QUESTION: 85

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?

The true class frequency for Romance is 77.56% and the predicted class frequency for Adventure is 20.85%
The true class frequency for Romance is 57.92% and the predicted class frequency for Adventure is 13.12%
The true class frequency for Romance is 0.78 and the predicted class frequency for Adventure is (0.47- 0.32)
The true class frequency for Romance is 77.56% * 0.78 and the predicted class frequency for Adventure is 20.85%*0.32

Answer(s): B

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A Machine Learning Specialist wants to bring a custom algorithm to Amazon SageMaker. The Specialist implements the algorithm in a Docker container supported by Amazon SageMaker.

How should the Specialist package the Docker container so that Amazon SageMaker can launch the training correctly?

Modify the bash_profile file in the container and add a bash command to start the training program
Use CMD config in the Dockerfile to add the training program as a CMD of the image
Configure the training program as an ENTRYPOINT named train
Copy the training program to directory /opt/ml/train

Answer(s): C

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QUESTION: 87

A Data Scientist needs to analyze employment data. The dataset contains approximately 10 million observations on people across 10 different features. During the preliminary analysis, the Data Scientist notices that income and age distributions are not normal. While income levels shows a right skew as expected, with fewer individuals having a higher income, the age distribution also show a right skew, with fewer older individuals participating in the workforce.

Which feature transformations can the Data Scientist apply to fix the incorrectly skewed data? (Choose two.)

Cross-validation
Numerical value binning
High-degree polynomial transformation
Logarithmic transformation
One hot encoding

Answer(s): B,D

Reference:

https://towardsdatascience.com/understanding-feature-engineering-part-1-continuous-numeric-data-da4e47099a7b

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QUESTION: 88

A web-based company wants to improve its conversion rate on its landing page. Using a large historical dataset of customer visits, the company has repeatedly trained a multi-class deep learning network algorithm on Amazon SageMaker. However, there is an overfitting problem: training data shows 90% accuracy in predictions, while test data shows 70% accuracy only.

The company needs to boost the generalization of its model before deploying it into production to maximize conversions of visits to purchases.

Which action is recommended to provide the HIGHEST accuracy model for the company’s test and validation data?

Increase the randomization of training data in the mini-batches used in training
Allocate a higher proportion of the overall data to the training dataset
Apply L1 or L2 regularization and dropouts to the training
Reduce the number of layers and units (or neurons) from the deep learning network

Answer(s): C

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Free MLS-C01 Exam Braindumps (page: 22)

QUESTION: 85

QUESTION: 86

QUESTION: 87

Reference:

QUESTION: 88