Quiz: Spam Detection

1. What is the primary goal of spam detection in NLP?

To classify messages as spam or not spam

To translate text between languages

To generate email content

To summarize long documents

2. Which type of model is typically used for spam detection?

Classification model

Generative model

Clustering model

Regression model

3. Why is labeled data important in training spam detection models?

To generate new messages

To identify the sender's location

To reduce inference time

To teach the model the difference between spam and legitimate content

4. Which of the following is often a strong indicator of spam?

Greetings and salutations

Presence of promotional phrases or urgent calls to action

Grammatical correctness

Short messages

5. What technique converts words into numerical form for NLP models?

Word embedding

Bitmasking

Audio fingerprinting

Image encoding

6. Why is a dropout layer useful in spam classification models?

It speeds up email delivery

It helps the model forget common spam keywords

It increases text input length

It prevents overfitting by randomly disabling neurons during training

7. How do NLP-based spam filters handle evolving spam patterns?

They continuously learn from new labeled data

They use static keyword lists

They ignore older data

They manually update their parameters

8. Which kind of data is useful in improving spam detection accuracy aside from the message content?

Font style in the email

Recipient’s phone number

Email metadata like sender info and frequency

Browser history

9. What does a model output in binary spam classification?

Translation outputs

Spam or Not-Spam probabilities

A set of audio features

Sentiment labels

10. Why is it important to avoid misclassifying legitimate messages as spam?

It improves visualization

It increases server load

It improves training loss

It ensures important emails are not lost

Quiz 25: Spam Detection