A financial company is training a generative AI model to predict outcomes of loan applications. The training dataset is small. The dataset categorizes loan applicants as “younger-aged,” “middle-aged,” or “older-aged.”Most individuals in the dataset are characterized as "middle-aged."The company removes the age range feature from the training dataset.Which model behavior will likely happen as a result of this change to the dataset?
Answer(s): A
Removing the age feature eliminates an important variable that differentiates applicant groups. Since the dataset is already imbalanced, the model will generalize based on dominant patterns and lose the ability to account for age-related differences, leading to poorer predictions especially for underrepresented younger and older groups.
HOTSPOT (Drag and Drop is not supported)An AI practitioner is using an Amazon Nova text model. The AI practitioner wants to apply prompt engineering techniques to ensure that prompts support an iterative refinement process.Select the correct prompt design component from the following list for each definition. Select each prompt design component one time.Hot Area:
Specify the use case for the model — TaskDefine the persona the model should assume to meet the requirements effectively — RoleDescribe the tone, format, or structure that the model should follow — Response styleSet clear metrics to evaluate whether the model output meets expectations — Success criteriaTask defines what the model is being asked to do. Role sets the persona or perspective the model should adopt. Response style determines the tone, format, and structure of the output. Success criteria establish how to judge whether the response meets the intended requirements.
A financial company is developing a generative AI application for loan approval decisions. The company needs the application output to be responsible and fair.Which solution meets these requirements?
Reviewing the training data for biases and ensuring representation from all demographics is essential for developing a responsible and fair generative AI application, especially in sensitive domains like loan approval decisions. This approach helps reduce bias and supports ethical AI practices.
HOTSPOT (Drag and Drop is not supported)A company wants to use ML to increase customer engagement and sales. The company has collected a large dataset that includes customer demographics, purchase history, browsing patterns, and product ratings.Select the correct ML approach from the following list for each use case. Select each ML approach one time.Hot Area:
Customer lifetime value is a continuous numeric prediction representing expected revenue over time.The likelihood that a customer will stop using services is a binary outcome prediction.Grouping customers by similar behaviors and preferences requires identifying natural patterns without predefined labels.
A company is analyzing financial transaction records. The company categorizes the records as either personal or business. The company inserts the categories into the transaction records.Which data preparation step does this describe?
Answer(s): B
Assigning categories such as "personal" or "business" to financial transaction records is an example of data labeling, where each data point is tagged with a specific label for use in machine learning or data analysis tasks.
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