Free Microsoft DP-100 Exam Questions

Question 1

You develop and train a machine learning model to predict fraudulent transactions for a hotel booking website. Traffic to the site varies considerably. The site experiences heavy traffic on Monday and Friday and much lower traffic on other days. Holidays are also high web traffic days. You need to deploy the model as an Azure Machine Learning real-time web service endpoint on compute that can dynamically scale up and down to support demand. Which deployment compute option should you use?

A : attached Azure Databricks cluster

B : Azure Container Instance (ACI)

C : Azure Kubernetes Service (AKS) inference cluster

D : Azure Machine Learning Compute Instance

E : attached virtual machine in a different region

Answer: E

Question 2

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

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

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

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

E : 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.

Answer: A

Question 3

You use Azure Machine Learning Studio to build a machine learning experiment. You need to divide data into two distinct datasets. Which module should you use?

A : Split Data

B : Load Trained Model

C : Assign Data to Clusters

D : Group Data into Bins

Answer: D

Question 4

You develop and train a machine learning model to predict fraudulent transactions for a hotel booking website. Traffic to the site varies considerably. The site experiences heavy traffic on Monday and Friday and much lower traffic on other days. Holidays are also high web traffic days. You need to deploy the model as an Azure Machine Learning real-time web service endpoint on compute that can dynamically scale up and down to support demand. Which deployment compute option should you use?

A : attached Azure Databricks cluster

B : Azure Container Instance (ACI)

C : Azure Kubernetes Service (AKS) inference cluster

D : Azure Machine Learning Compute Instance

E : attached virtual machine in a different region

Answer: E

Question 5

You use the Azure Machine Learning SDK to run a training experiment that trains a classification model and

calculates its accuracy metric.

The model will be retrained each month as new data is available.

You must register the model for use in a batch inference pipeline.

You need to register the model and ensure that the models created by subsequent retraining experiments are

registered only if their accuracy is higher than the currently registered model.

What are two possible ways to achieve this goal? Each correct answer presents a complete solution.

NOTE: Each correct selection is worth one point.

A : Specify a different name for the model each time you register it.

B : Register the model with the same name each time regardless of accuracy, and always use the latest version of the model in the batch inferencing pipeline

C : Specify the model framework version when registering the model, and only register subsequent models if this value is higher.

D : Specify a property named accuracy with the accuracy metric as a value when registering the model, and only register subsequent models if their accuracy is higher than the accuracy property value of the currently registered model.

E : Specify a tag named accuracy with the accuracy metric as a value when registering the model, and only register subsequent models if their accuracy is higher than the accuracy tag value of the currently registered model.

Answer: D,E