- A
LLMs have a fixed context window that cannot be extended.
Why wrong: Context windows can be extended via techniques like sliding window or ALiBi.
- B
LLMs can perform zero-shot learning without any task-specific training.
Zero-shot learning is a key capability of LLMs.
- C
LLMs understand and reason about code as well as natural language.
Many LLMs are trained on code and exhibit code understanding.
- D
LLMs always produce factually accurate outputs.
Why wrong: LLMs can hallucinate and produce inaccurate information.
- E
LLMs require fine-tuning for every new task.
Why wrong: Few-shot or zero-shot prompting often eliminates the need for fine-tuning.
Question 410 of 991
1Z0-1127 Fundamentals of Large Language Models Practice Question
This 1Z0-1127 practice question tests your understanding of fundamentals of large language models. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
Which TWO statements about large language model (LLM) capabilities are correct?
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
✓
LLMs can perform zero-shot learning without any task-specific training.
Option B is correct because LLMs are pre-trained on vast and diverse datasets, enabling them to perform zero-shot learning by generalizing from their training to new tasks without any task-specific fine-tuning. This capability stems from the model's ability to understand prompts and generate relevant responses based on patterns learned during pre-training, not from explicit instruction on the new task.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
LLMs have a fixed context window that cannot be extended.
Why it's wrong here
Context windows can be extended via techniques like sliding window or ALiBi.
- ✓
LLMs can perform zero-shot learning without any task-specific training.
Why this is correct
Zero-shot learning is a key capability of LLMs.
Related concept
Read the scenario before looking for a memorised answer.
- ✓
LLMs understand and reason about code as well as natural language.
Why this is correct
Many LLMs are trained on code and exhibit code understanding.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
LLMs always produce factually accurate outputs.
Why it's wrong here
LLMs can hallucinate and produce inaccurate information.
- ✗
LLMs require fine-tuning for every new task.
Why it's wrong here
Few-shot or zero-shot prompting often eliminates the need for fine-tuning.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Oracle often tests the misconception that LLMs are rigid and require explicit retraining for every new task, when in fact their pre-training enables flexible generalization through zero-shot and few-shot learning.
Detailed technical explanation
How to think about this question
Zero-shot learning in LLMs works because the model's pre-training on a massive corpus of text (e.g., trillions of tokens) creates a rich internal representation of language patterns, syntax, and world knowledge. When given a prompt like 'Translate this to French: Hello', the model leverages its learned associations to generate the correct output without ever being explicitly trained on translation tasks. This is distinct from few-shot learning, where a few examples are provided in the prompt to guide the model's output.
KKey Concepts to Remember
- Read the scenario before looking for a memorised answer.
- Find the constraint that changes the correct option.
- Eliminate answers that are true in general but not in this case.
TExam Day Tips
- Watch for words such as best, first, most likely and least administrative effort.
- Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
How this comes up in practice
A practitioner preparing for the 1Z0-1127 exam encounters this exact type of scenario on the job. The correct answer here is not the most general option — it is the best answer for the specific constraint described. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Real exam questions reward reading the full scenario before eliminating options, because the constraint defines which answer fits.
What to study next
Got this wrong? Here's your next step.
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FAQ
Questions learners often ask
What does this 1Z0-1127 question test?
Fundamentals of Large Language Models — This question tests Fundamentals of Large Language Models — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: LLMs can perform zero-shot learning without any task-specific training. — Option B is correct because LLMs are pre-trained on vast and diverse datasets, enabling them to perform zero-shot learning by generalizing from their training to new tasks without any task-specific fine-tuning. This capability stems from the model's ability to understand prompts and generate relevant responses based on patterns learned during pre-training, not from explicit instruction on the new task.
What should I do if I get this 1Z0-1127 question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Last reviewed: Jul 4, 2026
This 1Z0-1127 practice question is part of Courseiva's free Oracle certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the 1Z0-1127 exam.
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