NCA-GENL Question 93
Select 3You are tasked with analyzing a large dataset containing text samples to prepare it for training a generative AI language model. Under the supervision of a senior team member, what are the appropriate steps you should take to ensure the dataset is ready for use?
- A
Perform preprocessing such as tokenization, lowercasing, and removing special characters.
- B
Directly feed the raw dataset into the training pipeline to save time.
- C
Collaborate with your senior team member to identify and remove biases or imbalances in the data.
- D
Analyze the dataset for missing or corrupt entries and address these issues.
- E
Add synthetic noise to the dataset without consulting your senior team member to improve model robustness.
Show answer and explanation
Correct answers: A, C, D
Explanation
Data analysis and preparation are critical steps before training any generative AI model. Preprocessing ensures the dataset is in the correct format, while collaborating with a senior team member helps identify and mitigate potential issues such as bias or missing data. Feeding raw data or taking unapproved actions, such as adding synthetic noise, can lead to suboptimal model performance or ethical concerns.
- A. Correct.
Preprocessing steps like tokenization, lowercasing, and removing special characters are essential to prepare the text data for training. These steps ensure the model can process the data effectively.
- B. Incorrect.
Feeding raw datasets directly into the training pipeline is not advisable, as the data may contain errors, inconsistencies, or irrelevant information that could negatively impact the model's performance.
- C. Correct.
Collaborating with a senior team member to address biases or imbalances is a crucial step in responsible AI development. This ensures that the model does not reinforce harmful stereotypes or unfair patterns.
- D. Correct.
Analyzing the dataset for missing or corrupt entries and resolving these issues is a fundamental part of data preparation. Models trained on incomplete or erroneous data may perform poorly.
- E. Incorrect.
Adding synthetic noise to the dataset without consulting a senior team member is not appropriate, as it may lead to unintended consequences or degrade the dataset quality if done incorrectly.