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Generative AI and Foundation Models →mediumMultiple ChoiceObjective-mapped

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 developer is using Amazon Titan Text Express via the Bedrock InvokeModel API to summarize long documents. The documents are approximately 6,000 tokens each, but the model's context window is 4,000 tokens. What is the BEST approach to handle this?

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.

A
Truncate the document to 4,000 tokens by removing the middle portion
Why wrong: Truncating the document by removing the middle portion is not effective because it loses critical context and may produce an incomplete summary.
B
Use the Converse API instead, which supports longer context natively
Why wrong: The Converse API is designed for conversational interactions and does not inherently extend the context window for summarization tasks.
C
Split the document into chunks of 3,500 tokens, summarize each chunk, then combine the summaries
This is the best approach because it breaks the document into smaller chunks within the model's 4,000-token limit, summarizes each chunk, and then combines the summaries to produce a final summary. This technique preserves the overall content and is a standard method for handling documents that exceed the context window.
D
Switch to Amazon Titan Text Premier, which has a larger context window
Why wrong: Switching to Amazon Titan Text Premier is not the best approach because the developer is already using Titan Text Express, and the chunking strategy is a more direct and cost-effective solution. While Titan Text Premier has a 32,000-token context window, the question asks for the best approach given the existing model, and chunking works without changing the model.

Full breakdown with real-world context →

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 the document into chunks of 3,500 tokens, summarize each chunk, then combine the summaries

Option C is correct because it uses a chunking-and-summarization strategy to handle documents that exceed the model's context window. Amazon Titan Text Express has a 4,000-token context limit, so splitting the 6,000-token document into smaller chunks (e.g., 3,500 tokens each), summarizing each chunk independently via the InvokeModel API, and then combining those summaries produces a final summary without exceeding the model's token limit. Option D is incorrect because although Amazon Titan Text Premier has a 32,000-token context window, switching models is not the best approach given the developer is already using Titan Text Express; the chunking strategy is a more direct and cost-effective solution that works with the existing model.

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.

✗
Truncate the document to 4,000 tokens by removing the middle portion
Why it's wrong here
Truncating the document by removing the middle portion is not effective because it loses critical context and may produce an incomplete summary.
✗
Use the Converse API instead, which supports longer context natively
Why it's wrong here
The Converse API is designed for conversational interactions and does not inherently extend the context window for summarization tasks.
✓
Split the document into chunks of 3,500 tokens, summarize each chunk, then combine the summaries
Why this is correct
This is the best approach because it breaks the document into smaller chunks within the model's 4,000-token limit, summarizes each chunk, and then combines the summaries to produce a final summary. This technique preserves the overall content and is a standard method for handling documents that exceed the context window.
Clue confirmation
The clue word "best" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
✗
Switch to Amazon Titan Text Premier, which has a larger context window
Why it's wrong here
Switching to Amazon Titan Text Premier is not the best approach because the developer is already using Titan Text Express, and the chunking strategy is a more direct and cost-effective solution. While Titan Text Premier has a 32,000-token context window, the question asks for the best approach given the existing model, and chunking works without changing the model.

Common exam traps

Common exam trap: answer the scenario, not the keyword

The trap here is that candidates assume the Converse API or a different Titan model automatically extends the context window. While Amazon Titan Text Premier does have a larger 32,000-token context window, switching models is not the best approach because the developer is already using Titan Text Express, and chunking is a standard technique that works without changing the model. The Converse API does not change the underlying model's constraints.

Detailed technical explanation

How to think about this question

The chunking strategy leverages the model's ability to process each segment independently, but care must be taken to avoid splitting sentences or paragraphs mid-way, which can degrade summary quality. Overlapping chunks (e.g., 10-20% overlap) can help maintain coherence across boundaries. In practice, the combined summaries may still exceed the context window, requiring a second-level summarization pass, but for a single 6,000-token document, two chunks of 3,500 tokens each (with overlap) fit within the 4,000-token limit per call.

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 startup's cloud architect reviews their monthly bill and notices costs are higher than expected for a long-running batch job. Switching from on-demand instances to Reserved Instances — or using Spot/Preemptible VMs — can reduce compute costs by up to 72 %. Questions like this test whether you understand the tradeoffs between commitment, flexibility, and cost across cloud pricing models.

What to study next

Got this wrong? Here's your next step.

Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.

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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 the document into chunks of 3,500 tokens, summarize each chunk, then combine the summaries — Option C is correct because it uses a chunking-and-summarization strategy to handle documents that exceed the model's context window. Amazon Titan Text Express has a 4,000-token context limit, so splitting the 6,000-token document into smaller chunks (e.g., 3,500 tokens each), summarizing each chunk independently via the InvokeModel API, and then combining those summaries produces a final summary without exceeding the model's token limit. Option D is incorrect because although Amazon Titan Text Premier has a 32,000-token context window, switching models is not the best approach given the developer is already using Titan Text Express; the chunking strategy is a more direct and cost-effective solution that works with the existing model.

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.

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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