- A
Fine-tune the model on the full documents without modification
Why wrong: Fine-tuning on full documents without modification is incorrect because the model's context window is limited to 8,192 tokens. Documents longer than this would exceed the limit and cause errors or require implicit truncation, which loses data.
- B
Convert documents into embeddings before fine-tuning
Why wrong: Embeddings are used for retrieval, not as input for fine-tuning the model itself.
- C
Split documents into chunks of fewer than 8,192 tokens with overlap
Chunking ensures each segment fits within the context window while preserving continuity through overlap.
- D
Truncate each document to 8,192 tokens
Why wrong: Truncation discards potentially important legal content; chunking with overlap is preferred.
AIF-C01 Generative AI and Foundation Models Practice Question
This AIF-C01 practice question tests your understanding of generative ai and foundation models. 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.
A data scientist is fine-tuning a large language model on a custom dataset of legal documents. The dataset contains 100,000 documents, each with an average length of 5,000 tokens. The model has a context window of 8,192 tokens. What is the MOST important consideration for preparing the data for fine-tuning?
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
Split documents into chunks of fewer than 8,192 tokens with overlap
Option C is correct because the model's context window is 8,192 tokens, but the average document length is 5,000 tokens, meaning many documents will exceed this limit. Splitting documents into chunks of fewer than 8,192 tokens with overlap ensures that no input exceeds the context window, preserves contextual continuity across chunk boundaries, and prevents loss of information that would occur with simple truncation.
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.
- ✗
Fine-tune the model on the full documents without modification
Why it's wrong here
Fine-tuning on full documents without modification is incorrect because the model's context window is limited to 8,192 tokens. Documents longer than this would exceed the limit and cause errors or require implicit truncation, which loses data.
- ✗
Convert documents into embeddings before fine-tuning
Why it's wrong here
Embeddings are used for retrieval, not as input for fine-tuning the model itself.
- ✓
Split documents into chunks of fewer than 8,192 tokens with overlap
Why this is correct
Chunking ensures each segment fits within the context window while preserving continuity through overlap.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Truncate each document to 8,192 tokens
Why it's wrong here
Truncation discards potentially important legal content; chunking with overlap is preferred.
Common exam traps
Common exam trap: answer the scenario, not the keyword
A common misconception is that truncation is acceptable for fine-tuning, but the trap here is that truncation discards data and breaks document coherence, whereas chunking with overlap is the correct approach to handle variable-length inputs within a fixed context window.
Detailed technical explanation
How to think about this question
When splitting documents, a common strategy is to use a sliding window approach with overlap (e.g., 256–512 tokens) to maintain context at chunk boundaries, which is critical for legal documents where references or clauses may span across splits. Under the hood, the tokenizer maps text to token IDs, and the model's attention mechanism is limited to the context window size; exceeding it causes out-of-memory errors or silent truncation in frameworks like Hugging Face Transformers. In practice, for a 5,000-token document, splitting into two chunks of ~2,500 tokens with 256-token overlap ensures each chunk fits within the 8,192 limit while preserving continuity.
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.
TExam Day Tips
- 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 cloud solutions architect for a retail company is evaluating services for a new workload. The correct answer here reflects best practice for the specific scenario described — not a general cloud recommendation. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Cloud exam questions reward reading the constraint carefully: the same technology can be right or wrong depending on the use case.
What to study next
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FAQ
Questions learners often ask
What does this AIF-C01 question test?
Generative AI and Foundation Models — This question tests Generative AI and Foundation Models — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Split documents into chunks of fewer than 8,192 tokens with overlap — Option C is correct because the model's context window is 8,192 tokens, but the average document length is 5,000 tokens, meaning many documents will exceed this limit. Splitting documents into chunks of fewer than 8,192 tokens with overlap ensures that no input exceeds the context window, preserves contextual continuity across chunk boundaries, and prevents loss of information that would occur with simple truncation.
What should I do if I get this AIF-C01 question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Last reviewed: Jul 4, 2026
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