QUESTION: 41

What is Word2vec?

A bag of words.
A matrix of how frequently words appear in a group of documents.
A word embedding method that builds a one-hot encoded matrix from samples and the terms that appear in them.
A word embedding method that finds characteristics of words in a very large number of documents.

Answer(s): D

Explanation:

Word2vec is a word embedding method that finds characteristics of words in a very large number of documents. Word embedding is a technique that converts words into numerical vectors that represent their meaning, usage, or context. Word2vec learns a dense and continuous vector representation for each word based on its context in a large corpus of text. Word2vec can capture the semantic and syntactic similarity and relationships among words, such as synonyms, antonyms, analogies, or associations.

Reveal Solution Next Question

QUESTION: 42

We are using the k-nearest neighbors algorithm to classify the new data points. The features are on different scales.
Which method can help us to solve this problem?

Log transformation
Normalization
Square-root transformation
Standardization

Answer(s): B

Explanation:

Normalization is a method that can help us to solve the problem of features being on different scales when using the k-nearest neighbors algorithm. Normalization is a technique that rescales the values of features to a common range, such as [0, 1] or [-1, 1]. Normalization can help reduce the influence or dominance of some features over others, as well as improve the accuracy and performance of the algorithm.

Reveal Solution Next Question

QUESTION: 43

An HR solutions firm is developing software for staffing agencies that uses machine learning. The team uses training data to teach the algorithm and discovers that it generates lower employability scores for women. Also, it predicts that women, especially with children, are less likely to get a high-paying job.
Which type of bias has been discovered?

Automation
Emergent
Preexisting
Technical

Answer(s): C

Explanation:

Preexisting bias is a type of bias that originates from historical or social contexts, such as stereotypes, prejudices, or discriminations. Preexisting bias can affect the data or the algorithm used for machine learning, as well as the outcomes or decisions made by machine learning. Preexisting bias can cause unfair or harmful impacts on certain groups or individuals based on their attributes, such as gender, race, age, or disability. In this case, the software that uses machine learning generates lower employability scores for women and predicts that women, especially with children, are less likely to get a high-paying job. This indicates that the software has preexisting bias against women, which may reflect the historical or social inequalities or expectations in the labor market.

Reveal Solution Next Question

QUESTION: 44

Which two encodes can be used to transform categories data into numerical features? (Select two.)

Count Encoder
Log Encoder
Mean Encoder
Median Encoder
One-Hot Encoder

Answer(s): C,E

Explanation:

Encoding is a technique that transforms categorical data into numerical features that can be used by machine learning models. Categorical data are data that have a finite number of possible values or categories, such as gender, color, or country. Encoding can help convert categorical data into a format that is suitable and understandable for machine learning models. Some of the encoding methods that can be used to transform categorical data into numerical features are:
Mean Encoder: Mean encoder is a method that replaces each category with the mean value of the target variable for that category. Mean encoder can capture the relationship between the category and the target variable, but it may cause overfitting or multicollinearity problems. One-Hot Encoder: One-hot encoder is a method that creates a binary vector for each category, where only one element has a value of 1 (the hot bit) and the rest have a value of 0. One-hot encoder can create distinct and orthogonal vectors for each category, but it may increase the dimensionality and sparsity of the data.

Reveal Solution Next Question

Free AIP-210 Exam Braindumps (page: 11)

QUESTION: 41

Explanation:

QUESTION: 42

Explanation:

QUESTION: 43

Explanation:

QUESTION: 44

Explanation: