AI0-001 Implementing AI Solutions Practice Question
In the AI project lifecycle, which phase involves partitioning the dataset into training, validation, and test sets?
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Data preparation
Data preparation includes splitting the data to evaluate model performance and prevent leakage.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
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Data acquisition
Why it's wrong here
Splitting into training, validation and test sets is data preparation, not acquisition; acquisition only gathers and labels raw data. It is tempting because both phases touch the dataset, but acquisition occurs before any partitioning decision is made.
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Model selection
Why it's wrong here
Model selection chooses and tunes candidate algorithms, operating after data preparation is complete. It is tempting because it sits within the same iterative lifecycle, but splitting data into training, validation and test sets belongs to data preparation, which precedes and feeds model selection.
- ✓
Data preparation
Why this is correct
Data preparation covers cleaning, labelling and splitting the dataset into training, validation and test partitions before modelling begins. These subsets support fitting, hyperparameter tuning and unbiased final evaluation respectively, so the split belongs to this phase rather than data collection or deployment.
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Problem definition
Why it's wrong here
Problem definition sets objectives, success metrics and scope before any data exists, so it cannot partition a dataset. It is tempting because it is the earliest lifecycle phase, but partitioning happens later during data preparation.
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Written by Johnson Ajibi, MSc IT Security
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