Free Microsoft DP-100 Exam Questions

Question 1

You plan to use the Hyperdrive feature of Azure Machine Learning to determine the optimal hyperparameter values when training a model. You must use Hyperdrive to try combinations of the following hyperparameter values: learning_rate: any value between 0.001 and 0.1 batch_size: 16, 32, or 64 You need to configure the search space for the Hyperdrive experiment. Which two parameter expressions should you use? Each correct answer presents part of the solution. NOTE: Each correct selection is worth one point.

A : a choice expression for learning_rate

B : a normal expression for batch_size

C : a choice expression for batch_size

D : a uniform expression for batch_size

Answer: B,D

Question 2

You use the following Python code in a notebook to deploy a model as a web service:The deployment fails. You need to use the Python SDK in the notebook to determine the events that occurred during service deployment an initialization. Which code segment should you use?

A : service.state

B : service.environment

C : service.get_logs()

D : Service.serialize

Answer: C

Question 3

An organization creates and deploys a multi-class image classification deep learning model that uses a set of labeled photographs. The software engineering team reports there is a heavy inferencing load for the prediction web services during the summer. The production web service for the model fails to meet demand despite having a fully-utilized compute cluster where the web service is deployed. You need to improve performance of the image classification web service with minimal downtime and minimal administrative effort. What should you advise the IT Operations team to do?

A : Increase the minimum node count of the compute cluster where the web service is deployed.

B : Create a new compute cluster by using larger VM sizes for the nodes, redeploy the web service to that cluster, and update the DNS registration for the service endpoint to point to the new cluster.

C : Increase the VM size of nodes in the compute cluster where the web service is deployed.

D : Increase the node count of the compute cluster where the web service is deployed.

Answer: D

Question 4

You create an Azure Machine Learning pipeline named pipeline 1 with two steps that contain Python scnpts. Data processed by the first step is passed to the second step. You must update the content of the downstream data source of pipeline 1 and run the pipeline again. You need to ensure the new run of pipeline 1 fully processes the updated content. Solution: Change the value of the compute.target parameter of the PythonScriptStep object in the two steps. Does the solution meet the goal'

A : Yes

B : No

Answer: B

Question 5

A set of CSV files contains sales records. All the CSV files have the same data schema.

Each CSV file contains the sales record for a particular month and has the filename sales.csv. Each file in

stored in a folder that indicates the month and year when the data was recorded. The folders are in an Azure

blob container for which a datastore has been defined in an Azure Machine Learning workspace. The folders

are organized in a parent folder named sales to create the following hierarchical structure:

At the end of each month, a new folder with that month’s sales file is added to the sales folder.

You plan to use the sales data to train a machine learning model based on the following requirements:

You must define a dataset that loads all of the sales data to date into a structure that can be easily

converted to a dataframe.

You must be able to create experiments that use only data that was created before a specific previous

month, ignoring any data that was added after that month.

You must register the minimum number of datasets possible.

You need to register the sales data as a dataset in Azure Machine Learning service workspace.

What should you do?

A : Create a tabular dataset that references the datastore and explicitly specifies each 'sales/mm-yyyy/sales.csv' file every month. Register the dataset with the name sales_dataset each month, replacing theexisting dataset and specifying a tag named month indicating the month and year it was registered. Usethis dataset for all experiments.

B : Create a tabular dataset that references the datastore and specifies the path 'sales/*/sales.csv', register thedataset with the name sales_dataset and a tag named month indicating the month and year it wasregistered, and use this dataset for all experiments.

C : Create a new tabular dataset that references the datastore and explicitly specifies each 'sales/mm-yyyy/sales.csv' file every month. Register the dataset with the name sales_dataset_MM-YYYY each monthwith appropriate MM and YYYY values for the month and year. Use the appropriate month-specificdataset for experiments.

D : Create a tabular dataset that references the datastore and explicitly specifies each 'sales/mm-yyyy/sales.csv' file. Register the dataset with the name sales_dataset each month as a new version and with atag named month indicating the month and year it was registered. Use this dataset for all experiments,identifying the version to be used based on the month tag as necessary.

Answer: B