The correct action is to reduce the number of retrieved documents. This resolves the context length exceeded error because the total token count from the retrieved chunks, when combined with the system prompt and user query, surpasses the model’s maximum context window—a hard limit in large language models. In a Retrieval-Augmented Generation (RAG) pipeline, each retrieved document adds tokens, and exceeding this limit truncates input or causes failure. On the Oracle Cloud Infrastructure Generative AI Professional 1Z0-1127 exam, this question tests your understanding of RAG optimization and token budgeting, often appearing as a scenario where the model returns an error despite correct retrieval logic. A common trap is to increase chunk size or model capacity, but the most direct fix is reducing the number of documents retrieved. Memory tip: think of the context window as a suitcase—you can’t fit everything, so pack only the most relevant items.
1Z0-1127 Practice Question: Building LLM Applications with RAG and Vector Search
This 1Z0-1127 practice question tests your understanding of building llm applications with rag and vector search. 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.
Exhibit
Error: The total token count (4082) exceeds the model's maximum context length (4096). The input includes 512 tokens for system prompt, 3072 tokens for retrieved documents, and 498 tokens for the user query.
Refer to the exhibit. What is the best action to resolve this error?
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.
Error: The total token count (4082) exceeds the model's maximum context length (4096). The input includes 512 tokens for system prompt, 3072 tokens for retrieved documents, and 498 tokens for the user query.
A
Decrease the temperature of the generation model
Why wrong: Temperature affects output randomness, not input token count.
B
Increase the max_tokens parameter for generation
Why wrong: Increasing max_tokens would not reduce the input token count; it might even increase the total if the model generates a longer output.
C
Reduce the number of retrieved documents
Reducing retrieved documents directly decreases the token count from that segment, bringing total under the limit.
D
Use a smaller chunk size for documents
Why wrong: Smaller chunks alone may not reduce total tokens if the number of chunks remains the same.
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Reduce the number of retrieved documents
The input exceeds the model's context length due to a high number of retrieved document tokens. Reducing the number of documents retrieved (or their size) is the most direct fix.
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.
✗
Decrease the temperature of the generation model
Why it's wrong here
Temperature affects output randomness, not input token count.
✗
Increase the max_tokens parameter for generation
Why it's wrong here
Increasing max_tokens would not reduce the input token count; it might even increase the total if the model generates a longer output.
✓
Reduce the number of retrieved documents
Why this is correct
Reducing retrieved documents directly decreases the token count from that segment, bringing total under the limit.
Clue confirmation
The clue word "best" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
✗
Use a smaller chunk size for documents
Why it's wrong here
Smaller chunks alone may not reduce total tokens if the number of chunks remains the same.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Many certification questions include familiar terms but test a specific constraint. Read the exact wording before choosing an answer that is generally true but wrong for this case.
Trap categories for this question
Command / output trap
Temperature affects output randomness, not input token count.
Detailed technical explanation
How to think about this question
This question should be treated as a scenario, not a definition check. Identify the problem, the constraint and the best action. Then compare each option against those facts.
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.
Use explanations to understand the rule behind the answer.
TExam Day Tips
→Underline the problem statement mentally.
→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 practitioner preparing for the 1Z0-1127 exam encounters this exact type of scenario on the job. The correct answer here is not the most general option — it is the best answer for the specific constraint described. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Real exam questions reward reading the full scenario before eliminating options, because the constraint defines which answer fits.
What to study next
Got this wrong? Here's your next step.
Identify which 1Z0-1127 exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.
Building LLM Applications with RAG and Vector Search — This question tests Building LLM Applications with RAG and Vector Search — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Reduce the number of retrieved documents — The input exceeds the model's context length due to a high number of retrieved document tokens. Reducing the number of documents retrieved (or their size) is the most direct fix.
What should I do if I get this 1Z0-1127 question wrong?
Identify which 1Z0-1127 exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.
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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