Question: 1
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?
Question: 2
You are developing deep learning models to analyze semi-structured, unstructured, and structured data types.
You have the following data available for model building:
*Video recordings of sporting events
*Transcripts of radio commentary about events
*Logs from related social media feeds captured during sporting events
You need to select an environment for creating the model.
Which environment should you use?
Question: 3
You create a binary classification model by using Azure Machine Learning Studio.
You must tune hyperparameters by performing a parameter sweep of the model. The parameter sweep must meet the following requirements:
*iterate all possible combinations of hyperparameters
*minimize computing resources required to perform the sweep
*You need to perform a parameter sweep of the model.
Which parameter sweep mode should you use?
Question: 4
You create an Azure Machine Learning compute resource to train models. The compute resource is configured as follows:
* Minimum nodes: 2
* Maximum nodes: 4
You must decrease the minimum number of nodes and increase the maximum number of nodes to the following values:
* Minimum nodes: 0
* Maximum nodes: 8
You need to reconfigure the compute resource.
Question: 5
Because of non-linear relationships in the data, the pipeline calculates the natural log (Ln) of the prices in the training data, trains a model to predict this natural log of price value, and then calculates the exponential of the scored label to get the predicted price.
The training pipeline is shown in the exhibit (Click the Training pipeline tab.)
You create a real-time inference pipeline from the training pipeline, as shown in the exhibit. (Click the Real-time pipeline lab.)
You need to modify the inference pipeline to ensure that the web service returns the exponential of the scored label as the predicted automobile price and that client applications are not required to include a price value in the input values.
Which three modifications must you make to the inference pipeline? Each correct answer presents part of the solution.
