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Questions and Answers
Data Engineer is responsible for designing and managing the database infrastructure.
Data Engineer is responsible for designing and managing the database infrastructure.
False
Data Scientist is responsible for developing statistical and machine learning models.
Data Scientist is responsible for developing statistical and machine learning models.
True
One of the responsibilities of a DBA is to acquire and integrate data from various sources.
One of the responsibilities of a DBA is to acquire and integrate data from various sources.
False
Data Engineer is responsible for developing ETL processes.
Data Engineer is responsible for developing ETL processes.
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Data Scientist is responsible for ensuring data security in the database infrastructure.
Data Scientist is responsible for ensuring data security in the database infrastructure.
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Collaborating with other teams to ensure the success of the analytics project is a common responsibility shared by all three roles: DBA, Data Engineer, and Data Scientist.
Collaborating with other teams to ensure the success of the analytics project is a common responsibility shared by all three roles: DBA, Data Engineer, and Data Scientist.
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In Unsupervised Type of machine learning, there is a label column used to classify or predict.
In Unsupervised Type of machine learning, there is a label column used to classify or predict.
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Supervised machine learning algorithms are mainly used for discovering patterns in data.
Supervised machine learning algorithms are mainly used for discovering patterns in data.
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Binary Classification is a method used under Unsupervised Type of machine learning.
Binary Classification is a method used under Unsupervised Type of machine learning.
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NCI in machine learning stands for Numerical and Categorical Input.
NCI in machine learning stands for Numerical and Categorical Input.
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Rule Base in decision trees creates rules for classifying data.
Rule Base in decision trees creates rules for classifying data.
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Unsupervised Type of machine learning focuses on predicting outcomes based on labeled data.
Unsupervised Type of machine learning focuses on predicting outcomes based on labeled data.
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Descriptive analytics focuses on characterizing data by analyzing the central tension, dispersion, and shape.
Descriptive analytics focuses on characterizing data by analyzing the central tension, dispersion, and shape.
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Predictive analytics involves using exploratory data analysis (EDA) in SPSS.
Predictive analytics involves using exploratory data analysis (EDA) in SPSS.
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Operationalize phase involves delivering final reports, briefings, code, and technical documents.
Operationalize phase involves delivering final reports, briefings, code, and technical documents.
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Supervised learning is a type of predictive analytics.
Supervised learning is a type of predictive analytics.
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Prescriptive analytics focuses on characterizing data by analyzing central tendency, dispersion, and shape.
Prescriptive analytics focuses on characterizing data by analyzing central tendency, dispersion, and shape.
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In the communicate results phase, the key findings are identified in relation to the analytics challenge and the business problem.
In the communicate results phase, the key findings are identified in relation to the analytics challenge and the business problem.
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Decision trees can handle only numerical inputs, not categorical inputs.
Decision trees can handle only numerical inputs, not categorical inputs.
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Decision trees assume linearity between predictor variables and the label.
Decision trees assume linearity between predictor variables and the label.
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Small variations in training data do not affect decision trees.
Small variations in training data do not affect decision trees.
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Having many irrelevant variables in the dataset makes decision trees a good choice.
Having many irrelevant variables in the dataset makes decision trees a good choice.
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Decision trees are robust when dealing with redundant or correlated variables.
Decision trees are robust when dealing with redundant or correlated variables.
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Overfitting is not a concern when using decision trees.
Overfitting is not a concern when using decision trees.
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Increasing the number of epochs to an infinite number makes sense for improving accuracy.
Increasing the number of epochs to an infinite number makes sense for improving accuracy.
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SVM is always better than ANN when dealing with linearly separable data.
SVM is always better than ANN when dealing with linearly separable data.
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Linear approaches are more effective than non-linear approaches in separating data points.
Linear approaches are more effective than non-linear approaches in separating data points.
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Kernel methods transform training data into lower dimensional spaces.
Kernel methods transform training data into lower dimensional spaces.
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Ensemble Classification techniques can provide worse results compared to a decision tree model.
Ensemble Classification techniques can provide worse results compared to a decision tree model.
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An orchestra is comparable to a soloist in terms of generating sound and music quality.
An orchestra is comparable to a soloist in terms of generating sound and music quality.
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