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NEW QUESTION 24
A health care company is planning to use neural networks to classify their X-ray images into normal and abnormal classes. The labeled data is divided into a training set of 1,000 images and a test set of 200 images.
The initial training of a neural network model with 50 hidden layers yielded 99% accuracy on the training set, but only 55% accuracy on the test set.
What changes should the Specialist consider to solve this issue? (Choose three.)

A. Enable early stoppingB. Include all the images from the test set in the training setC. Choose a higher number of layersD. Enable dropoutE. Choose a lower number of layersF. Choose a smaller learning rate

Answer: B,C,D

 

NEW QUESTION 25
A company is running a machine learning prediction service that generates 100 TB of predictions every day A Machine Learning Specialist must generate a visualization of the daily precision-recall curve from the predictions, and forward a read-only version to the Business team.
Which solution requires the LEAST coding effort?

A. Run a daily Amazon EMR workflow to generate precision-recall data, and save the results in Amazon S3 Give the Business team read-only access to S3B. Generate daily precision-recall data in Amazon ES, and publish the results in a dashboard shared with the Business team.C. Generate daily precision-recall data in Amazon QuickSight, and publish the results in a dashboard shared with the Business teamD. Run a daily Amazon EMR workflow to generate precision-recall data, and save the results in Amazon S3 Visualize the arrays in Amazon QuickSight, and publish them in a dashboard shared with the Business team

Answer: C

 

NEW QUESTION 26
A Machine Learning Specialist is preparing data for training on Amazon SageMaker. The Specialist is using one of the SageMaker built-in algorithms for the training. The dataset is stored in .CSV format and is transformed into a numpy.array, which appears to be negatively affecting the speed of the training.
What should the Specialist do to optimize the data for training on SageMaker?

A. Transform the dataset into the RecordIO protobuf format.B. Use the SageMaker batch transform feature to transform the training data into a DataFrame.C. Use AWS Glue to compress the data into the Apache Parquet format.D. Use the SageMaker hyperparameter optimization feature to automatically optimize the data.

Answer: A

 

NEW QUESTION 27
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