Instant Access Amazon.AWS-Certified-Machine-Learning-Specialty.v2024-06-17.q246 Actual Practice Test Engine for Free (Page 37)

Question 176

A Machine Learning Specialist has created a deep learning neural network model that performs well on the training data but performs poorly on the test data.
Which of the following methods should the Specialist consider using to correct this? (Select THREE.)

A.Decrease regularization.
B.Decrease feature combinations.
C.Decrease dropout.
D.Increase dropout.
E.Increase regularization.
F.Increase feature combinations.

Question 177

A manufacturing company asks its Machine Learning Specialist to develop a model that classifies defective parts into one of eight defect types. The company has provided roughly 100000 images per defect type for training During the injial training of the image classification model the Specialist notices that the validation accuracy is 80%, while the training accuracy is 90% It is known that human-level performance for this type of image classification is around 90% What should the Specialist consider to fix this issue1?

A.A longer training time
B.Making the network larger
C.Using a different optimizer
D.Using some form of regularization

Correct Answer: D

Explanation
Regularization is a technique that can be used to prevent overfitting and improve model performance on unseen data. Overfitting occurs when the model learns the training data too well and fails to generalize to new and unseen data. This can be seen in the question, where the validation accuracy is lower than the training accuracy, and both are lower than the human-level performance. Regularization is a way of adding some constraints or penalties to the model to reduce its complexity and prevent it from memorizing the training data.
Some common forms of regularization for image classification are:
Weight decay: Adding a term to the loss function that penalizes large weights in the model. This can help reduce the variance and noise in the model and make it more robust to small changes in the input.
Dropout: Randomly dropping out some units or connections in the model during training. This can help reduce the co-dependency among the units and make the model more resilient to missing or corrupted features.
Data augmentation: Artificially increasing the size and diversity of the training data by applying random transformations, such as cropping, flipping, rotating, scaling, etc. This can help the model learn more invariant and generalizable features and reduce the risk of overfitting to specific patterns in the training data.
The other options are not likely to fix the issue of overfitting, and may even worsen it:
A longer training time: This can lead to more overfitting, as the model will have more chances to fit the noise and details in the training data that are not relevant for the validation data.
Making the network larger: This can increase the model capacity and complexity, which can also lead to more overfitting, as the model will have more parameters to learn and adjust to the training data.
Using a different optimizer: This can affect the speed and stability of the training process, but not necessarily the generalization ability of the model. The choice of optimizer depends on the characteristics of the data and the model, and there is no guarantee that a different optimizer will prevent overfitting.
References:
Regularization (machine learning)
Image Classification: Regularization
How to Reduce Overfitting With Dropout Regularization in Keras

Question 178

An e-commerce company needs a customized training model to classify images of its shirts and pants products The company needs a proof of concept in 2 to 3 days with good accuracy Which compute choice should the Machine Learning Specialist select to train and achieve good accuracy on the model quickly?

A.m5 4xlarge (general purpose)
B.r5.2xlarge (memory optimized)
C.p3.2xlarge (GPU accelerated computing)
D.p3 8xlarge (GPU accelerated computing)

Question 179

An agency collects census information within a country to determine healthcare and social program needs by province and city. The census form collects responses for approximately 500 questions from each citizen.
Which combination of algorithms would provide the appropriate insights? (Select TWO.)

A.The factorization machines (FM) algorithm
B.The Latent Dirichlet Allocation (LDA) algorithm
C.The principal component analysis (PCA) algorithm
D.The k-means algorithm
E.The Random Cut Forest (RCF) algorithm

Question 180

A large mobile network operating company is building a machine learning model to predict customers who are likely to unsubscribe from the service. The company plans to offer an incentive for these customers as the cost of churn is far greater than the cost of the incentive.
The model produces the following confusion matrix after evaluating on a test dataset of 100 customers:
Based on the model evaluation results, why is this a viable model for production?

A.The precision of the model is 86%, which is less than the accuracy of the model.
B.The model is 86% accurate and the cost incurred by the company as a result of false negatives is less than the false positives.
C.The precision of the model is 86%, which is greater than the accuracy of the model.
D.The model is 86% accurate and the cost incurred by the company as a result of false positives is less than the false negatives.

Question 176

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