Designing and Implementing a Data Science Solution on Azure: DP-100

You plan to run a script as an experiment using a Script Run Configuration. The script uses modules from the scipy library as well as 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 clusters 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?

A. Do not specify an environment in the run configuration for the experiment. Run the experiment by using the default environment.

B. Create a virtual machine (VM) with the required Python configuration and attach the VM as a compute target. Use this compute target for all experiment runs.

C. Create and register an Environment that includes the required packages. Use this Environment for all experiment runs.

D. Create a config.yaml file defining the conda packages that are required and save the file in the experiment folder.

E. Always run the experiment with an Estimator by using the default packages.

You are a data scientist creating a linear regression model.

You need to determine how closely the data fits the regression line.

Which metric should you review?

A. Root Mean Square Error

B. Coefficient of determination

C. Recall

D. Precision

E. Mean absolute error

You are performing clustering by using the K-means algorithm.

You need to define the possible termination conditions.

Which three conditions can you use? Each correct answer presents a complete solution.

NOTE: Each correct selection is worth one point.

A. Centroids do not change between iterations.

B. The residual sum of squares (RSS) rises above a threshold.

C. The residual sum of squares (RSS) falls below a threshold.

D. A fixed number of iterations is executed.

E. The sum of distances between centroids reaches a maximum.