IBM Data Science Test 2025 – 400 Free Practice Questions to Pass the Exam

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What is the primary goal of data cleaning?

To enhance visualization of data results

To prepare the dataset for better model performance

The primary goal of data cleaning is to prepare the dataset for better model performance. This process involves identifying and correcting errors or inconsistencies in the data, which can significantly impact the accuracy and effectiveness of any analysis or predictive modeling. Clean data ensures that the models trained on it can learn effectively, minimize biases, and produce reliable outcomes.

When datasets contain inaccuracies, missing values, or outliers, they can skew model predictions or lead to misleading conclusions. Effective data cleaning leads to a more accurate representation of the underlying trends, enhancing the robustness of the resulting models. While the process may somewhat relate to visualization and data size, the core intent of data cleaning focuses on ensuring data quality for improved analytical outcomes and model training.

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To reduce the size of the dataset

To increase data redundancy

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