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Machine Learning and Deep LearninghardMultiple ChoiceObjective-mapped

AI0-001 Machine Learning and Deep Learning Practice Question

Exhibit

Refer to the exhibit.

```
Epoch 1/10
 - loss: 2.3026 - accuracy: 0.1000 - val_loss: 2.3026 - val_accuracy: 0.1000
Epoch 2/10
 - loss: 2.3026 - accuracy: 0.1000 - val_loss: 2.3026 - val_accuracy: 0.1000
```

A data scientist is training a multi-class classifier with 10 classes. The training log shows the above output for the first two epochs. What is the most likely cause?

⚠ Common exam trap

CompTIA often tests the misconception that a flat loss curve is always due to data issues or model capacity, when in fact it is a classic symptom of a zero or extremely small learning rate that prevents any weight updates.

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

The learning rate is set to zero

When the learning rate is set to zero, the optimizer makes no updates to the model weights regardless of the computed gradients. The training loss remains constant across epochs because the parameters never change, which matches the log showing identical loss values for both epochs. This is a common debugging scenario where a misconfigured learning rate prevents any learning from occurring.

Answer analysis

Option-by-option breakdown

For each option: why learners choose it and why it is or isn't the right answer here.

  • Batch normalization is disabled

    Why it's wrong here

    Disabling batch normalization may slow training but would not cause constant loss.

  • The learning rate is set to zero

    Why this is correct

    A zero learning rate prevents any weight updates, so the model outputs remain at initial random values.

  • The dataset is imbalanced

    Why it's wrong here

    Imbalanced data would cause the model to predict the majority class, not constant loss at 2.3026.

  • The model is overfitting

    Why it's wrong here

    Overfitting would show decreasing training loss and increasing validation loss, not constant values.

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Written by Johnson Ajibi, MSc IT Security

Senior Network & Security Engineer · founder of Courseiva

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