Fundamentals of Large Language Models →hardMultiple SelectObjective-mapped

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

Which THREE are known challenges when deploying large language models in production?

⚠ Common exam trap

Oracle often tests the distinction between known challenges (bias, cost, hallucination) and desirable properties (fast inference, low memory) that are actually false for LLMs, trapping candidates who confuse optimization goals with current limitations.

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

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Bias in training data perpetuating stereotypes

Large language models (LLMs) are trained on vast, unfiltered internet text corpora that inherently contain societal biases. These biases are learned and can be amplified during inference, leading to outputs that perpetuate harmful stereotypes, which is a well-documented production challenge.

Answer analysis

Option-by-option breakdown

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

✓
Bias in training data perpetuating stereotypes
Why this is correct
Models can reflect and amplify biases from training data.
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High computational cost for inference
Why this is correct
LLMs require substantial compute resources.
✓
Hallucination of plausible but incorrect information
Why this is correct
LLMs can generate confident false statements.
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Fast inference speed due to parallelization
Why it's wrong here
Inference is often slow, not fast.
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Low memory footprint
Why it's wrong here
Large models require significant memory.

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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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