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AIP-210 Valid Test Guide, VCE AIP-210 Exam Simulator

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CertNexus AIP-210 Exam Syllabus Topics:

Topic
Details

Topic 1

Design machine and deep learning models
Explain data collection
transformation process in ML workflow

Topic 2

Train, validate, and test data subsets
Training and Tuning ML Systems and Models

Topic 3

Understanding the Artificial Intelligence Problem
Analyze the use cases of ML algorithms to rank them by their success probability

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CertNexus Certified Artificial Intelligence Practitioner (CAIP) Sample Questions (Q40-Q45):

NEW QUESTION # 40
Which of the following is a common negative side effect of not using regularization?

A. Higher compute resources
B. Low test accuracy
C. Overfitting
D. Slow convergence time

Answer: C

Explanation:
Explanation
Overfitting is a common negative side effect of not using regularization. Regularization is a technique that reduces the complexity of a model by adding a penalty term to the loss function, which prevents the model from learning too many parameters that may fit the noise in the training data. Overfitting occurs when the model performs well on the training data but poorly on the test data or new data, because it has memorized the training data and cannot generalize well. References: Regularization (mathematics) - Wikipedia, Overfitting in Machine Learning: What It Is and How to Prevent It

NEW QUESTION # 41
What is Word2vec?

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

Answer: C

Explanation:
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 associations1.

NEW QUESTION # 42
Which of the following text vectorization methods is appropriate and correctly defined for an English-to- Spanish translation machine?

A. Using Word2vec because in translation machines, we do not care about the order of the words.
B. Using TF-IDF because in translation machines, we need to consider the order of the words.
C. Using Word2vec because in translation machines, we need to consider the order of the words.
D. Using TF-IDF because in translation machines, we do not care about the order of the words.

Answer: C

Explanation:
Text vectorization is a technique that converts text into numerical vectors that can be used by machine learning models. Text vectorization can use different methods to represent text features, such as word frequency, word order, word meaning, or word context. Some of the common text vectorization methods are:
* TF-IDF: TF-IDF (term frequency-inverse document frequency) is a method that assigns a weight to each word based on its frequency in a document and its rarity across a collection of documents. TF-IDF can capture the importance and relevance of words for a given topic or domain, but it does not consider the order or meaning of words.
* Word2vec: Word2vec is a method that learns a 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, as well as preserve the order of words.
For an English-to-Spanish translation machine, using Word2vec would be appropriate and correctly defined, because in translation machines, we need to consider the order of the words, as well as their meaning and context.

NEW QUESTION # 43
Which of the following approaches is best if a limited portion of your training data is labeled?

A. Semi-supervised learning
B. Probabilistic clustering
C. Dimensionality reduction
D. Reinforcement learning

Answer: A

Explanation:
Explanation
Semi-supervised learning is an approach that is best if a limited portion of your training data is labeled.
Semi-supervised learning is a type of machine learning that uses both labeled and unlabeled data to train a model. Semi-supervised learning can leverage the large amount of unlabeled data that is easier and cheaper to obtain and use it to improve the model's performance. Semi-supervised learning can use various techniques, such as self-training, co-training, or generative models, to incorporate unlabeled data into the learning process.

NEW QUESTION # 44
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?

A. Emergent
B. Technical
C. Automation
D. Preexisting

Answer: D

Explanation:
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 disability3. 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.

NEW QUESTION # 45
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