Microsoft DP-100 Online Practice Questions and Exam Preparation

Microsoft DP-100 Online Questions & Answers

• Question 31:

  You have a deployment of an Azure OpenAI Service base model. You plan to fine-tune the model.

  You need to prepare a file that contains training data for multi-turn chat.

  Which file encoding method should you use?

  A. UTF-8
  B. UTF-16
  C. ISO-8859-1
  D. ASCII
  A. UTF-8

• Question 32:

  DRAG DROP

  You are developing a machine learning solution by using the Azure Machine Learning designer.

  You need to create a web service that applications can use to submit data feature values and retrieve a predicted label.

  Which three actions should you perform in sequence? To answer, move the appropriate actions from the list of actions to the answer area and arrange them in the correct order.

  Select and Place:

  

  

  ---

  Explanation:

  Step 1: Create and run a training pipeline.

  In part one (not included here) of the tutorial you trained a linear regression model on car prices. In part two, you deploy the model to give others a chance to use it.

  Step 2: Deploy a service to an inference cluster

  Create an inferencing cluster.

  In the dialog box that appears, you can select from any existing Azure Kubernetes Service (AKS) clusters to deploy your model to. If you don't have an AKS cluster, use the following steps to create one.

  *Details omitted*

  Deploy the real-time endpoint

  After your AKS service has finished provisioning, return to the real-time inferencing pipeline to complete deployment.

  Step 3: Create and run a real-time inference pipeline.

  Create a real-time inference pipeline

  To deploy your pipeline, you must first convert the training pipeline into a real-time inference pipeline. This process removes training components and adds web service inputs and outputs to handle requests.

  1. On pipeline job detail page, above the pipeline canvas, select Create inference pipeline > Real-time inference pipeline.

  *Details omitted* 2. Select Submit, and use the same compute target and experiment that you used in part one.

  3. Go to the real-time inference pipeline job detail by selecting Job detail link in the left pane.

  4. Select Deploy in the job detail page.

  https://learn.microsoft.com/en-us/azure/machine-learning/tutorial-designer-automobile-price-deploy

• Question 33:

  You have an Azure Machine Learning workspace named Workspace1.

  Workspace1 has a registered MLflow model named model1 with PyFunc flavor.

  You plan to deploy model1 to an online endpoint named endpoint1 without egress connectivity by using Azure Machine Learning Python SDK v2.

  You have the following code:

  

  You need to add a parameter to the ManagedOnlineDeployment object to ensure the model deploys successfully.

  Solution: Add the code_path parameter.

  Does the solution meet the goal?

  A. Yes
  B. No
  B. No

  ---

  
  

• Question 34:

  You run Azure Machine Learning training experiments. The training scripts directory contains 100 files that includes a file named .amlignore. The directory also contains subdirectories named ./outputs and ./logs.

  There are 20 files in the training scripts directory that must be excluded from the snapshot to the compute targets. You create a file named .gitignore in the root of the directory. You add the names of the 20 files to the .gitignore file. These 20 files continue to be copied to the compute targets.

  You need to exclude the 20 files.

  What should you do?

  A. Copy the contents of the file named .gitignore to the file named .amlignore.
  B. Move the file named .gitignore to the ./outputs directory.
  C. Move the file named .gitignore to the ./logs directory.
  D. Add the contents of the file named .amlignore to the file named .gitignore.
  A. Copy the contents of the file named .gitignore to the file named .amlignore.

  ---

  Explanation

  To prevent unnecessary files from being included in the snapshot, make an ignore file (.gitignore or .amlignore) in the directory. Add the files and directories to exclude to this file. For more information on the syntax to use inside this file, see syntax and patterns for .gitignore. The .amlignore file uses the same syntax. If both files exist, the .amlignore file is used and the .gitignore file is unused.

  https://learn.microsoft.com/en-us/azure/machine-learning/how-to-save-write-experiment-files

• Question 35:

  HOTSPOT

  You are developing code to analyse a dataset that includes age information for a large group of diabetes patients. You create an Azure Machine Learning workspace and install all required libraries. You set the privacy budget to 1.0.

  You must analyze the dataset and preserve data privacy. The code must run twice before the privacy budget is depleted.

  You need to complete the code.

  Which values should you use? To answer, select the appropriate options m the answer area.

  NOTE: Each correct selection is worth one point.

  

  

• Question 36:

  You create a multi-class image classification deep learning model.

  You train the model by using PyTorch version 1.2.

  You need to ensure that the correct version of PyTorch can be identified for the inferencing environment when the model is deployed.

  What should you do?

  A. Save the model locally as a.pt file, and deploy the model as a local web service.
  B. Deploy the model on computer that is configured to use the default Azure Machine Learning conda environment.
  C. Register the model with a .pt file extension and the default version property.
  D. Register the model, specifying the model_framework and model_framework_version properties.
  D. Register the model, specifying the model_framework and model_framework_version properties.

  ---

  Explanation

  framework_version: The PyTorch version to be used for executing training code.

  https://docs.microsoft.com/en-us/python/api/azureml-train-core/azureml.train.dnn.pytorch?view=azure-ml-

  py

• Question 37:

  HOTSPOT

  You create an Azure Machine Learning workspace

  You are developing a Python SDK v2 notebook to perform custom model training in the workspace. The notebook code imports all required packages.

  You need to complete the Python SDK v2 code to include a training script. environment, and compute information.

  How should you complete ten code? To answer, select the appropriate options in the answer area.

  NOTE: Each correct selection is worth one point

  

  

• Question 38:

  You must store data in Azure Blob Storage to support Azure Machine Learning.

  You need to transfer the data into Azure Blob Storage.

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

  NOTE: Each correct selection is worth one point.

  A. Bulk Insert SQL Query
  B. AzCopy
  C. Python script
  D. Azure Storage Explorer
  E. Bulk Copy Program (BCP)
  B. AzCopy
  C. Python script
  D. Azure Storage Explorer

  ---

  Explanation

  You can move data to and from Azure Blob storage using different technologies:

  1. Azure Storage-Explorer

  2. AzCopy

  3. Python

  4. SSIS References:

  https://docs.microsoft.com/en-us/azure/machine-learning/team-data-science-process/move-azure-blob

• Question 39:

  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:

  1. 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.

  2. 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.

  3. 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 the existing dataset and specifying a tag named month indicating the month and year it was registered. Use this dataset for all experiments.
  B. Create a tabular dataset that references the datastore and specifies the path 'sales/*/sales.csv', register the dataset with the name sales_dataset and a tag named month indicating the month and year it was registered, 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 month with appropriate MM and YYYY values for the month and year. Use the appropriate month- specific dataset 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 a tag 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.
  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 a tag 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.

  ---

  Explanation

  Specify the path.

  Example:

  The following code gets the workspace existing workspace and the desired datastore by name. And then passes the datastore and file locations to the path parameter to create a new TabularDataset, weather_ds.

  from azureml.core import Workspace, Datastore, Dataset datastore_name = 'your datastore name'

  # get existing workspace workspace = Workspace.from_config()

  # retrieve an existing datastore in the workspace by name datastore = Datastore.get(workspace, datastore_name)

  # create a TabularDataset from 3 file paths in datastore datastore_paths = [(datastore, 'weather/2018/11.csv'), (datastore, 'weather/2018/12.csv'), (datastore, 'weather/2019/*.csv')] weather_ds = Dataset.Tabular.from_delimited_files(path=datastore_paths)

• Question 40:

  DRAG DROP

  You train and register a model by using the Azure Machine Learning Python SDK v2 on a local workstation. Python 3.7 and Visual Studio Code are installed on the workstation.

  When you try to deploy the model into production to a Kubernetes online endpoint, you experience an error in the scoring script that causes deployment to fail.

  You need to debug the service on the local workstation before deploying the service to production.

  Which three actions should you perform in sequence? To answer, move the appropriate actions from the list of actions to the answer area and arrange them in the correct order.

  Select and Place:

  

  

  ---

  Explanation:

  Step 1: Install Docker on the workstation Interactive debugging with Visual Studio Code Prerequisites include:

  * Docker

  * Python 3

  Important

  Before running your experiment locally make sure that:

  Docker is running.

  The azureML.CLI Compatibility Mode setting in Visual Studio Code is set to 1.0 as specified in the prerequisites

  Step 2: Run the begin_create_or_update method of an MLClient class instance with the local parameter set to true.

  Deploy locally

  Local deployment is deploying a model to a local Docker environment. Local deployment is useful for testing and debugging before deployment to the cloud.

  To use local deployment, add local=True parameter in the command:

  ml_client.begin_create_or_update(online_deployment, local=True) ml_client is the instance for MLCLient class, and online_deployment is the instance for either ManagedOnlineDeployment class or KubernetesOnlineDeployment class.

  Step 3: Debug and modify the scoring script as necessary Debug experiment locally

  Incorrect:

  * Upgrade the Python version on the workstation Not necessary

  https://learn.microsoft.com/en-us/azure/machine-learning/how-to-debug-visual-studio-code

  https://learn.microsoft.com/en-us/azure/machine-learning/how-to-troubleshoot-online-endpoints