- A
Limiting max tokens
Why wrong: Max tokens only limits length, not content sensitivity.
- B
OCI Data Safe masking
Why wrong: Data Safe is for database security, not model outputs.
- C
Output filtering via custom inference wrapper
A custom wrapper can filter outputs to remove sensitive information.
- D
Input sanitization
Why wrong: Input sanitization reduces risk but does not protect against model generating sensitive content.
Question 753 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.
An architect needs to ensure that an LLM deployed in OCI does not reveal sensitive information in its outputs. Which technique should be used?
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
✓
Output filtering via custom inference wrapper
Option C is correct because output filtering via a custom inference wrapper allows the architect to inspect and sanitize the model's generated text before it reaches the user, preventing the leakage of sensitive information such as PII, credentials, or internal data. This technique operates at the application layer, intercepting the LLM's response and applying rules or regex patterns to redact or block prohibited content, which is essential for compliance and data security in production deployments.
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.
- ✗
Limiting max tokens
Why it's wrong here
Max tokens only limits length, not content sensitivity.
- ✗
OCI Data Safe masking
Why it's wrong here
Data Safe is for database security, not model outputs.
- ✓
Output filtering via custom inference wrapper
Why this is correct
A custom wrapper can filter outputs to remove sensitive information.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Input sanitization
Why it's wrong here
Input sanitization reduces risk but does not protect against model generating sensitive content.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Oracle often tests the distinction between input-side controls (like sanitization) and output-side controls (like filtering), and the trap here is that candidates confuse input sanitization with output filtering, assuming that cleaning the input is sufficient to prevent data leakage from the model's training or internal knowledge.
Trap categories for this question
Command / output trap
Data Safe is for database security, not model outputs.
Detailed technical explanation
How to think about this question
Under the hood, a custom inference wrapper acts as a middleware component that intercepts the LLM's raw output stream, often using a Python-based proxy or OCI API Gateway with custom logic. It can implement pattern matching (e.g., regex for credit card numbers or API keys), call external classification models, or apply context-aware redaction using libraries like Presidio or custom NLP rules. In a real-world scenario, this is critical for regulated industries like healthcare or finance, where even a single accidental disclosure of PHI or financial data in a chatbot response could lead to severe compliance violations.
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.
Quick reference
OSI Model Reference
| Layer | Name | PDU | Key Protocols / Devices |
|---|---|---|---|
| 7 | Application | Data | HTTP, HTTPS, DNS, SMTP, FTP, SSH |
| 6 | Presentation | Data | TLS / SSL, JPEG, ASCII encoding |
| 5 | Session | Data | NetBIOS, RPC, SIP |
| 4 | Transport | Segment / Datagram | TCP, UDP |
| 3 | Network | Packet | IP, ICMP, OSPF — Routers |
| 2 | Data Link | Frame | Ethernet, Wi-Fi, PPP — Switches, Bridges |
| 1 | Physical | Bits | Cables, NICs, Hubs, Repeaters |
What to study next
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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: Output filtering via custom inference wrapper — Option C is correct because output filtering via a custom inference wrapper allows the architect to inspect and sanitize the model's generated text before it reaches the user, preventing the leakage of sensitive information such as PII, credentials, or internal data. This technique operates at the application layer, intercepting the LLM's response and applying rules or regex patterns to redact or block prohibited content, which is essential for compliance and data security in production deployments.
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: Jun 30, 2026
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