AWS Certified Machine Learning Engineer – Associate (MLA-C01) — Question 86

A manufacturing company uses an ML model to determine whether products meet a standard for quality. The model produces an output of "Passed" or "Failed." Robots separate the products into the two categories by using the model to analyze photos on the assembly line.

Which metrics should the company use to evaluate the model's performance? (Choose two.)

Answer options

• A. Precision and recall
• B. Root mean square error (RMSE) and mean absolute percentage error (MAPE)
• C. Accuracy and F1 score
• D. Bilingual Evaluation Understudy (BLEU) score
• E. Perplexity

Correct answer: A, C

Explanation

Precision and recall are crucial for this binary classification task as they measure the model's ability to correctly identify passed and failed products. Accuracy and F1 score are also relevant as they provide insights into the overall performance and balance between precision and recall. The other options, such as RMSE, MAPE, BLEU score, and perplexity, are not suitable for evaluating classification models.