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Data Analytics (Eng) / Data Analitika (Ing) - 344

Data Analytics (Eng) / Data Analitika (Ing) - 344

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Which of the following best describes a redundant feature?

A feature that is correlated with the output target, but is not correlated with other features.

A feature that has no correlation with the output target.

A feature that is only correlated with other features.

A feature that is correlated with the output target and other features.

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In a feature selection process, what should be done with irrelevant features?

They should be transformed to become relevant.

They should be removed from the dataset as they do not contribute to the predictive power of the model.

They should be merged with other features.

They should be retained to ensure completeness.

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Why is Pearson correlation used rather than covariance?

Pearson correlation measures the relationship between more than two variables.

Pearson correlation standardizes the measure of the relationship between two variables, making it easier to interpret.

Unlike covariance, Pearson correlation values are limited to zero to one.

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Which of the following best describes the filter method of feature selection?

Embedding feature selection within the model algorithm.

Performing statistical tests between descriptive features and the target feature to identify relevant features.

Randomly selecting features to include in the model.

Adding or removing features from the model and comparing performance.

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What is the first step in the filter method of feature selection?

Removing irrelevant features based on model performance.

Embedding feature selection within the algorithm.

Building the initial model with all features.

Performing statistical tests to evaluate the relevance of each feature.

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What does covariance measure?

The average value of a data set

The spread of values in a single data set

The probability of a particular outcome

The degree to which two variables change together

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Which of the following derived features can be created from a "Birthdate" column to improve a demographic model?

Keeping the "Birthdate" column unchanged.

Creating an "Age" column by calculating the difference between the current date and the birthdate.

Removing the "Birthdate" column to reduce the dataset size.

Ignoring the "Birthdate" column.

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In an ABT for customer churn analysis, each row would most likely represent:

A summary of all transactions in a month

A transaction made by any customer

The overall churn rate for the company

A single customer

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How can data staleness negatively impact an ABT used for customer behavior analysis?

Stale data can simplify the feature engineering process.

Stale data can reduce the computational load on the system.

Stale data can result in models that do not reflect current customer behaviors and trends.

Stale data can increase the storage requirements for the ABT.

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What detail level do the rows in an Analytic Base Table (ABT) typically capture?

Detailed individual records or instances

Aggregated summaries

High-level overviews

Metadata information

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