During a qualitative evaluation of an LLM-based news summarizer, a reviewer notices that the model sometimes produces summaries that subtly favor one political party over another. Which type of issue is this, and what guardrail would be most appropriate?

Incoherence; implement a system prompt that instructs the model to be neutral.

Bias issue; implement an output guardrail that detects and flags politically biased language.

Factual inaccuracy; implement an input guardrail that filters out political topics.

Irrelevance; implement a post-processing step that checks for topic alignment.

A customer-service chatbot is being attacked by users who inject malicious instructions into their prompts to make the model ignore its guidelines. The team wants to implement a guardrail that neutralizes these injection attempts before the model processes the prompt. Which approach is most appropriate?

Use an output guardrail that checks the model's response for signs of guideline violation.

Add a stronger system prompt that emphasizes following guidelines.

Disable the chatbot's ability to process user input.

Use an input guardrail that detects and sanitizes prompt-injection patterns.

Which of the following is a safety concern that should be identified in LLM outputs?

The response uses complex vocabulary

The response is longer than expected

The response is written in a formal tone

The response includes instructions for creating a weapon

A company uses an LLM to generate product descriptions. During testing, they find that the model sometimes produces descriptions that are factually incorrect about product specifications. Which type of issue is this, and what guardrail would help?

Bias; implement an output guardrail that detects biased language.

Irrelevance; implement an input guardrail that filters out prompts about product specs.

Factual inaccuracy; implement an output guardrail that validates product specifications against a database.

Incoherence; implement a post-processing step that rephrases the description.

What is the primary purpose of guardrails in an LLM application?

To increase the model's vocabulary size

To prevent negative outcomes such as unsafe or low-quality responses

To reduce the latency of model inference

DatabricksCertified Generative AI Engineer Associate

Domain 3Objective 4

Safety and Guardrails GENERATIVE-AI-ENGINEER-ASSOCIATE Practice Questions (Page 3)

Part of the Section 3: Application Development domain, which makes up ~18% of our current practice bank. Databricks does not publish an official question count, but from its 90-minute exam (~35–60 total, ~6–11 in this domain), expect 1–2 from this objective — we provide 31 practice questions to prepare you well beyond it. (estimate)

31questions here

7free pages

7concepts

Questions 11–15

11application · medium
During a qualitative evaluation of an LLM-based news summarizer, a reviewer notices that the model sometimes produces summaries that subtly favor one political party over another. Which type of issue is this, and what guardrail would be most appropriate?
Select an answer first
12application · medium
A customer-service chatbot is being attacked by users who inject malicious instructions into their prompts to make the model ignore its guidelines. The team wants to implement a guardrail that neutralizes these injection attempts before the model processes the prompt. Which approach is most appropriate?
Select an answer first
13foundation · easy
Which of the following is a safety concern that should be identified in LLM outputs?
Select an answer first
14application · medium
A company uses an LLM to generate product descriptions. During testing, they find that the model sometimes produces descriptions that are factually incorrect about product specifications. Which type of issue is this, and what guardrail would help?
Select an answer first
15foundation · easy
What is the primary purpose of guardrails in an LLM application?
Select an answer first

Finished these 5 questions?

Review the revealed explanations, or continue through the curriculum.

Next 5 questions →Back to GENERATIVE-AI-ENGINEER-ASSOCIATE

Free Basic Practice is a study aid with revealable answers — not a scored exam. Examers.io is independent and not affiliated with or endorsed by Databricks. “GENERATIVE-AI-ENGINEER-ASSOCIATE” is a trademark of its owner, used for identification only.