Update GCP-ML-vA.json

questions/GCP-ML-vA.json

@@ -27,10 +27,12 @@
         "question": "You were asked to investigate failures of a product ion line component based on sensor readings. After receiving the dataset, you discover that less than 1% of the readings are positive examples representi ng failure incidents. You have tried to train several classifi cation models, but none of them converge. How shoul d you resolve the class imbalance problem?",
         "options": [
             "A. Use the class distribution to generate 10% positi ve examples.",
-            "B. Use a convolutional neural network with max pooling  and softmax activation. C. Downsample the data with upweighting to create a sa mple with 10% positive examples.",
+            "B. Use a convolutional neural network with max pooling  and softmax activation" ,
+            "C. Downsample the data with upweighting to create a sa mple with 10% positive examples.",
             "D. Remove negative examples until the numbers of pos itive and negative examples are equal."
         ],
-        "correct": "",
+        "correct": "C. Downsample the data with upweighting to create a sa mple with 10% positive examples.",
+        
         "explanation": "C. Downsample the data with upweighting to create a sample with 10% positive examples.\n\nExplanation:\n\nThe correct answer is C, Downsample the data with upweighting to create a sample with 10% positive examples. This approach involves reducing the number of negative examples while assigning a higher weight to the remaining negative examples. This helps to balance the class distribution and allows the model to converge.\n\nOption A is incorrect because generating positive examples artificially can lead to overfitting and poor generalization of the model.\n\nOption B is incorrect because convolutional neural networks with max pooling and softmax activation are typically used for image classification tasks, not for handling class imbalance problems.\n\nOption D is incorrect because removing negative examples can result in loss of valuable information and may not effectively address the class imbalance issue.\n\nIn summary, downsampling the data with upweighting is a suitable approach to handle class imbalance problems, especially when the number of positive examples is very small compared to the number of negative examples.",
         "references": ""
     },