CCAO-F Using the Claude API Practice Question
You are processing large documents with Claude. If the document exceeds the context window, which strategy is most effective for maintaining quality results?
⚠ Common exam trap
Candidates often suggest increasing the context window or simply summarizing the whole document, ignoring that RAG is the standard architectural pattern for handling data larger than the context limit.
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
✓
Use a RAG approach to retrieve and send only relevant chunks to the model.
When dealing with documents exceeding the context window, a RAG (Retrieval-Augmented Generation) approach is the standard solution. By segmenting the document into chunks and retrieving only the most relevant sections for a specific query, you ensure the model focuses on pertinent information. This avoids truncation issues while keeping the input within the model's limits, thereby maintaining the quality and relevance of the generated responses.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Increase the max_tokens to accommodate the entire document.
Why it's wrong here
Increasing max_tokens only affects the size of the generated output, not the input capacity. The context window is a hard limit on the total input and output tokens. Simply changing the parameter will not allow the model to process more text; it will still hit the context limit.
- ✓
Use a RAG approach to retrieve and send only relevant chunks to the model.
Why this is correct
RAG is the best practice for handling documents that are too large for the context window. By retrieving only the most relevant parts of the document, you stay well within token limits and provide the model with high-signal content, which improves accuracy and performance for large-scale analysis.
- ✗
Split the document into chunks and send them in parallel as separate API calls.
Why it's wrong here
Sending chunks in parallel creates separate, disconnected contexts. The model cannot synthesize information across chunks if they are not part of the same prompt. This approach results in fragmented, incomplete answers, as the model lacks visibility into the other parts of the document during each individual request.
- ✗
Request the model to summarize the document in sections using a loop.
Why it's wrong here
While looping is possible, it is prone to hallucination and loss of global context. It is inefficient and hard to manage compared to a retrieval-based architecture. RAG provides a more structured and reliable way to query large datasets, ensuring that the model maintains access to context when needed.
About these practice questions
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JA
Written and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official Anthropic exam blueprint
This CCAO-F practice question is part of Courseiva's free Anthropic certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the CCAO-F exam.