Microsoft DP-100 Exam Dumps

Correct Answer:

Step 1: Sweep Clustering

Start by using the "Tune Model Hyperparameters" module to select the best sets of parameters for each of the models we're considering.

One of the interesting things about the "Tune Model Hyperparameters" module is that it not only outputs the results from the Tuning, it also outputs the Trained Model.

Step 2: Train Model

Step 3: Evaluate Model

Scenario: You need to provide the test results to the Fabrikam Residences team. You create data visualizations to aid in presenting the results.

You must produce a Receiver Operating Characteristic (ROC) curve to conduct a diagnostic test evaluation of the model. You need to select appropriate methods for producing the ROC curve in Azure Machine Learning Studio to compare the

Two-Class Decision Forest and the Two-Class Decision Jungle modules with one another.

References: http://breaking-bi.blogspot.com/2017/01/azure-machine-learning-model-evaluation.html

Correct Answer: B

Log a numerical or string value to the run with the given name using log(name, value, description=''). Logging a metric to a run causes that metric to be stored in the run record in the experiment. You can log the same metric multiple times

within a run, the result being considered a vector of that metric.

Example: run.log("accuracy", 0.95)

Incorrect Answers:

E: Using log_row(name, description=None, **kwargs) creates a metric with multiple columns as described in kwargs. Each named parameter generates a column with the value specified. log_row can be called once to log an arbitrary tuple, or multiple times in a loop to generate a complete table.

Example: run.log_row("Y over X", x=1, y=0.4)

Reference: https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.core.run

Correct Answer: A

Key-based authentication.

Web services deployed on AKS have key-based auth enabled by default. ACI-deployed services have key-based auth disabled by default, but you can enable it by setting auth_enabled = TRUE when creating the ACI web service. The

following is an example of creating an ACI deployment configuration with key-based auth enabled.

deployment_config <- aci_webservice_deployment_config(cpu_cores = 1, memory_gb = 1, auth_enabled = TRUE)

Reference: https://azure.github.io/azureml-sdk-for-r/articles/deploying-models.html