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CertNexus AIP-210 Exam Syllabus Topics:

Topic
Details

Topic 1

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

Topic 2

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

Topic 3

Recognize relative impact of data quality and size to algorithms
Engineering Features for Machine Learning

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CertNexus Certified Artificial Intelligence Practitioner (CAIP) Sample Questions (Q56-Q61):

NEW QUESTION # 56
An organization sells house security cameras and has asked their data scientists to implement a model to detect human feces, as distinguished from animals, so they can alert th customers only when a human gets close to their house.
Which of the following algorithms is an appropriate option with a correct reason?

A. Logistic regression, because this is a classification problem and our data is linearly separable.
B. A decision tree algorithm, because the problem is a classification problem with a small number of features.
C. k-means, because this is a clustering problem with a small number of features.
D. Neural network model, because this is a classification problem with a large number of features.

Answer: D

Explanation:
Explanation
Neural network models are suitable for classification problems with a large number of features, because they can learn complex and non-linear patterns from high-dimensional data. They can also handle image data, which is likely to be the input for the human face detection problem. Neural networks can also be trained using transfer learning, which can leverage pre-trained models on similar tasks and improve the accuracy and efficiency of the model. References: [Neural network - Wikipedia], [Transfer Learning - Machine Learning's Next Frontier]

NEW QUESTION # 57
Which of the following text vectorization methods is appropriate and correctly defined for an English-to- Spanish translation machine?

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

Answer: B

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 # 58
For each of the last 10 years, your team has been collecting data from a group of subjects, including their age and numerous biomarkers collected from blood samples. You are tasked with creating a prediction model of age using the biomarkers as input. You start by performing a linear regression using all of the data over the
10-year period, with age as the dependent variable and the biomarkers as predictors.
Which assumption of linear regression is being violated?

A. Normality
B. Equality of variance (Homoscedastidty)
C. Linearity
D. Independence

Answer: D

Explanation:
Explanation
Independence is an assumption of linear regression that states that the errors (residuals) of the model are independent of each other, meaning that they are not correlated or influenced by previous or subsequent errors.
Independence can be violated when the data has serial correlation or autocorrelation, which means that the value of a variable at a given time depends on its previous or future values. This can happen when the data is collected over time (time series) or over space (spatial data). In this case, the data is collected over time from a group of subjects, which may introduce serial correlation among the errors.

NEW QUESTION # 59
An organization sells house security cameras and has asked their data scientists to implement a model to detect human feces, as distinguished from animals, so they can alert th customers only when a human gets close to their house.
Which of the following algorithms is an appropriate option with a correct reason?

A. Logistic regression, because this is a classification problem and our data is linearly separable.
B. A decision tree algorithm, because the problem is a classification problem with a small number of features.
C. k-means, because this is a clustering problem with a small number of features.
D. Neural network model, because this is a classification problem with a large number of features.

Answer: D

Explanation:
Neural network models are suitable for classification problems with a large number of features, because they can learn complex and non-linear patterns from high-dimensional data. They can also handle image data, which is likely to be the input for the human face detection problem. Neural networks can also be trained using transfer learning, which can leverage pre-trained models on similar tasks and improve the accuracy and efficiency of the model. References: [Neural network - Wikipedia], [Transfer Learning - Machine Learning's Next Frontier]

NEW QUESTION # 60
The following confusion matrix is produced when a classifier is used to predict labels on a test dataset. How precise is the classifier?

A. (48+37)/100
B. 37/(37+8)
C. 37/(37+7)
D. 48/(48+37)

Answer: B

Explanation:
Precision is a measure of how well a classifier can avoid false positives (incorrectly predicted positive cases).
Precision is calculated by dividing the number of true positives (correctly predicted positive cases) by the number of predicted positive cases (true positives and false positives). In this confusion matrix, the true positives are 37 and the false positives are 8, so the precision is 37/(37+8) = 0.822.

NEW QUESTION # 61
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