You use the Azure Machine Learning Python SDK to define a pipeline to train a model.
The data used to train the model is read from a folder in a datastore.
You need to ensure the pipeline runs automatically whenever the data in the folder changes.
What should you do?
You are implementing a machine learning model to predict stock prices.
The model uses a PostgreSQL database and requires GPU processing.
You need to create a virtual machine that is pre-configured with the required tools.
What should you do?
Incorrect Answers:
A, C: PostgreSQL (CentOS) is only available in the Linux Edition.
B: The Azure Geo AI Data Science VM (Geo-DSVM) delivers geospatial analytics capabilities from Microsoft's
Data Science VM. Specifically, this VM extends the AI and data science toolkits in the Data Science VM by
adding ESRI's market-leading ArcGIS Pro Geographic Information System.
D: DLVM is a template on top of DSVM image. In terms of the packages, GPU drivers etc are all there in the
DSVM image. Mostly it is for convenience during creation where we only allow DLVM to be created on GPU
VM instances on Azure.
https://docs.microsoft.com/en-us/azure/machine-learning/data-science-virtual-machine/overview
You create a multi-class image classification deep learning model that uses a set of labeled images. You
create a script file named train.py that uses the PyTorch 1.3 framework to train the model.
You must run the script by using an estimator. The code must not require any additional Python libraries to be installed in the environment for the estimator. The time required for model training must be minimized.
You need to define the estimator that will be used to run the script.
Which estimator type should you use?
For PyTorch, TensorFlow and Chainer tasks, Azure Machine Learning provides respective PyTorch, TensorFlow, and Chainer estimators to simplify using these frameworks.
https://docs.microsoft.com/en-us/azure/machine-learning/how-to-train-ml-models
You have a dataset that includes confidential dat
a. You use the dataset to train a model.
You must use a differential privacy parameter to keep the data of individuals safe and private.
You need to reduce the effect of user data on aggregated results.
What should you do?
Differential privacy tries to protect against the possibility that a user can produce an indefinite number of reports to eventually reveal sensitive data. A value known as epsilon measures how noisy, or private, a report is. Epsilon has an inverse relationship to noise or privacy. The lower the epsilon, the more noisy (and private) the data is.
https://docs.microsoft.com/en-us/azure/machine-learning/concept-differential-privacy
You manage an Azure Machine Learning workspace. You have an environment for training jobs which uses an existing Docker image. A new version of the Docker image is available.
You need to use the latest version of the Docker image for the environment configuration by using the Azure Machine Learning SDK v2-What should you do?
