AI0-001 AI Concepts and Foundations Practice Question
Exhibit
Refer to the exhibit.
```
{
"dataset": {
"name": "customer_churn",
"features": ["age", "tenure", "monthly_charges", "total_charges"],
"target": "churn",
"splits": {
"train": 0.7,
"test": 0.15,
"validation": 0.15
}
},
"model": {
"type": "RandomForestClassifier",
"params": {
"n_estimators": 200,
"max_depth": 10,
"random_state": 42
}
}
}
```Refer to the exhibit. A data scientist defines a model configuration in JSON. Which component is missing from the configuration for a complete machine learning pipeline?
⚠ Common exam trap
CompTIA often tests the misconception that a model configuration is complete if it includes the model type, hyperparameters, and evaluation metrics, but candidates overlook that data preprocessing is a mandatory pipeline stage for transforming raw data before training.
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 preprocessing steps
A complete machine learning pipeline must include data preprocessing steps to transform raw data into a format suitable for model training. The JSON configuration defines the model type, evaluation metrics, and training hyperparameters, but omits any specification for data cleaning, normalization, feature encoding, or splitting, which are essential for reproducibility and model performance.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Training hyperparameters
Why it's wrong here
Hyperparameters are already included.
- ✓
Data preprocessing steps
Why this is correct
Preprocessing (scaling, encoding) is missing.
- ✗
Model type
Why it's wrong here
Model type is already specified.
- ✗
Evaluation metrics
Why it's wrong here
Metrics can be defined separately; preprocessing is more fundamental.
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