AML AML Natural Language Processing & Computer Vision 1

Question 1

Which NLP technique converts words into dense vector representations that capture semantic relationships?

Accepted Answer

Word2Vec / Word Embeddings

Answer

Word embeddings like Word2Vec map words to dense vectors where semantically similar words have high cosine similarity.

Question 2

What does TF-IDF stand for in text feature extraction?

Accepted Answer

Term Frequency–Inverse Document Frequency

Answer

TF-IDF weighs each word by how often it appears in a document (TF) penalized by how common it is across all documents (IDF).

Question 3

In computer vision, what is the role of a convolutional layer in a CNN?

Accepted Answer

Apply learnable filters to detect local spatial features

Answer

Convolutional layers slide learnable filters across the input image to detect local patterns like edges, textures, and shapes.

Question 4

Which NLP task involves assigning a label (positive, negative, neutral) to a piece of text based on its emotional tone?

Accepted Answer

Sentiment Analysis

Answer

Sentiment analysis classifies text according to the opinion or emotion expressed, commonly as positive, negative, or neutral.

Question 5

What is the purpose of max pooling in a convolutional neural network?

Accepted Answer

Reduce spatial dimensions while retaining dominant features

Answer

Max pooling downsamples feature maps by taking the maximum value in each pooling window, reducing size while preserving strong activations.

(AML) Advanced Machine Learning Certification Practice Test