You work for a computer peripheral manufacturer that builds printers, external hard drives, etc. You are on the machine learning team where you are currently building a machine learning model to be used to find anomalies in the functional behavior of your company’s line of printers. The printers generate IoT device messages that are streamed to your model S3 bucket using Amazon Kinesis Data Streams. You have performed your data cleansing and data engineering of your IoT printer data. You are now ready to start training your model. You have chosen the Random Cut Forest SageMaker built-in algorithm for your model. You hope to find anomalies in your customer’s printer activity by looking for outlier observations using your Random Cut Forest-based model. Finding these anomalies will help your company provide better customer service.
You have started your first training job, but you see that your training job is failing. What may be the cause of this failure?

Answer options:

A.You have selected compute resources of the GPU compute instance class.
B.You have selected compute resources of the CPU compute instance class.
C.You have built your training data files using the CSV file type.
D.You have built your training data files using the recordio-protobuf file type.

Answer: A
Option A is correct. SageMaker only supports the CPU instance class for the Random Cut Forest algorithm.
Option B is incorrect. SageMaker only supports the CPU instance class for the Random Cut Forest algorithm. So selecting the instance class of CPU would not cause your training job to fail.
Option C is incorrect. SageMaker supports both the CSV and recordio-protobuf file types for training data files. So using the CSV file type for your training data would not cause your training job to fail.
Option D is incorrect. SageMaker supports both the CSV and recordio-protobuf file types for training data files. So using the recordio-protobuf file type for your training data would not cause your training job to fail.
Reference:
Please see the Amazon SageMaker developer guide titled Train a Model with Amazon SageMaker, the Amazon SageMaker developer guide titled Common Parameters for Built-In Algorithms.