Examtopics

AWS Certified Machine Learning - Specialty

  • Topic 1 Question 261

    A social media company wants to develop a machine learning (ML) model to detect inappropriate or offensive content in images. The company has collected a large dataset of labeled images and plans to use the built-in Amazon SageMaker image classification algorithm to train the model. The company also intends to use SageMaker pipe mode to speed up the training.

    The company splits the dataset into training, validation, and testing datasets. The company stores the training and validation images in folders that are named Training and Validation, respectively. The folders contain subfolders that correspond to the names of the dataset classes. The company resizes the images to the same size and generates two input manifest files named training.lst and validation.lst, for the training dataset and the validation dataset, respectively. Finally, the company creates two separate Amazon S3 buckets for uploads of the training dataset and the validation dataset.

    Which additional data preparation steps should the company take before uploading the files to Amazon S3?

    • Generate two Apache Parquet files, training.parquet and validation.parquet, by reading the images into a Pandas data frame and storing the data frame as a Parquet file. Upload the Parquet files to the training S3 bucket.

    • Compress the training and validation directories by using the Snappy compression library. Upload the manifest and compressed files to the training S3 bucket.

    • Compress the training and validation directories by using the gzip compression library. Upload the manifest and compressed files to the training S3 bucket.

    • Generate two RecordIO files, training.rec and validation.rec, from the manifest files by using the im2rec Apache MXNet utility tool. Upload the RecordIO files to the training S3 bucket.

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