NCA-AIIO Question 72
Select 3You are working under the supervision of a senior team member on a data analysis project involving large datasets for an AI model. The senior team member asks you to evaluate the distribution of a dataset before preprocessing. Which of the following steps should you take to conduct this analysis effectively?
- A
Visualize the dataset using histograms or box plots to assess data distribution.
- B
Directly start training the AI model to see how it performs without analyzing the data.
- C
Calculate statistical measures like mean, median, standard deviation, and skewness.
- D
Identify any missing or anomalous data points within the dataset.
- E
Assume the data is normally distributed and skip further checks.
Show answer and explanation
Correct answers: A, C, D
Explanation
Conducting data analysis under supervision involves thoroughly evaluating the dataset to ensure its quality and suitability for AI model training. Steps like visualizing the data, calculating statistical measures, and identifying missing or anomalous data points are crucial. Skipping these steps or making assumptions without proper analysis can lead to poor model performance and unreliable results.
- A. Correct.
Visualizing the dataset using histograms or box plots is a common and effective way to assess the distribution of data and detect patterns, outliers, or anomalies.
- B. Incorrect.
Directly starting training without analyzing the data is not a recommended practice. Data analysis is crucial for identifying issues that might affect model performance.
- C. Correct.
Calculating statistical measures like mean, median, standard deviation, and skewness provides important insights into the dataset's properties and distribution.
- D. Correct.
Identifying missing or anomalous data points is an essential step in ensuring data quality and integrity before proceeding with further analysis or model training.
- E. Incorrect.
Assuming the data is normally distributed without verification can lead to incorrect conclusions and suboptimal preprocessing steps.