AIF-C01 practice question: Generative AI concepts (3 of 3)
Fundamentals of Generative AI A free question from our original AIF-C01 practice exam. Try it, then open the answer to see why each option is right or wrong.
An insurer's claims assistant gives vague answers.
The instructions are already clear, but the model never sees the customer's policy wording, the claim history, or the results of a claim-lookup tool. The team builds a layer that selects these items and assembles them into the model's input for each request, within a fixed token budget.
What is this practice called?
Choose one answer.
AA. Pre-training, because the model learns the policies from scratch on each live request.
BB. Hyperparameter tuning, because it adjusts the model's settings to the insurer's data.
CC. Prompt caching, because it stores earlier responses and reuses them for the same prompt prefix.
DD. Context engineering, because it decides which information the model sees in each request.
Show the answer and explanation
Answer: D
A. Wrong: Pre-training is the large initial training run on broad data. It happens once, not for each request, and it does not feed live policy text into a request.
B. Wrong: Hyperparameter tuning changes training settings to improve a model's fit. It does not choose which documents or tool results appear in each request.
C. Wrong: Caching speeds up and lowers the cost of repeated prompt prefixes. It does not decide which new information the model is shown.
D. Right: Correct. Context engineering selects and arranges the policy text, conversation history and tool results that fill the model's input. It works within the token budget the model allows. Learn more: Amazon Bedrock Knowledge Bases (opens in a new tab)
Explanation
A model answers from what it is shown in its input, so the quality of an answer depends on the context it receives. Context engineering designs what goes into that input, such as retrieved documents, history and tool results, within a token limit. Pre-training, tuning and caching address other parts of the system. This question tests recognizing the practice that shapes the model's input.
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