Designing and Implementing a Data Science Solution on Azure — Question 124

You plan to run a script as an experiment. The script uses modules from the SciPy library and several Python packages that are not typically installed in a default conda environment.

You plan to run the experiment on your local workstation for small datasets and scale out the experiment by running it on more powerful remote compute dusters for larger datasets.

You need to ensure that the experiment runs successfully on local and remote compute with the least administrative effort.

What should you do?

Answer options

• A. Leave the environment unspecified for the experiment. Run the expenment by using the default environment.
• B. Create a config.yaml file that defines the required conda packages and save the file in the experiment folder.
• C. Create and register an environment that includes the required packages. Use this environment for all experiment jobs.
• D. Create a virtual machine (VM) by using the required Python configuration and attach the VM as a compute target. Use this compute target for all experiment runs.

Correct answer: C

Explanation

The correct answer is C because creating and registering an environment with the required packages ensures that both local and remote executions have the same dependencies, minimizing compatibility issues. Option A is incorrect as it relies on a default environment, which may lack the needed packages. Option B, while helpful, does not directly create an environment for use. Option D introduces unnecessary complexity and overhead by using a VM when a conda environment would suffice.