A company is creating an agent for its application by using Amazon Bedrock Agents. The agent is performing well, but the company wants to improve the agent’s accuracy by providing some specific examples.Which solution meets these requirements?
Answer(s): A
Embedding specific input–output examples directly into the agent’s advanced prompt (few-shot prompting)guides the model toward more accurate behavior without retraining or additional tooling.
An AI practitioner is evaluating the performance of an Amazon SageMaker model. The AI practitioner must choose a performance metric. The metric must show the ratio of the number of correctly classified items to the total number of correctly and incorrectly classified items.Which metric meets these requirements?
Accuracy is defined as the number of correctly classified items (both true positives and true negatives) divided by the total number of items evaluated (the sum of true positives, true negatives, false positives, and false negatives), matching exactly the ratio described.
A financial company is building an AI assistant. The company wants to fine-tune a text-to-text foundation model(FM) to improve response tone.Which data format will meet these requirements?
Answer(s): B
Fine-tuning a text-to-text foundation model typically requires structured training examples that pair an input prompt with the desired output. A JSONL dataset containing prompt-completion pairs lets the model learn how responses should be phrased, including the desired tone and style.
A company has developed a neural network model to replace an existing decision tree model. The neural network model has a higher prediction accuracy compared to the decision tree model. However, the neural network model’s decision process is not as explainable as the decision tree model’s decision process.Which tradeoff is the company making by adopting the neural network model?
Answer(s): C
Neural networks typically provide higher predictive performance but are less transparent than decision trees,resulting in reduced interpretability of how decisions are made.
A company needs to collect a large dataset to train an AI assistant in a specific content area.Which dataset will meet this requirement?
To train an AI assistant for a specific content area, you need a large dataset of diverse conversations that incorporate the relevant terminology and context for that area. This enables the assistant to learn how to respond accurately and appropriately within the targeted subject.
Post your Comments and Discuss Amazon AIF-C01 exam prep with other Community members:
Our website is free, but we have to fight against AI bots and content theft. We're sorry for the inconvenience caused by these security measures. You can access the rest of the AIF-C01 content, but please register or login to continue.