MLS-C01 Exam: Absolutely Free Online Practice

Question 1
- A Machine Learning Specialist built an image classification deep learning model. However the Specialist ran into an overfitting problem in which the training and testing accuracies were 99% and 75%r respectively.
  How should the Specialist address this issue and what is the reason behind it?
  
  A : The learning rate should be increased because the optimization process was trapped at a local minimum.
  
  B : The dropout rate at the flatten layer should be increased because the model is not generalized enough.
  
  C : The dimensionality of dense layer next to the flatten layer should be increased because the model is not complex enough.
  
  D : The epoch number should be increased because the optimization process was terminated before it reached the global minimum.
  
  Answer: B
Question 2
- Given the following confusion matrix for a movie classification model, what is the true class frequency for Romance and the predicted class frequency for Adventure?
  
  A : The true class frequency for Romance is 77.56% and the predicted class frequency for Adventure is 20.85%
  
  B : The true class frequency for Romance is 57.92% and the predicted class frequency for Adventure is 13.12%
  
  C : The true class frequency for Romance is 0.78 and the predicted class frequency for Adventure is (0.47- 0.32)
  
  D : The true class frequency for Romance is 77.56% × 0.78 and the predicted class frequency for Adventure is 20.85% × 0.32
  
  Answer: B
Question 3
- The displayed graph is from a foresting model for testing a time series.
  
  Considering the graph only, which conclusion should a Machine Learning Specialist make about the behavior of the model?
  
  A : The model predicts both the trend and the seasonality well.
  
  B : The model predicts the trend well, but not the seasonality.
  
  C : The model predicts the seasonality well, but not the trend.
  
  D : The model does not predict the trend or the seasonality well.
  
  Answer: D
Question 4
- An automotive company uses computer vision in its autonomous cars. The company trained its object detection models successfully by using transfer learning from a convolutional neural network (CNN). The company trained the models by using PyTorch through the Amazon SageMaker SDK. The vehicles have limited hardware and compute power. The company wants to optimize the model to reduce memory, battery, and hardware consumption without a significant sacrifice in accuracy. Which solution will improve the computational efficiency of the models?
  
  A :
  Use Amazon CloudWatch metrics to gain visibility into the SageMaker training weights, gradients, biases, and activation outputs. Compute the filter ranks based on the training information. Apply pruning to remove the low-ranking filters. Set new weights based on the pruned set of filters. Run a new training job with the pruned model.
  
  B :
  Use Amazon SageMaker Ground Truth to build and run data labeling workflows. Collect a larger labeled dataset with the labelling workflows. Run a new training job that uses the new labeled data with previous training data.
  
  C :
  Use Amazon SageMaker Debugger to gain visibility into the training weights, gradients, biases, and activation outputs. Compute the filter ranks based on the training information. Apply pruning to remove the low-ranking filters. Set the new weights based on the pruned set of filters. Run a new training job with the pruned model.
  
  D :
  Use Amazon SageMaker Model Monitor to gain visibility into the ModelLatency metric and OverheadLatency metric of the model after the company deploys the model. Increase the model learning rate. Run a new training job.
  
  Answer: C
Question 5
- A chemical company has developed several machine learning (ML) solutions to identify chemical process abnormalities. The time series values of independent variables and the labels are available for the past 2 years and are sufficient to accurately model the problem.
  The regular operation label is marked as 0 The abnormal operation label is marked as 1. Process abnormalities have a significant negative effect on the company’s profits. The company must avoid these abnormalities.
  Which metrics will indicate an ML solution that will provide the GREATEST probability of detecting an abnormality?
  A : Precision = 0.91 -
  Recall = 0.6
  
  B : Precision = 0.61 -
  Recall = 0.98
  
  C : Precision = 0.7 -
  Recall = 0.9
  
  D : Precision = 0.98 -
  Recall = 0.8
  
  Answer: B

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