- A
Encoder-only uses bidirectional attention and is suited for classification or NER; decoder-only uses causal attention and is suited for text generation
Correct distinction between the two architectures.
- B
Encoder-only is trained for text generation; decoder-only is trained for classification
Why wrong: Typical training objectives are reversed.
- C
Both use the same attention pattern but differ in number of layers
Why wrong: They use fundamentally different attention patterns.
- D
Encoder-only uses causal attention; decoder-only uses bidirectional attention
Why wrong: That’s the opposite of the correct description.
Question 117 of 991
1Z0-1127 LLM Fundamentals Practice Question
This 1Z0-1127 practice question tests your understanding of llm fundamentals. Compare every option against the stated constraints before choosing — the best answer satisfies all requirements, not just the most obvious one. 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 of the following best describes the difference between an encoder-only model (e.g., BERT) and a decoder-only model (e.g., GPT)?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue:
"best"Why it matters: Signals that multiple options may be partially correct. Choose the option that most directly solves the exact problem described, not the one that sounds most complete.
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
✓
Encoder-only uses bidirectional attention and is suited for classification or NER; decoder-only uses causal attention and is suited for text generation
Option A is correct because encoder-only models like BERT employ bidirectional attention, allowing each token to attend to all other tokens in both directions, which is ideal for tasks requiring full context understanding such as classification or named entity recognition (NER). In contrast, decoder-only models like GPT use causal (masked) attention, where each token can only attend to previous tokens, making them suitable for autoregressive text generation.
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.
- ✓
Encoder-only uses bidirectional attention and is suited for classification or NER; decoder-only uses causal attention and is suited for text generation
Why this is correct
Correct distinction between the two architectures.
Clue confirmation
The clue word "best" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Encoder-only is trained for text generation; decoder-only is trained for classification
Why it's wrong here
Typical training objectives are reversed.
- ✗
Both use the same attention pattern but differ in number of layers
Why it's wrong here
They use fundamentally different attention patterns.
- ✗
Encoder-only uses causal attention; decoder-only uses bidirectional attention
Why it's wrong here
That’s the opposite of the correct description.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Cisco often tests the reversal of attention patterns (bidirectional vs. causal) as a common trap, leading candidates to confuse which architecture is suited for generation versus understanding tasks.
Detailed technical explanation
How to think about this question
Under the hood, bidirectional attention in BERT computes attention scores over the entire input sequence simultaneously, enabling rich contextual embeddings for each token. Causal attention in GPT applies a triangular mask to the attention matrix, ensuring that during generation, each token's representation depends only on past tokens, which is critical for maintaining autoregressive consistency. A subtle behavior is that while BERT can be adapted for generation via techniques like masked language modeling, its core architecture lacks the causal masking needed for coherent left-to-right text generation without modification.
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 network engineer segments a warehouse floor into three subnets: 20 scanners, 5 printers, and 2 management hosts. Picking the wrong mask wastes addresses or leaves too few usable hosts. Exam questions test whether you can apply CIDR notation, calculate block size, and identify the correct usable-host range for a given prefix.
What to study next
Got this wrong? Here's your next step.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
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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: Encoder-only uses bidirectional attention and is suited for classification or NER; decoder-only uses causal attention and is suited for text generation — Option A is correct because encoder-only models like BERT employ bidirectional attention, allowing each token to attend to all other tokens in both directions, which is ideal for tasks requiring full context understanding such as classification or named entity recognition (NER). In contrast, decoder-only models like GPT use causal (masked) attention, where each token can only attend to previous tokens, making them suitable for autoregressive text generation.
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.
Are there clue words in this question I should notice?
Yes — watch for: "best". Signals that multiple options may be partially correct. Choose the option that most directly solves the exact problem described, not the one that sounds most complete.
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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