 1 = 5.412. tf-idf = 4 × 5.412 ≈ 21.6.
3. error: idf = ln[(10,001)/(9,000 + 1)] + 1 ≈ ln(1.111) + 1 ≈ 0.105 + 1 = 1.105. tf-idf = 15 × 1.105 ≈ 16.6.

Because 54.2 > 21.6 > 16.6, the token "kernel" has the greatest TF-IDF weight.

The result illustrates how a term that is both relatively frequent within the document and comparatively rare across the corpus attains the highest TF-IDF score, while very common words ("error") are down-weighted despite high within-document frequency.

The calculation can be completed with the information provided. The default term frequency (tf) in scikit-learn is the raw count of the term in the document, not a frequency normalized by document length. Therefore, knowing the total number of terms in the ticket is not necessary.