Task 2.3: Analyze Model Performance MLA-C01 Practice Questions (Page 6)

Questions 26–30

1. 26foundation · easy

   A binary classifier is evaluated on a test set. The confusion matrix shows: true positives = 40, true negatives = 30, false positives = 10, false negatives = 20. What is the total number of actual positive instances in the test set?

   • A70
   • B60
   • C50
   • D100
   Reveal Solution Discussion Select an answer first
2. 27foundation · easy

   To reduce training cost while maintaining acceptable model performance, what is a common strategy?

   • AIncrease the number of training epochs
   • BUse early stopping to halt training when validation performance stops improving
   • CUse a larger instance type
   • DUse a more complex model
   Reveal Solution Discussion Select an answer first
4. 28foundation · easy

   Which AWS service provides metrics such as class imbalance, label skew, and data type mismatch to analyze training data quality?

   • AAWS Glue DataBrew
   • BAmazon SageMaker Debugger
   • CAmazon SageMaker Clarify
   • DAmazon SageMaker Model Monitor
   Reveal Solution Discussion Select an answer first
5. 29foundation · easy

   Which SageMaker Clarify metric measures the difference in the proportion of positive labels between different groups in the training data?

   • ALabel skew
   • BClass imbalance
   • CFeature importance
   • DData type mismatch
   Reveal Solution Discussion Select an answer first
6. 30foundation · easy

   When choosing between a deep learning model and a linear model, what is a key tradeoff to consider?

   • ADeep learning models always have higher accuracy.
   • BLinear models are always faster to train.
   • CDeep learning models may offer better performance but require more data and computational resources.
   • DLinear models are more accurate on large datasets.
   Reveal Solution Discussion Select an answer first