A monitoring service generates 1 TB of scale metrics record data every minute. A Research team performs queries on this data using Amazon Athena. The queries run slowly due to the large volume of data, and the team requires better performance.
How should the records be stored in Amazon S3 to improve query performance?

• A.RecordIO
• B.Parquet files
• C.CSV files
• D.Compressed JSON

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A retail company intends to use machine learning to categorize new products A labeled dataset of current products was provided to the Data Science team The dataset includes 1 200 products The labeled dataset has
15 features for each product such as title dimensions, weight, and price Each product is labeled as belonging to one of six categories such as books, games, electronics, and movies.
Which model should be used for categorizing new products using the provided dataset for training?

• A.An XGBoost model where the objective parameter is set to multi: softmax
• B.A deep convolutional neural network (CNN) with a softmax activation function for the last layer
• C.A regression forest where the number of trees is set equal to the number of product categories
• D.A DeepAR forecasting model based on a recurrent neural network (RNN)

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A manufacturing company stores production volume data in a PostgreSQL database.
The company needs an end-to-end solution that will give business analysts the ability to prepare data for processing and to predict future production volume based the previous year's production volume. The solution must not require the company to have coding knowledge.
Which solution will meet these requirements with the LEAST effort?

• A.Use AWS Database Migration Service (AWS DMS) to transfer the data from the PostgreSQL database to an Amazon S3 bucket. Create an Amazon EMR cluster to read the S3 bucket and perform the data preparation. Use Amazon SageMaker Studio for the prediction modeling.
• B.Use AWS Glue DataBrew to read the data that is in the PostgreSQL database and to perform the data preparation. Use Amazon SageMaker Canvas for the prediction modeling.
• C.Use AWS Database Migration Service (AWS DMS) to transfer the data from the PostgreSQL database to an Amazon S3 bucket. Use AWS Glue to read the data in the S3 bucket and to perform the data preparation. Use Amazon SageMaker Canvas for the prediction modeling.
• D.Use AWS Glue DataBrew to read the data that is in the PostgreSQL database and to perform the data preparation. Use Amazon SageMaker Studio for the prediction modeling.

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A company has collected customer comments on its products, rating them as safe or unsafe, using decision trees. The training dataset has the following features: id, date, full review, full review summary, and a binary safe/unsafe tag. During training, any data sample with missing features was dropped. In a few instances, the test set was found to be missing the full review text field.
For this use case, which is the most effective course of action to address test data samples with missing features?

• A.Drop the test samples with missing full review text fields, and then run through the test set.
• B.Copy the summary text fields and use them to fill in the missing full review text fields, and then run through the test set.
• C.Use an algorithm that handles missing data better than decision trees.
• D.Generate synthetic data to fill in the fields that are missing data, and then run through the test set.

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A data scientist has developed a machine learning translation model for English to Japanese by using Amazon SageMaker's built-in seq2seq algorithm with 500,000 aligned sentence pairs. While testing with sample sentences, the data scientist finds that the translation quality is reasonable for an example as short as five words. However, the quality becomes unacceptable if the sentence is 100 words long.
Which action will resolve the problem?

• A.Add more nodes to the recurrent neural network (RNN) than the largest sentence's word count.
• B.Choose a different weight initialization type.
• C.Change preprocessing to use n-grams.
• D.Adjust hyperparameters related to the attention mechanism.

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