A Machine Learning Specialist works for a credit card processing company and needs to predict which transactions may be fraudulent in near-real time. Specifically, the Specialist must train a model that returns the probability that a given transaction may fraudulent.
How should the Specialist frame this business problem?

• A.Multi-category classification
• B.Regression classification
• C.Streaming classification
• D.Binary classification

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A manufacturing company has a production line with sensors that collect hundreds of quality metrics. The company has stored sensor data and manual inspection results in a data lake for several months. To automate quality control, the machine learning team must build an automated mechanism that determines whether the produced goods are good quality, replacement market quality, or scrap quality based on the manual inspection results.
Which modeling approach will deliver the MOST accurate prediction of product quality?

• A.Amazon SageMaker DeepAR forecasting algorithm
• B.Amazon SageMaker XGBoost algorithm
• C.Amazon SageMaker Latent Dirichlet Allocation (LDA) algorithm
• D.A convolutional neural network (CNN) and ResNet

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A Machine Learning Specialist is using Apache Spark for pre-processing training data As part of the Spark pipeline, the Specialist wants to use Amazon SageMaker for training a model and hosting it Which of the following would the Specialist do to integrate the Spark application with SageMaker? (Select THREE )

• A.Download the AWS SDK for the Spark environment
• B.Convert the DataFrame object to a CSV file, and use the CSV file as input for obtaining inferences from SageMaker.
• C.Compress the training data into a ZIP file and upload it to a pre-defined Amazon S3 bucket.
• D.Use the appropriate estimator from the SageMaker Spark Library to train a model.
• E.Install the SageMaker Spark library in the Spark environment.
• F.Use the sageMakerModel. transform method to get inferences from the model hosted in SageMaker

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A wildlife research company has a set of images of lions and cheetahs. The company created a dataset of the images. The company labeled each image with a binary label that indicates whether an image contains a lion or cheetah. The company wants to train a model to identify whether new images contain a lion or cheetah.
.... Dh Amazon SageMaker algorithm will meet this requirement?

• A.XGBoost
• B.Image Classification - TensorFlow
• C.Object Detection - TensorFlow
• D.Semantic segmentation - MXNet

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A retail chain has been ingesting purchasing records from its network of 20,000 stores to Amazon S3 using Amazon Kinesis Data Firehose To support training an improved machine learning model, training records will require new but simple transformations, and some attributes will be combined The model needs lo be retrained daily Given the large number of stores and the legacy data ingestion, which change will require the LEAST amount of development effort?

• A.Require that the stores to switch to capturing their data locally on AWS Storage Gateway for loading into Amazon S3 then use AWS Glue to do the transformation
• B.Deploy an Amazon EMR cluster running Apache Spark with the transformation logic, and have the cluster run each day on the accumulating records in Amazon S3, outputting new/transformed records to Amazon S3
• C.Spin up a fleet of Amazon EC2 instances with the transformation logic, have them transform the data records accumulating on Amazon S3, and output the transformed records to Amazon S3.
• D.Insert an Amazon Kinesis Data Analytics stream downstream of the Kinesis Data Firehouse stream that transforms raw record attributes into simple transformed values using SQL.

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