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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	Identify potential ethical concerns Analyze machine learning system use cases
Topic 3	Train, validate, and test data subsets Training and Tuning ML Systems and Models
Topic 4	Recognize relative impact of data quality and size to algorithms Engineering Features for Machine Learning
Topic 5	Address business risks, ethical concerns, and related concepts in training and tuning Work with textual, numerical, audio, or video data formats
Topic 6	Transform numerical and categorical data Address business risks, ethical concerns, and related concepts in operationalizing the model

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CertNexus Certified Artificial Intelligence Practitioner (CAIP) Sample Questions (Q21-Q26):

NEW QUESTION # 21
Which of the following scenarios is an example of entanglement in ML pipelines?

A. Change the way output is visualized in the monitoring step.
B. Add a new pipeline for retraining the model in the model training step.
C. Add a new method for drift detection in the model evaluation step.
D. Change in normalization function in the feature engineering step.

Answer: D

Explanation:
Explanation
Entanglement in ML pipelines occurs when a change in one step affects other steps that depend on it.
Changing the normalization function in the feature engineering step would affect the model training and evaluation steps, as they rely on the features generated by the feature engineering step. Therefore, this scenario is an example of entanglement in ML pipelines. The other scenarios are not examples of entanglement, as they do not affect other steps in the pipeline.

NEW QUESTION # 22
You have a dataset with many features that you are using to classify a dependent variable. Because the sample size is small, you are worried about overfitting. Which algorithm is ideal to prevent overfitting?

A. Logistic regression
B. Decision tree
C. XGBoost
D. Random forest

Answer: D

NEW QUESTION # 23
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?

A. Square-root transformation
B. Standardization
C. Log transformation
D. Normalization

Answer: D

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 algorithm2.

NEW QUESTION # 24
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 do not care about 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 need to consider the order of the words.

Answer: C

Explanation:
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 # 25
Which of the following is TRUE about SVM models?

A. They use the sigmoid function to classify the data points.
B. They can be used only for regression.
C. They can be used only for classification.
D. They can take the feature space into higher dimensions to solve the problem.

Answer: D

Explanation:
SVM models can use kernel functions to map the input data into higher-dimensional feature spaces, where linear separation is possible. This allows SVM models to handle non-linear problems effectively.
References: CertNexus Certified Artificial Intelligence Practitioner, Support vector machine - Wikipedia

NEW QUESTION # 26
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