LLM Application Design Practice Exam

Question 1 LLM Application Design

How can you practice human escalation?

RAG retrieves relevant external documents and adds them to model context so answers can use fresh or private knowledge. Apply it to a small realistic example, explain the decision out loud, then check whether the result matches the intended outcome. It protects users and reduces compliance risk. A simple example is using retrieval quality in a small project, note, or decision before applying it to a larger real-world situation.

Question 2 LLM Application Design

What is embedding search?

A simple example is using pii handling in a small project, note, or decision before applying it to a larger real-world situation. A simple example is using context compression in a small project, note, or decision before applying it to a larger real-world situation. Source citation links generated claims to supporting documents or records. Embedding search converts text into vectors and finds semantically similar passages even when exact keywords differ.

Question 3 LLM Application Design

How can you practice model routing?

Tool calling lets an LLM request structured actions such as database queries, web searches, calculations, or file edits instead of only producing text. Retrieval quality measures whether the right documents are found for a user question. Apply it to a small realistic example, explain the decision out loud, then check whether the result matches the intended outcome. Editing prompts in production without a history of what changed.

Question 4 LLM Application Design

How can you practice guardrail?

Apply it to a small realistic example, explain the decision out loud, then check whether the result matches the intended outcome. A tool schema describes the tool name, inputs, required fields, and allowed values. A simple example is using prompt caching in a small project, note, or decision before applying it to a larger real-world situation. Tool calling lets an LLM request structured actions such as database queries, web searches, calculations, or file edits instead of only producing text.

Question 5 LLM Application Design

What is a simple example of output parser?

Source citation links generated claims to supporting documents or records. A simple example is using model routing in a small project, note, or decision before applying it to a larger real-world situation. A simple example is using output parser in a small project, note, or decision before applying it to a larger real-world situation. Showing raw errors to users without guidance.

Question 6 LLM Application Design

What question should you ask when using red teaming?

Context compression summarizes or filters older information to fit within the model context window. Ask: what problem is this solving, what trade-off does it create, and how will I know it worked? Launching without logs that connect user problems to model traces. It adds reliability around probabilistic model behavior.

Question 7 LLM Application Design

How can you practice prompt versioning?

It reveals vulnerabilities before users or attackers find them. Apply it to a small realistic example, explain the decision out loud, then check whether the result matches the intended outcome. Source citation links generated claims to supporting documents or records. Launching without logs that connect user problems to model traces.

Question 8 LLM Application Design

Why does output parser matter in LLM application design?

Grounding ties model output to provided sources, records, or tool results, reducing hallucinations and making answers easier to verify. Assuming the model will always return perfectly formatted JSON. It makes model output usable by software systems. A simple example is using retrieval quality in a small project, note, or decision before applying it to a larger real-world situation.

Question 9 LLM Application Design

What question should you ask when using source citation?

Chunking splits documents into smaller passages that can be indexed and retrieved effectively without losing too much meaning. Clear schemas reduce malformed calls and unsafe ambiguity. Ask: what problem is this solving, what trade-off does it create, and how will I know it worked? Editing prompts in production without a history of what changed.

Question 10 LLM Application Design

What is an evaluation set?

Tool calling lets an LLM request structured actions such as database queries, web searches, calculations, or file edits instead of only producing text. A simple example is using conversation memory in a small project, note, or decision before applying it to a larger real-world situation. Saving sensitive or irrelevant data without a retention policy. An evaluation set is a collection of representative prompts, expected behavior, and scoring rules used to test an LLM app.

Question 11 LLM Application Design

Why does observability matter in LLM application design?

Chunking splits documents into smaller passages that can be indexed and retrieved effectively without losing too much meaning. It makes production LLM behavior diagnosable. A guardrail is a rule, validation step, or workflow constraint that prevents unsafe or low-quality output. Assuming the model will always return perfectly formatted JSON.

Question 12 LLM Application Design

What does fallback response mean in LLM application design?

An evaluation set is a collection of representative prompts, expected behavior, and scoring rules used to test an LLM app. It makes model output usable by software systems. A fallback response is what the app says or does when the model or tool fails. Conversation memory stores relevant user or task context across turns or sessions.

Question 13 LLM Application Design

What is a simple example of prompt caching?

Citing sources that were not actually used or do not support the claim. A fallback response is what the app says or does when the model or tool fails. It improves efficiency and user experience. A simple example is using prompt caching in a small project, note, or decision before applying it to a larger real-world situation.

Question 14 LLM Application Design

What is a simple example of model routing?

A simple example is using model routing in a small project, note, or decision before applying it to a larger real-world situation. Showing raw errors to users without guidance. A simple example is using conversation memory in a small project, note, or decision before applying it to a larger real-world situation. A fallback response is what the app says or does when the model or tool fails.

Question 15 LLM Application Design

What is a common mistake with conversation memory?

Observability tracks requests, model choices, tools, latency, cost, and failures. Saving sensitive or irrelevant data without a retention policy. Red teaming deliberately tests an LLM app with adversarial or unusual inputs. Retrieval quality measures whether the right documents are found for a user question.

Question 16 LLM Application Design

Why does guardrail matter in LLM application design?

Conversation memory stores relevant user or task context across turns or sessions. Apply it to a small realistic example, explain the decision out loud, then check whether the result matches the intended outcome. It adds reliability around probabilistic model behavior. It helps apps with large stable context blocks.

Question 17 LLM Application Design

What question should you ask when using output parser?

Reranking reorders retrieved passages using a stronger relevance model so the most useful context appears first. Prompt versioning tracks changes to prompts the same way code changes are tracked. It prevents automation from making risky decisions alone. Ask: what problem is this solving, what trade-off does it create, and how will I know it worked?

Question 18 LLM Application Design

How can you practice fallback response?

PII handling defines how personal data is collected, masked, stored, and sent to models. Launching without logs that connect user problems to model traces. It keeps the product graceful under outages, rate limits, or uncertainty. Apply it to a small realistic example, explain the decision out loud, then check whether the result matches the intended outcome.

Question 19 LLM Application Design

What is a common mistake with prompt caching?

It reveals vulnerabilities before users or attackers find them. A guardrail is a rule, validation step, or workflow constraint that prevents unsafe or low-quality output. Grounding ties model output to provided sources, records, or tool results, reducing hallucinations and making answers easier to verify. Caching dynamic or user-specific data incorrectly.

Question 20 LLM Application Design

What does conversation memory mean in LLM application design?

Conversation memory stores relevant user or task context across turns or sessions. Red teaming deliberately tests an LLM app with adversarial or unusual inputs. A simple example is using fallback response in a small project, note, or decision before applying it to a larger real-world situation. It enables continuity without forcing users to repeat themselves.

Question 21 LLM Application Design

How can you practice conversation memory?

RAG retrieves relevant external documents and adds them to model context so answers can use fresh or private knowledge. Embedding search converts text into vectors and finds semantically similar passages even when exact keywords differ. It protects users and reduces compliance risk. Apply it to a small realistic example, explain the decision out loud, then check whether the result matches the intended outcome.

Question 22 LLM Application Design

What is retrieval-augmented generation (RAG)?

Prompt versioning tracks changes to prompts the same way code changes are tracked. RAG retrieves relevant external documents and adds them to model context so answers can use fresh or private knowledge. A simple example is using conversation memory in a small project, note, or decision before applying it to a larger real-world situation. A simple example is using model routing in a small project, note, or decision before applying it to a larger real-world situation.

Question 23 LLM Application Design

What does guardrail mean in LLM application design?

A simple example is using output parser in a small project, note, or decision before applying it to a larger real-world situation. A guardrail is a rule, validation step, or workflow constraint that prevents unsafe or low-quality output. A simple example is using red teaming in a small project, note, or decision before applying it to a larger real-world situation. A simple example is using retrieval quality in a small project, note, or decision before applying it to a larger real-world situation.

Question 24 LLM Application Design

What does source citation mean in LLM application design?

Red teaming deliberately tests an LLM app with adversarial or unusual inputs. It reveals vulnerabilities before users or attackers find them. Source citation links generated claims to supporting documents or records. It preserves useful continuity while controlling cost and latency.

Question 25 LLM Application Design

What is a common mistake with context compression?

Compressing away details needed for accurate decisions. A simple example is using context compression in a small project, note, or decision before applying it to a larger real-world situation. It prevents automation from making risky decisions alone. A simple example is using conversation memory in a small project, note, or decision before applying it to a larger real-world situation.

Question 26 LLM Application Design

What question should you ask when using prompt versioning?

It reveals vulnerabilities before users or attackers find them. Compressing away details needed for accurate decisions. Ask: what problem is this solving, what trade-off does it create, and how will I know it worked? Red teaming deliberately tests an LLM app with adversarial or unusual inputs.

Question 27 LLM Application Design

What does tool schema mean in LLM application design?

A tool schema describes the tool name, inputs, required fields, and allowed values. A simple example is using source citation in a small project, note, or decision before applying it to a larger real-world situation. It makes regressions easier to debug and improvements easier to reproduce. An LLM application combines a language model with product logic, data, tools, prompts, and user experience to solve a specific workflow.

Question 28 LLM Application Design

What is a common mistake with retrieval quality?

A simple example is using tool schema in a small project, note, or decision before applying it to a larger real-world situation. Blaming the model when the retrieved context was irrelevant. It lets users verify answers and increases trust. Source citation links generated claims to supporting documents or records.

Question 29 LLM Application Design

How can you practice output parser?

Apply it to a small realistic example, explain the decision out loud, then check whether the result matches the intended outcome. Embedding search converts text into vectors and finds semantically similar passages even when exact keywords differ. Poor retrieval leads to poor answers even when the model is strong. RAG retrieves relevant external documents and adds them to model context so answers can use fresh or private knowledge.

Question 30 LLM Application Design

What question should you ask when using retrieval quality?

A simple example is using human escalation in a small project, note, or decision before applying it to a larger real-world situation. A simple example is using pii handling in a small project, note, or decision before applying it to a larger real-world situation. A simple example is using prompt versioning in a small project, note, or decision before applying it to a larger real-world situation. Ask: what problem is this solving, what trade-off does it create, and how will I know it worked?

Question 31 LLM Application Design

What question should you ask when using prompt caching?

Ask: what problem is this solving, what trade-off does it create, and how will I know it worked? Structured output asks the model to return data in a predictable format such as JSON, making it easier to validate and consume. It makes regressions easier to debug and improvements easier to reproduce. Launching without logs that connect user problems to model traces.

Question 32 LLM Application Design

What question should you ask when using guardrail?

Ask: what problem is this solving, what trade-off does it create, and how will I know it worked? Red teaming deliberately tests an LLM app with adversarial or unusual inputs. Sending every task to the largest model by default. An LLM application combines a language model with product logic, data, tools, prompts, and user experience to solve a specific workflow.

Question 33 LLM Application Design

What is a simple example of red teaming?

Citing sources that were not actually used or do not support the claim. A simple example is using red teaming in a small project, note, or decision before applying it to a larger real-world situation. Launching without logs that connect user problems to model traces. Prompt injection is an attack where untrusted content tries to override instructions, reveal secrets, or force unintended actions.

Question 34 LLM Application Design

Why does retrieval quality matter in LLM application design?

Poor retrieval leads to poor answers even when the model is strong. Sending every task to the largest model by default. Blaming the model when the retrieved context was irrelevant. A simple example is using conversation memory in a small project, note, or decision before applying it to a larger real-world situation.

Question 35 LLM Application Design

What does PII handling mean in LLM application design?

It protects users and reduces compliance risk. PII handling defines how personal data is collected, masked, stored, and sent to models. Treating guardrails as a substitute for testing and monitoring. A simple example is using conversation memory in a small project, note, or decision before applying it to a larger real-world situation.

Question 36 LLM Application Design

Why does source citation matter in LLM application design?

A tool schema describes the tool name, inputs, required fields, and allowed values. Compressing away details needed for accurate decisions. Observability tracks requests, model choices, tools, latency, cost, and failures. It lets users verify answers and increases trust.

Question 37 LLM Application Design

What question should you ask when using fallback response?

Narrow permissions limit damage from mistakes or prompt injection by exposing only the actions and data needed for the task. An output parser validates and converts model text into structured application data. Ask: what problem is this solving, what trade-off does it create, and how will I know it worked? An evaluation set is a collection of representative prompts, expected behavior, and scoring rules used to test an LLM app.

Question 38 LLM Application Design

What is a common mistake with human escalation?

It reveals vulnerabilities before users or attackers find them. Treating guardrails as a substitute for testing and monitoring. Letting the model act autonomously in sensitive workflows. It lets users verify answers and increases trust.

Question 39 LLM Application Design

How can you practice context compression?

Apply it to a small realistic example, explain the decision out loud, then check whether the result matches the intended outcome. It protects users and reduces compliance risk. Citing sources that were not actually used or do not support the claim. It lets users verify answers and increases trust.

Question 40 LLM Application Design

What is a simple example of context compression?

A simple example is using context compression in a small project, note, or decision before applying it to a larger real-world situation. Reranking reorders retrieved passages using a stronger relevance model so the most useful context appears first. Clear schemas reduce malformed calls and unsafe ambiguity. Only testing happy-path prompts.

Question 41 LLM Application Design

What is a common mistake with red teaming?

It reveals vulnerabilities before users or attackers find them. Only testing happy-path prompts. A simple example is using prompt versioning in a small project, note, or decision before applying it to a larger real-world situation. It helps apps with large stable context blocks.

Question 42 LLM Application Design

What is tool calling?

It preserves useful continuity while controlling cost and latency. PII handling defines how personal data is collected, masked, stored, and sent to models. Structured output asks the model to return data in a predictable format such as JSON, making it easier to validate and consume. Tool calling lets an LLM request structured actions such as database queries, web searches, calculations, or file edits instead of only producing text.

Question 43 LLM Application Design

What does human escalation mean in LLM application design?

PII handling defines how personal data is collected, masked, stored, and sent to models. Human escalation routes uncertain or high-impact cases to a person. Blaming the model when the retrieved context was irrelevant. A tool schema describes the tool name, inputs, required fields, and allowed values.

Question 44 LLM Application Design

What is reranking?

Reranking reorders retrieved passages using a stronger relevance model so the most useful context appears first. It prevents automation from making risky decisions alone. Model routing sends tasks to different models based on cost, latency, difficulty, or capability. It protects users and reduces compliance risk.

Question 45 LLM Application Design

What question should you ask when using tool schema?

Ask: what problem is this solving, what trade-off does it create, and how will I know it worked? Showing raw errors to users without guidance. Tool calling lets an LLM request structured actions such as database queries, web searches, calculations, or file edits instead of only producing text. Structured output asks the model to return data in a predictable format such as JSON, making it easier to validate and consume.

Question 46 LLM Application Design

Why does human escalation matter in LLM application design?

A simple example is using model routing in a small project, note, or decision before applying it to a larger real-world situation. It makes production LLM behavior diagnosable. It prevents automation from making risky decisions alone. An evaluation set is a collection of representative prompts, expected behavior, and scoring rules used to test an LLM app.

Question 47 LLM Application Design

Why does red teaming matter in LLM application design?

It reveals vulnerabilities before users or attackers find them. Prompt caching reuses repeated context or instructions to reduce cost and latency. Observability tracks requests, model choices, tools, latency, cost, and failures. Narrow permissions limit damage from mistakes or prompt injection by exposing only the actions and data needed for the task.

Question 48 LLM Application Design

What is a common mistake with source citation?

Launching without logs that connect user problems to model traces. Reranking reorders retrieved passages using a stronger relevance model so the most useful context appears first. Prompt caching reuses repeated context or instructions to reduce cost and latency. Citing sources that were not actually used or do not support the claim.

Question 49 LLM Application Design

What does observability mean in LLM application design?

Observability tracks requests, model choices, tools, latency, cost, and failures. Sending sensitive data to tools or logs unnecessarily. A simple example is using prompt versioning in a small project, note, or decision before applying it to a larger real-world situation. Grounding ties model output to provided sources, records, or tool results, reducing hallucinations and making answers easier to verify.

Question 50 LLM Application Design

How can you practice prompt caching?

Prompt versioning tracks changes to prompts the same way code changes are tracked. Showing raw errors to users without guidance. Launching without logs that connect user problems to model traces. Apply it to a small realistic example, explain the decision out loud, then check whether the result matches the intended outcome.