1Z0-1127-25 Fundamentals of Large Language Models Practice Question
Which THREE of the following are known limitations of large language models that practitioners must consider?
⚠ Common exam trap
Oracle often tests the misconception that LLMs have a hard token limit of a few hundred tokens, but the trap is that modern models have large context windows (e.g., 128K tokens) and the real limitation is the quadratic computational cost of attention, not a strict inability to process longer inputs.
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
✓
Hallucination of facts not present in the input.
Large language models (LLMs) are prone to hallucination, where they generate plausible-sounding but factually incorrect information that was not present in the input. This occurs because LLMs are next-token predictors without a built-in fact-checking mechanism, and they can invent details, citations, or events to maintain coherence. Practitioners must implement retrieval-augmented generation (RAG) or external verification to mitigate this risk.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Hallucination of facts not present in the input.
Why this is correct
LLMs often generate plausible but false information.
- ✓
Generation of toxic or harmful language.
Why this is correct
Without safeguards, LLMs can produce harmful content.
- ✗
Limited to processing only one language at a time.
Why it's wrong here
LLMs can handle multiple languages in the same session.
- ✓
Bias amplification from training data.
Why this is correct
LLMs learn and can amplify biases present in data.
- ✗
Inability to process inputs longer than a few hundred tokens.
Why it's wrong here
Many LLMs support thousands of tokens.
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