1Z0-1127-25 LLM Fundamentals Practice Question
In a Transformer model, what is the role of positional encoding?
⚠ Common exam trap
The 1Z0-1127 exam often tests the misconception that positional encoding is responsible for enabling parallel processing, when in fact it is the self-attention mechanism's non-sequential computation that allows parallelism, and positional encoding merely injects order information into that parallel framework.
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
✓
To provide information about the position of each token in the sequence
Positional encoding is essential in Transformer models because the self-attention mechanism processes all tokens in parallel and has no inherent notion of sequence order. By adding positional encodings (often sinusoidal or learned) to the input embeddings, the model can distinguish between tokens at different positions, enabling it to capture word order and relative positions. Without this, the model would treat the sequence as a bag of tokens, losing all sequential context.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
To reduce the number of parameters in the model
Why it's wrong here
Positional encoding adds parameters, not reduces them.
- ✗
To enable the model to process tokens in parallel
Why it's wrong here
Parallel processing is enabled by the architecture, not specifically by positional encoding.
- ✗
To encode the semantic meaning of each token
Why it's wrong here
Semantic meaning is captured by embeddings, not positional encoding.
- ✓
To provide information about the position of each token in the sequence
Why this is correct
This is the exact purpose of positional encoding.
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