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NEW QUESTION 33
A Machine Learning Specialist working for an online fashion company wants to build a data ingestion solution for the company's Amazon S3-based data lake.
The Specialist wants to create a set of ingestion mechanisms that will enable future capabilities comprised of:
- Real-time analytics
- Interactive analytics of historical data
- Clickstream analytics
- Product recommendations
Which services should the Specialist use?

A. AWS Glue as the data catalog; Amazon Kinesis Data Streams and Amazon Kinesis Data Analytics for historical data insights; Amazon Kinesis Data Firehose for delivery to Amazon ES for clickstream analytics; Amazon EMR to generate personalized product recommendationsB. Amazon Athena as the data catalog: Amazon Kinesis Data Streams and Amazon Kinesis Data Analytics for near-real-time data insights; Amazon Kinesis Data Firehose for clickstream analytics; AWS Glue to generate personalized product recommendationsC. Amazon Athena as the data catalog; Amazon Kinesis Data Streams and Amazon Kinesis Data Analytics for historical data insights; Amazon DynamoDB streams for clickstream analytics; AWS Glue to generate personalized product recommendationsD. AWS Glue as the data catalog; Amazon Kinesis Data Streams and Amazon Kinesis Data Analytics for real-time data insights; Amazon Kinesis Data Firehose for delivery to Amazon ES for clickstream analytics; Amazon EMR to generate personalized product recommendations

Answer: D

 

NEW QUESTION 34
A Machine Learning Specialist is building a model to predict future employment rates based on a wide range of economic factors While exploring the data, the Specialist notices that the magnitude of the input features vary greatly The Specialist does not want variables with a larger magnitude to dominate the model What should the Specialist do to prepare the data for model training'?

A. Apply quantile binning to group the data into categorical bins to keep any relationships in the data by replacing the magnitude with distributionB. Apply the Cartesian product transformation to create new combinations of fields that are independent of the magnitudeC. Apply normalization to ensure each field will have a mean of 0 and a variance of 1 to remove any significant magnitudeD. Apply the orthogonal sparse Diagram (OSB) transformation to apply a fixed-size sliding window to generate new features of a similar magnitude.

Answer: C

 

NEW QUESTION 35
A machine learning (ML) specialist needs to extract embedding vectors from a text series. The goal is to provide a ready-to-ingest feature space for a data scientist to develop downstream ML predictive models. The text consists of curated sentences in English. Many sentences use similar words but in different contexts. There are questions and answers among the sentences, and the embedding space must differentiate between them.
Which options can produce the required embedding vectors that capture word context and sequential QA information? (Choose two.)

A. Amazon SageMaker BlazingText algorithm in continuous bag-of-words (CBOW) modeB. Amazon SageMaker BlazingText algorithm in Skip-gram modeC. Amazon SageMaker seq2seq algorithmD. Amazon SageMaker Object2Vec algorithmE. Combination of the Amazon SageMaker BlazingText algorithm in Batch Skip-gram mode with a custom recurrent neural network (RNN)

Answer: C,D

Explanation:
Reference:
https://docs.aws.amazon.com/sagemaker/latest/dg/object2vec.html

 

NEW QUESTION 36
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. Use the SageMaker hyperparameter optimization feature to automatically optimize the data.B. Transform the dataset into the RecordIO protobufformat.C. Use the SageMaker batch transform feature to transform the training data into a DataFrame.D. Use AWS Glue to compress the data into the Apache Parquet format.

Answer: B

 

NEW QUESTION 37
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