Question 759 of 991

LLM Fundamentals →mediumMultiple SelectObjective-mapped

1Z0-1127 LLM Fundamentals Practice Question

This 1Z0-1127 practice question tests your understanding of llm fundamentals. The scenario asks you to isolate a root cause — eliminate options that address a different problem before choosing. 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 of the following are characteristics of decoder-only models like GPT? (Select TWO)

A
They process input through an encoder and a decoder
Why wrong: That describes encoder-decoder models, not decoder-only.
B
They use bidirectional self-attention
Why wrong: Decoder-only models use causal (unidirectional) attention, not bidirectional.
C
They use masked self-attention to prevent attending to future tokens
Masked self-attention ensures each token only attends to previous tokens.
D
They are ideal for tasks requiring full bidirectional context like NER
Why wrong: Bidirectional context is typical of encoder-only models (e.g., BERT), not decoder-only.
E
They are typically used for generative tasks like text completion
Decoder-only models are autoregressive and excel at text generation.

Full breakdown with real-world context →

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

✓

They use masked self-attention to prevent attending to future tokens

Decoder-only models use masked self-attention (causal) and generate tokens left-to-right. They cannot use bidirectional context because future tokens are masked.

Key principle: Count usable hosts — not total addresses — and remember that the network and broadcast addresses are not available to hosts in standard IPv4 subnets.

Answer analysis

Option-by-option breakdown

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

✗
They process input through an encoder and a decoder
Why it's wrong here
That describes encoder-decoder models, not decoder-only.
✗
They use bidirectional self-attention
Why it's wrong here
Decoder-only models use causal (unidirectional) attention, not bidirectional.
✓
They use masked self-attention to prevent attending to future tokens
Why this is correct
Masked self-attention ensures each token only attends to previous tokens.
Related concept
CIDR notation defines the prefix length.
✗
They are ideal for tasks requiring full bidirectional context like NER
Why it's wrong here
Bidirectional context is typical of encoder-only models (e.g., BERT), not decoder-only.
✓
They are typically used for generative tasks like text completion
Why this is correct
Decoder-only models are autoregressive and excel at text generation.
Related concept
CIDR notation defines the prefix length.

Common exam traps

Common exam trap: usable hosts are not the same as total addresses

Subnetting questions often tempt you into counting all addresses. In normal IPv4 subnets, the network and broadcast addresses are not usable host addresses.

Detailed technical explanation

How to think about this question

Subnetting questions test whether you can identify the network, broadcast address, usable range, mask and correct subnet. Slow down enough to calculate the block size correctly.

KKey Concepts to Remember

CIDR notation defines the prefix length.
Block size helps identify subnet boundaries.
Network and broadcast addresses are not usable hosts in normal IPv4 subnets.
The required host count determines the smallest suitable subnet.

TExam Day Tips

Write the block size before choosing the subnet.
Check whether the question asks for hosts, subnets or a specific address range.
Do not confuse /24, /25, /26 and /27 host counts.

Key takeaway

Count usable hosts — not total addresses — and remember that the network and broadcast addresses are not available to hosts in standard IPv4 subnets.

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.

Review block sizes, usable host formulas (2^n − 2), and how to find network and broadcast addresses for /24 through /30. Then practise related 1Z0-1127 subnetting questions on CIDR, address ranges, and subnet selection.

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FAQ

Questions learners often ask

What does this 1Z0-1127 question test?

LLM Fundamentals — This question tests LLM Fundamentals — CIDR notation defines the prefix length..

What is the correct answer to this question?

The correct answer is: They use masked self-attention to prevent attending to future tokens — Decoder-only models use masked self-attention (causal) and generate tokens left-to-right. They cannot use bidirectional context because future tokens are masked.

What should I do if I get this 1Z0-1127 question wrong?

What is the key concept behind this question?

CIDR notation defines the prefix length.

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Last reviewed: Jul 4, 2026

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