NCA-AIIO Question 74
Select 4You are assisting a senior data scientist in analyzing a dataset for training an AI model. The dataset contains missing values, inconsistent formatting, and some outliers. The senior team member asks you to suggest preprocessing steps before moving forward. Which of the following steps should be included in your recommendation?
- A
Impute missing values using appropriate statistical methods or domain-specific knowledge.
- B
Normalize or standardize features to ensure they are on the same scale.
- C
Directly train the AI model without addressing missing values to avoid data manipulation.
- D
Remove outliers only if they are deemed irrelevant to the problem domain.
- E
Ensure the dataset formatting is consistent (e.g., date formats, categorical encoding).
Show answer and explanation
Correct answers: A, B, D, E
Explanation
Preprocessing data is a crucial step in AI workflows to clean and prepare the dataset for analysis and training. Addressing missing values, feature scaling, outlier handling, and consistent formatting ensures the dataset is reliable and suitable for training. Skipping these steps can lead to poor model performance or failures during training.
- A. Correct.
Imputing missing values is a standard data preprocessing step that ensures the dataset is complete and prevents errors during model training.
- B. Correct.
Normalizing or standardizing features is important for many algorithms to work effectively, especially those sensitive to feature scaling.
- C. Incorrect.
Directly training the AI model without addressing missing values is incorrect, as it can lead to errors or biases in the model.
- D. Correct.
Removing outliers is valid if they are irrelevant to the problem domain, but they must be carefully evaluated to avoid discarding valuable data.
- E. Correct.
Ensuring consistent formatting is crucial for avoiding errors during data analysis and model training, as inconsistent formats can cause issues in downstream processes.