Fundamentals of Large Language Models →hardMultiple ChoiceObjective-mapped

1Z0-1127-25 Fundamentals of Large Language Models Practice Question

An AI assistant needs to solve complex math word problems step by step. Which prompting technique is most suitable?

⚠ Common exam trap

Oracle often tests the misconception that simply increasing model capacity (context window) or randomness (temperature) can substitute for structured reasoning, when in fact the prompting strategy itself is the critical factor for multi-step tasks.

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

✓

Chain-of-thought prompting with few-shot examples.

Chain-of-thought prompting with few-shot examples is most suitable because it guides the LLM to break down complex math word problems into intermediate reasoning steps, mimicking human problem-solving. Few-shot examples provide a template for the desired reasoning structure, which significantly improves accuracy on multi-step arithmetic tasks compared to direct answer generation.

Answer analysis

Option-by-option breakdown

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

✓
Chain-of-thought prompting with few-shot examples.
Why this is correct
Correct: CoT with examples guides reasoning.
✗
Zero-shot prompting with the problem only.
Why it's wrong here
Incorrect: May not elicit step-by-step reasoning.
✗
Prompting with a high temperature setting.
Why it's wrong here
Incorrect: High temperature increases randomness, not reasoning.
✗
Using a model with a larger context window.
Why it's wrong here
Incorrect: Larger context does not improve reasoning.

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1Z0-1127-25 Fundamentals of Large Language Models Practice Question

Why each option matters

Option-by-option breakdown

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