- A
Beam search
Why wrong: Beam search maintains multiple candidate sequences, not deterministic single-token selection.
- B
Temperature sampling
Why wrong: Temperature sampling adjusts the probability distribution and samples stochastically.
- C
Top-k sampling
Why wrong: Top-k sampling restricts to the k most probable tokens and then samples from them.
- D
Greedy decoding
Greedy decoding selects the token with the highest probability at each step, producing deterministic outputs.
Question 973 of 991
1Z0-1127 LLM Fundamentals Practice Question
This 1Z0-1127 practice question tests your understanding of llm fundamentals. 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 sampling strategy selects the token with the highest probability at each step, resulting in deterministic and often repetitive outputs?
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
✓
Greedy decoding
Greedy decoding selects the token with the highest probability at each step, making it deterministic and often leading to repetitive outputs because it always chooses the most likely next token without considering future alternatives. This contrasts with stochastic methods that introduce randomness or explore multiple paths.
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.
- ✗
Beam search
Why it's wrong here
Beam search maintains multiple candidate sequences, not deterministic single-token selection.
- ✗
Temperature sampling
Why it's wrong here
Temperature sampling adjusts the probability distribution and samples stochastically.
- ✗
Top-k sampling
Why it's wrong here
Top-k sampling restricts to the k most probable tokens and then samples from them.
- ✓
Greedy decoding
Why this is correct
Greedy decoding selects the token with the highest probability at each step, producing deterministic outputs.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Cisco often tests the distinction between deterministic (greedy) and stochastic (sampling-based) strategies, and the trap here is confusing 'greedy decoding' with 'beam search' because both involve selecting high-probability tokens, but beam search maintains multiple paths while greedy does not.
Detailed technical explanation
How to think about this question
Under the hood, greedy decoding applies argmax to the output probability distribution at each timestep, which is equivalent to setting temperature to 0 in many frameworks. This approach is computationally efficient but prone to repetition and lack of creativity, often used in baseline comparisons or when strict adherence to a single output path is required, such as in certain classification tasks or constrained generation.
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?
LLM Fundamentals — This question tests LLM Fundamentals — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Greedy decoding — Greedy decoding selects the token with the highest probability at each step, making it deterministic and often leading to repetitive outputs because it always chooses the most likely next token without considering future alternatives. This contrasts with stochastic methods that introduce randomness or explore multiple paths.
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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