QUESTION: 53

A company's Machine Learning Specialist needs to improve the training speed of a time-series forecasting model using TensorFlow. The training is currently implemented on a single-GPU machine and takes approximately 23 hours to complete. The training needs to be run daily.

The model accuracy is acceptable, but the company anticipates a continuous increase in the size of the training data and a need to update the model on an hourly, rather than a daily, basis. The company also wants to minimize coding effort and infrastructure changes.

What should the Machine Learning Specialist do to the training solution to allow it to scale for future demand?

Do not change the TensorFlow code. Change the machine to one with a more powerful GPU to speed up the training.
Change the TensorFlow code to implement a Horovod distributed framework supported by Amazon SageMaker. Parallelize the training to as many machines as needed to achieve the business goals.
Switch to using a built-in AWS SageMaker DeepAR model. Parallelize the training to as many machines as needed to achieve the business goals.
Move the training to Amazon EMR and distribute the workload to as many machines as needed to achieve the business goals.

Answer(s): B

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QUESTION: 54

Which of the following metrics should a Machine Learning Specialist generally use to compare/evaluate machine learning classification models against each other?

Recall
Misclassification rate
Mean absolute percentage error (MAPE)
Area Under the ROC Curve (AUC)

Answer(s): D

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QUESTION: 55

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?

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 S3.
Generate daily precision-recall data in Amazon QuickSight, and publish the results in a dashboard shared with the Business team.
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.
Generate daily precision-recall data in Amazon ES, and publish the results in a dashboard shared with the Business team.

Answer(s): C

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QUESTION: 56

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?

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

Answer(s): C

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