Question 236
You create a deep learning model for image recognition on Azure Machine Learning service using GPU-based training.
You must deploy the model to a context that allows for real-time GPU-based inferencing.
You need to configure compute resources for model inferencing.
Which compute type should you use?
You must deploy the model to a context that allows for real-time GPU-based inferencing.
You need to configure compute resources for model inferencing.
Which compute type should you use?
Question 237
You have a multi-class image classification deep learning model that uses a set of labeled photographs. You create the following code to select hyperparameter values when training the model.
For each of the following statements, select Yes if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.
For each of the following statements, select Yes if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.
Question 238
You publish a batch inferencing pipeline that will be used by a business application.
The application developers need to know which information should be submitted to and returned by the REST interface for the published pipeline.
You need to identify the information required in the REST request and returned as a response from the published pipeline.
Which values should you use in the REST request and to expect in the response? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
The application developers need to know which information should be submitted to and returned by the REST interface for the published pipeline.
You need to identify the information required in the REST request and returned as a response from the published pipeline.
Which values should you use in the REST request and to expect in the response? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Question 239
You plan to build a team data science environment. Data for training models in machine learning pipelines will be over 20 GB in size.
You have the following requirements:
- Models must be built using Caffe2 or Chainer frameworks.
- Data scientists must be able to use a data science environment to
build the machine learning pipelines and train models on their personal devices in both connected and disconnected network environments.
- Personal devices must support updating machine learning pipelines
when connected to a network.
You need to select a data science environment.
Which environment should you use?
You have the following requirements:
- Models must be built using Caffe2 or Chainer frameworks.
- Data scientists must be able to use a data science environment to
build the machine learning pipelines and train models on their personal devices in both connected and disconnected network environments.
- Personal devices must support updating machine learning pipelines
when connected to a network.
You need to select a data science environment.
Which environment should you use?
Question 240
You have an Azure Machine Learning workspace.
You plan to tune a model hyperparameter when you train the model.
You need to define a search space that returns a normally distributed value.
Which parameter should you use?
You plan to tune a model hyperparameter when you train the model.
You need to define a search space that returns a normally distributed value.
Which parameter should you use?