A company has developed a generative text summarization model by using Amazon Bedrock. The company will use Amazon Bedrock automatic model evaluation capabilities.Which metric should the company use to evaluate the accuracy of the model?
Answer(s): C
BERTScore is a metric specifically designed for evaluating the quality of text generated by models, particularly in tasks like text summarization and machine translation. It uses contextual embeddings from a pre-trainedBERT model to compare generated text with reference text at the word level, making it well-suited for evaluating the accuracy of generative text summarization models.BERTScore assesses the semantic similarity between the generated text and the reference text, providing a more nuanced evaluation compared to traditional methods that focus on exact matches.
A financial company uses a generative AI model to assign credit limits to new customers. The company wants to make the decision-making process of the model more transparent to its customers.Which solution meets these requirements?
Answer(s): B
Explainable AI techniques provide transparency by identifying and displaying the specific factors that influenced the generative AI model’s credit limit decisions, making the decision-making process understandable to customers.
A company wants to develop an interpretable ML model to assess the risk of loan applications.Which type of ML model or algorithm will meet these requirements?
Logistic regression is a simple, interpretable ML model that provides clear relationships between input features and predicted outcomes. It is widely used for risk assessment tasks like loan applications, where transparency and explainability are critical.
An AI practitioner wants to predict the classification of flowers based on petal length, petal width, sepal length,and sepal width.Which algorithm meets these requirements?
Answer(s): A
K-nearest neighbors (k-NN) is a supervised learning algorithm used for classification tasks. It works by classifying a data point based on how its features (such as petal length, petal width, sepal length, and sepal width in this case) are similar to the data points in the training set. For this problem, where the task is to classify flowers based on their features, k-NN is an appropriate choice.k-NN is simple and effective for classification problems where the decision boundary is not necessarily linear,as it looks at the closest neighbors to make predictions.
A bank uses an AI model to evaluate customer loan applications. After model deployment, the bank observes that customers who have very little credit history are flagged as high risk. These customers are flagged regardless of their income level.Which training data characteristic caused this model bias?
If the training data contains only customers with long credit histories, the model does not learn representative patterns for customers with limited credit history. As a result, it can generalize poorly for those customers and systematically classify them as high risk, creating bias caused by unrepresentative training data.
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