NCA-AIIO Question 71
Select 3You are tasked with analyzing a dataset for an AI model training workflow under the supervision of a senior team member. The dataset contains missing values in multiple columns, inconsistent data formats, and outliers. What steps should you take to ensure the dataset is ready for the training process?
- A
Identify and handle missing values by imputing or removing them based on the senior team member's guidance.
- B
Ensure all numerical columns have consistent data formats, such as converting strings to floats where necessary.
- C
Ignore the outliers in the dataset, as they will not affect model training significantly.
- D
Document the data cleaning process and share it with the senior team member for validation.
- E
Start training the AI model without addressing the issues, as the model can handle noisy data automatically.
Show answer and explanation
Correct answers: A, B, D
Explanation
Preparing a dataset for AI model training requires addressing missing values, ensuring consistent data formats, and documenting the process. These steps ensure the data is clean and suitable for training. Ignoring outliers without analysis or skipping data preprocessing entirely can negatively impact model performance.
- A. Correct.
Handling missing values is a critical step in preparing data for AI model training, as missing data can lead to inaccurate or biased results. Imputation or removal should be done with proper guidance.
- B. Correct.
Ensuring consistent data formats is essential for compatibility with AI frameworks and to avoid errors during the training process.
- C. Incorrect.
Ignoring outliers is not recommended without proper analysis, as they can significantly skew the model's outputs, depending on the context and algorithm.
- D. Correct.
Documenting the data cleaning process is important for reproducibility and to ensure that the senior team member can validate the steps taken.
- E. Incorrect.
Starting training without addressing data quality issues can lead to poor model performance and unreliable results.