CCAR-F Context and Reliability Practice Question
Exhibit
{
"role": "user",
"content": "I have a 500-page book. Summarize it in 3 sentences."
}Refer to the exhibit. Why might the model struggle with this request?
⚠ Common exam trap
Test-takers often assume models possess complete internal knowledge of specific books or manuals, neglecting the necessity of providing explicit grounding text.
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
✓
The prompt does not provide the source text for the summary.
The prompt lacks sufficient grounding. Asking the model to summarize a 500-page book without providing the content relies on the model's internal memory of the book, which may be incomplete or hallucinated. To achieve reliability, you must provide the source text (e.g., via RAG) or, if the book is well-known, provide high-quality reference material within the context to ensure the summary is based on verifiable facts, not stochastic recall.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
The request is too simple for the model.
Why it's wrong here
The problem is not that the task is simple; it is that it lacks the necessary information to perform the task reliably. The model is being asked to summarize something it does not have in its context window. This is a fundamental limitation of providing context-less queries for large-scale external knowledge.
- ✓
The prompt does not provide the source text for the summary.
Why this is correct
Reliable summarization requires access to the source content. Expecting the model to summarize a book from its training weights is prone to hallucination and factual inaccuracies. Providing the text directly is the best way to ensure the model produces a grounded, reliable summary that matches the actual document content.
- ✗
The model cannot handle 500 pages of text.
Why it's wrong here
Modern LLMs like Claude have large context windows and can easily handle the token equivalent of 500 pages. The issue is not the capacity of the model to ingest the text, but the fact that the text was not provided in the prompt to begin with. The model is being asked to summarize from memory.
- ✗
The request for 3 sentences is too restrictive.
Why it's wrong here
Length constraints are generally handled well by the model. If the model struggled, it would not be because it was asked to provide only 3 sentences. The primary bottleneck is the lack of source material, which makes accurate summarization impossible regardless of the target output length requested by the user.
About these practice questions
Courseiva writes every CCAR-F question from scratch — 271 in total, each with an explanation and a wrong-answer breakdown. None are copied from real exams or dumps. Learn why practice questions differ from exam dumps →
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 CCAR-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 CCAR-F exam.