Updated Microsoft DP-100 Practice Exam Dumps For Azure Data Scientist Associate Certification Exam

1. Question Set 1

You are developing a hands-on workshop to introduce Docker for Windows to attendees.

You need to ensure that workshop attendees can install Docker on their devices.

Which two prerequisite components should attendees install on the devices? Each correct answer presents part of the solution. NOTE: Each correct selection is worth one point.

2. Your team is building a data engineering and data science development environment.

The environment must support the following requirements:

- support Python and Scala

- compose data storage, movement, and processing services into automated data pipelines

- the same tool should be used for the orchestration of both data engineering and data science

- support workload isolation and interactive workloads

- enable scaling across a cluster of machines

You need to create the environment.

What should you do?

3. DRAG DROP

You are building an intelligent solution using machine learning models.

The environment must support the following requirements:

- Data scientists must build notebooks in a cloud environment

- Data scientists must use automatic feature engineering and model building in machine learning pipelines.

- Notebooks must be deployed to retrain using Spark instances with dynamic worker allocation.

- Notebooks must be exportable to be version controlled locally.

You need to create the environment.

Which four 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.

4. You plan to build a team data science environment. Data for training models in machine learning pipelines will be over 20 GB in size.

You have the following requirements:

- Models must be built using Caffe2 or Chainer frameworks.

- Data scientists must be able to use a data science environment to build the machine learning pipelines and train models on their personal devices in both connected and disconnected network environments. Personal devices must support updating machine learning pipelines when connected to a network.

You need to select a data science environment.

Which environment should you use?

5. You are implementing a machine learning model to predict stock prices.

The model uses a PostgreSQL database and requires GPU processing.

You need to create a virtual machine that is pre-configured with the required tools.

What should you do?

6. You are developing deep learning models to analyze semi-structured, unstructured, and structured data types.

You have the following data available for model building:

- Video recordings of sporting events

- Transcripts of radio commentary about events

- Logs from related social media feeds captured during sporting events

You need to select an environment for creating the model.

Which environment should you use?

7. 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.

8. You are moving a large dataset from Azure Machine Learning Studio to a Weka environment.

You need to format the data for the Weka environment.

Which module should you use?

9. You plan to create a speech recognition deep learning model.

The model must support the latest version of Python.

You need to recommend a deep learning framework for speech recognition to include in the Data Science Virtual Machine (DSVM).

What should you recommend?

10. You plan to use a Deep Learning Virtual Machine (DLVM) to train deep learning models using Compute Unified Device Architecture (CUDA) computations.

You need to configure the DLVM to support CUDA.

What should you implement?

11. You plan to use a Data Science Virtual Machine (DSVM) with the open source deep learning frameworks Caffe2 and PyTorch.

You need to select a pre-configured DSVM to support the frameworks.

What should you create?

12. HOTSPOT

You are performing sentiment analysis using a CSV file that includes 12,000 customer reviews written in a short sentence format. You add the CSV file to Azure Machine Learning Studio and configure it as the starting point dataset of an experiment. You add the Extract N-Gram Features from Text module to the experiment to extract key phrases from the customer review column in the dataset.

You must create a new n-gram dictionary from the customer review text and set the maximum n-gram size to trigrams.

What should you select? To answer, select the appropriate options in the answer area. NOTE: Each correct selection is worth one point.

13. You are developing a data science workspace that uses an Azure Machine Learning service.

You need to select a compute target to deploy the workspace.

What should you use?

14. You are solving a classification task.

The dataset is imbalanced.

You need to select an Azure Machine Learning Studio module to improve the classification accuracy.

Which module should you use?

15. DRAG DROP

You configure a Deep Learning Virtual Machine for Windows.

You need to recommend tools and frameworks to perform the following:

- Build deep neural network (DNN) models

- Perform interactive data exploration and visualization

Which tools and frameworks should you recommend? To answer, drag the appropriate tools to the correct tasks. Each tool may be used once, more than once, or not at all. You may need to drag the split bar between panes or scroll to view content. NOTE: Each correct selection is worth one point.

16. You are analyzing a dataset containing historical data from a local taxi company. You are developing a regression model.

You must predict the fare of a taxi trip.

You need to select performance metrics to correctly evaluate the regression model.

Which two metrics can you use? Each correct answer presents a complete solution? NOTE: Each correct selection is worth one point.

17. Note: This question is part of a series of questions that present the same scenario. Each question in the series contains a unique solution that might meet the stated goals. Some question sets might have more than one correct solution, while others might not have a correct solution.

After you answer a question in this section, you will NOT be able to return to it. As a result, these

questions will not appear in the review screen.

You are using Azure Machine Learning to run an experiment that trains a classification model.

You want to use Hyperdrive to find parameters that optimize the AUC metric for the model.

You configure a HyperDriveConfig for the experiment by running the following code:

You plan to use this configuration to run a script that trains a random forest model and then tests it with validation data. The label values for the validation data are stored in a variable named y_test variable, and the predicted probabilities from the model are stored in a variable named y_predicted.

You need to add logging to the script to allow Hyperdrive to optimize hyperparameters for the AUC metric.

Solution: Run the following code:

Does the solution meet the goal?

18. Note: This question is part of a series of questions that present the same scenario. Each question in the series contains a unique solution that might meet the stated goals. Some question sets might have more than one correct solution, while others might not have a correct solution.

After you answer a question in this section, you will NOT be able to return to it. As a result, these questions will not appear in the review screen.

You are using Azure Machine Learning to run an experiment that trains a classification model.

You want to use Hyperdrive to find parameters that optimize the AUC metric for the model.

You configure a HyperDriveConfig for the experiment by running the following code:

You plan to use this configuration to run a script that trains a random forest model and then tests it with validation data. The label values for the validation data are stored in a variable named y_test variable, and the predicted probabilities from the model are stored in a variable named y_predicted.

You need to add logging to the script to allow Hyperdrive to optimize hyperparameters for the AUC metric.

Solution: Run the following code:

Does the solution meet the goal?

19. Note: This question is part of a series of questions that present the same scenario. Each question in the series contains a unique solution that might meet the stated goals. Some question sets might have more than one correct solution, while others might not have a correct solution.

After you answer a question in this section, you will NOT be able to return to it. As a result, these questions will not appear in the review screen.

You are using Azure Machine Learning to run an experiment that trains a classification model.

You want to use Hyperdrive to find parameters that optimize the AUC metric for the model.

You configure a HyperDriveConfig for the experiment by running the following code:

You plan to use this configuration to run a script that trains a random forest model and then tests it with validation data. The label values for the validation data are stored in a variable named y_test variable, and the predicted probabilities from the model are stored in a variable named y_predicted.

You need to add logging to the script to allow Hyperdrive to optimize hyperparameters for the AUC metric.

Solution: Run the following code:

Does the solution meet the goal?

20. HOTSPOT

The finance team asks you to train a model using data in an Azure Storage blob container named finance-data.

You need to register the container as a datastore in an Azure Machine Learning workspace and ensure that an error will be raised if the container does not exist.

How should you complete the code? To answer, select the appropriate options in the answer area. NOTE: Each correct selection is worth one point.

21. You plan to provision an Azure Machine Learning Basic edition workspace for a data science project.

You need to identify the tasks you will be able to perform in the workspace.

Which three tasks will you be able to perform? Each correct answer presents a complete solution. NOTE: Each correct selection is worth one point.

22. HOTSPOT

A coworker registers a datastore in a Machine Learning services workspace by using the following code:

You need to write code to access the datastore from a notebook.

How should you complete the code segment? To answer, select the appropriate options in the answer area. NOTE: Each correct selection is worth one point.

23. 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?

24. DRAG DROP

An organization uses Azure Machine Learning service and wants to expand their use of machine learning.

You have the following compute environments.

The organization does not want to create another compute environment.

You need to determine which compute environment to use for the following scenarios.

Which compute types should you use? To answer, drag the appropriate compute environments to the correct scenarios. Each compute environment may be used once, more than once, or not at all. You may need to drag the split bar between panes or scroll to view content. NOTE: Each correct selection is worth one point.

25. HOTSPOT

You create an Azure Machine Learning compute target named ComputeOne by using the STANDARD_D1 virtual machine image.

ComputeOne is currently idle and has zero active nodes.

You define a Python variable named was that references the Azure Machine Learning workspace.

You run the following Python code:

For each of the following statements, select Yes if the statement is true. Otherwise, select No. NOTE: Each correct selection is worth one point.

26. HOTSPOT

You are developing a deep learning model by using TensorFlow. You plan to run the model training workload on an Azure Machine Learning Compute Instance.

You must use CUDA-based model training.

You need to provision the Compute Instance.

Which two virtual machines sizes can you use? To answer, select the appropriate virtual machine sizes in the answer area. NOTE: Each correct selection is worth one point.

27. You use the following code to run a script as an experiment in Azure Machine Learning:

You must identify the output files that are generated by the experiment run.

You need to add code to retrieve the output file names.

Which code segment should you add to the script?

28. You write five Python scripts that must be processed in the order specified in Exhibit A C which allows the same modules to run in parallel, but will wait for modules with dependencies.

You must create an Azure Machine Learning pipeline using the Python SDK, because you want to script to create the pipeline to be tracked in your version control system. You have created five PythonScriptSteps and have named the variables to match the module names.

You need to create the pipeline shown. Assume all relevant imports have been done.

Which Python code segment should you use?

A)

B)

C)

D)

29. HOTSPOT

You are preparing to use the Azure ML SDK to run an experiment and need to create compute.

You run the following code:

For each of the following statements, select Yes if the statement is true. Otherwise, select No. NOTE: Each correct selection is worth one point.

30. You create a datastore named training_data that references a blob container in an Azure Storage account. The blob container contains a folder named csv_files in which multiple comma-separated values (CSV) files are stored.

You have a script named train.py in a local folder named ./script that you plan to run as an experiment using an estimator.

The script includes the following code to read data from the csv_files folder:

You have the following script.

You need to configure the estimator for the experiment so that the script can read the data from a data reference named data_ref that references the csv_files folder in the training_data datastore.

Which code should you use to configure the estimator?

A)

B)

C)

D)

E)

31. DRAG DROP

You create a multi-class image classification deep learning experiment by using the PyTorch framework.

You plan to run the experiment on an Azure Compute cluster that has nodes with GPU’s.

You need to define an Azure Machine Learning service pipeline to perform the monthly retraining of the image classification model. The pipeline must run with minimal cost and minimize the time required to train the model.

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

32. HOTSPOT

You are a lead data scientist for a project that tracks the health and migration of birds. You create a multi-image classification deep learning model that uses a set of labeled bird photos collected by experts. You plan to use the model to develop a cross-platform mobile app that predicts the species of bird captured by app users.

You must test and deploy the trained model as a web service.

The deployed model must meet the following requirements:

- An authenticated connection must not be required for testing.

- The deployed model must perform with low latency during inferencing.

- The REST endpoints must be scalable and should have a capacity to handle large number of requests when multiple end users are using the mobile application.

You need to verify that the web service returns predictions in the expected JSON format when a valid REST request is submitted.

Which compute resources should you use? To answer, select the appropriate options in the answer area. NOTE: Each correct selection is worth one point.

33. You create a deep learning model for image recognition on Azure Machine Learning service using GPU-based training.

You must deploy the model to a context that allows for real-time GPU-based inferencing.

You need to configure compute resources for model inferencing.

Which compute type should you use?

34. You create a batch inference pipeline by using the Azure ML SDK.

You run the pipeline by using the following code:

from azureml.pipeline.core import Pipeline

from azureml.core.experiment import Experiment

pipeline = Pipeline(workspace=ws, steps=[parallelrun_step])

pipeline_run = Experiment(ws, 'batch_pipeline').submit(pipeline)

You need to monitor the progress of the pipeline execution.

What are two possible ways to achieve this goal? Each correct answer presents a complete solution. NOTE: Each correct selection is worth one point.

35. You train and register a model in your Azure Machine Learning workspace.

You must publish a pipeline that enables client applications to use the model for batch inferencing. You must use a pipeline with a single ParallelRunStep step that runs a Python inferencing script to get predictions from the input data.

You need to create the inferencing script for the ParallelRunStep pipeline step.

Which two functions should you include? Each correct answer presents part of the solution. NOTE: Each correct selection is worth one point.

36. You deploy a model as an Azure Machine Learning real-time web service using the following code.

The deployment fails.

You need to troubleshoot the deployment failure by determining the actions that were performed during deployment and identifying the specific action that failed.

Which code segment should you run?

37. HOTSPOT

You deploy a model in Azure Container Instance.

You must use the Azure Machine Learning SDK to call the model API.

You need to invoke the deployed model using native SDK classes and methods.

How should you complete the command? To answer, select the appropriate options in the answer areas. NOTE: Each correct selection is worth one point.

38. 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?

39. Note: This question is part of a series of questions that present the same scenario. Each question in the series contains a unique solution that might meet the stated goals. Some question sets might have more than one correct solution, while others might not have a correct solution.

After you answer a question in this section, you will NOT be able to return to it. As a result, these questions will not appear in the review screen.

An IT department creates the following Azure resource groups and resources:

The IT department creates an Azure Kubernetes Service (AKS)-based inference compute target named aks-cluster in the Azure Machine Learning workspace.

You have a Microsoft Surface Book computer with a GPU. Python 3.6 and Visual Studio Code are installed.

You need to run a script that trains a deep neural network (DNN) model and logs the loss and accuracy metrics.

Solution: Attach the mlvm virtual machine as a compute target in the Azure Machine Learning workspace. Install the Azure ML SDK on the Surface Book and run Python code to connect to the workspace. Run the training script as an experiment on the mlvm remote compute resource.

Does the solution meet the goal?

40. Note: This question is part of a series of questions that present the same scenario. Each question in the series contains a unique solution that might meet the stated goals. Some question sets might have more than one correct solution, while others might not have a correct solution.

After you answer a question in this section, you will NOT be able to return to it. As a result, these questions will not appear in the review screen.

An IT department creates the following Azure resource groups and resources:

The IT department creates an Azure Kubernetes Service (AKS)-based inference compute target named aks-cluster in the Azure Machine Learning workspace.

You have a Microsoft Surface Book computer with a GPU. Python 3.6 and Visual Studio Code are installed.

You need to run a script that trains a deep neural network (DNN) model and logs the loss and accuracy metrics.

Solution: Install the Azure ML SDK on the Surface Book. Run Python code to connect to the workspace and then run the training script as an experiment on local compute.

Does the solution meet the goal?

41. Note: This question is part of a series of questions that present the same scenario. Each question in the series contains a unique solution that might meet the stated goals. Some question sets

might have more than one correct solution, while others might not have a correct solution.

After you answer a question in this section, you will NOT be able to return to it. As a result, these questions will not appear in the review screen.

An IT department creates the following Azure resource groups and resources:

The IT department creates an Azure Kubernetes Service (AKS)-based inference compute target named aks-cluster in the Azure Machine Learning workspace.

You have a Microsoft Surface Book computer with a GPU. Python 3.6 and Visual Studio Code are installed.

You need to run a script that trains a deep neural network (DNN) model and logs the loss and accuracy metrics.

Solution: Install the Azure ML SDK on the Surface Book. Run Python code to connect to the workspace.

Run the training script as an experiment on the aks-cluster compute target.

Does the solution meet the goal?

42. You create an Azure Machine Learning workspace.

You must create a custom role named DataScientist that meets the following requirements:

- Role members must not be able to delete the workspace.

- Role members must not be able to create, update, or delete compute resource in the workspace.

- Role members must not be able to add new users to the workspace.

You need to create a JSON file for the DataScientist role in the Azure Machine Learning workspace.

The custom role must enforce the restrictions specified by the IT Operations team.

Which JSON code segment should you use?

A)

B)

C)

D)

43. You train a machine learning model.

You must deploy the model as a real-time inference service for testing. The service requires low CPU utilization and less than 48 MB of RAM. The compute target for the deployed service must initialize automatically while minimizing cost and administrative overhead.

Which compute target should you use?

44. You register a model that you plan to use in a batch inference pipeline.

The batch inference pipeline must use a ParallelRunStep step to process files in a file dataset. The script has the ParallelRunStep step runs must process six input files each time the inferencing function is called.

You need to configure the pipeline.

Which configuration setting should you specify in the ParallelRunConfig object for the PrallelRunStep step?

45. HOTSPOT

You plan to use Hyperdrive to optimize the hyperparameters selected when training a model.

You create the following code to define options for the hyperparameter experiment:

For each of the following statements, select Yes if the statement is true. Otherwise, select No. NOTE: Each correct selection is worth one point.

46. You deploy a real-time inference service for a trained model.

The deployed model supports a business-critical application, and it is important to be able to monitor the data submitted to the web service and the predictions the data generates.

You need to implement a monitoring solution for the deployed model using minimal administrative effort.

What should you do?

47. HOTSPOT

You are preparing to build a deep learning convolutional neural network model for image classification.

You create a script to train the model using CUDA devices.

You must submit an experiment that runs this script in the Azure Machine Learning workspace.

The following compute resources are available:

- a Microsoft Surface device on which Microsoft Office has been installed. Corporate IT policies prevent the installation of additional software

- a Compute Instance named ds-workstation in the workspace with 2 CPUs and 8 GB of memory

- an Azure Machine Learning compute target named cpu-cluster with eight CPU-based nodes

- an Azure Machine Learning compute target named gpu-cluster with four CPU and GPU-based nodes

You need to specify the compute resources to be used for running the code to submit the experiment, and for running the script in order to minimize model training time.

Which resources should the data scientist use? To answer, select the appropriate options in the answer area. NOTE: Each correct selection is worth one point.

48. You create an Azure Machine Learning workspace. You are preparing a local Python environment on a laptop computer. You want to use the laptop to connect to the workspace and run experiments.

You create the following config.json file.

You must use the Azure Machine Learning SDK to interact with data and experiments in the workspace.

You need to configure the config.json file to connect to the workspace from the Python environment.

Which two additional parameters must you add to the config.json file in order to connect to the workspace? Each correct answer presents part of the solution. NOTE: Each correct selection is worth one point.

49. HOTSPOT

You are using Azure Machine Learning to train machine learning models. You need to compute target on which to remotely run the training script.

You run the following Python code:

For each of the following statements, select Yes if the statement is true. Otherwise, select No. NOTE: Each correct selection is worth one point.

50. You create an Azure Machine Learning compute resource to train models.

The compute resource is configured as follows:

- Minimum nodes: 2

- Maximum nodes: 4

You must decrease the minimum number of nodes and increase the maximum number of nodes to the following values:

- Minimum nodes: 0

- Maximum nodes: 8

You need to reconfigure the compute resource.

What are three possible ways to achieve this goal? Each correct answer presents a complete solution. NOTE: Each correct selection is worth one point.

51. HOTSPOT

You have an Azure blob container that contains a set of TSV files. The Azure blob container is registered as a datastore for an Azure Machine Learning service workspace. Each TSV file uses the same data schema.

You plan to aggregate data for all of the TSV files together and then register the aggregated data as a dataset in an Azure Machine Learning workspace by using the Azure Machine Learning SDK for Python.

You run the following code.

For each of the following statements, select Yes if the statement is true. Otherwise, select No. NOTE: Each correct selection is worth one point.

52. You create a new Azure subscription. No resources are provisioned in the subscription.

You need to create an Azure Machine Learning workspace.

What are three possible ways to achieve this goal? Each correct answer presents a complete solution. NOTE: Each correct selection is worth one point.

53. HOTSPOT

You use Azure Machine Learning to train and register a model.

You must deploy the model into production as a real-time web service to an inference cluster named service-compute that the IT department has created in the Azure Machine Learning workspace.

Client applications consuming the deployed web service must be authenticated based on their Azure Active Directory service principal.

You need to write a script that uses the Azure Machine Learning SDK to deploy the model. The necessary modules have been imported.

How should you complete the code? To answer, select the appropriate options in the answer area. NOTE: Each correct selection is worth one point.

54. 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?

55. You create a batch inference pipeline by using the Azure ML SDK.

You configure the pipeline parameters by executing the following code:

You need to obtain the output from the pipeline execution.

Where will you find the output?

56. You use Azure Machine Learning designer to create a real-time service endpoint. You have a single Azure Machine Learning service compute resource.

You train the model and prepare the real-time pipeline for deployment.

You need to publish the inference pipeline as a web service.

Which compute type should you use?

57. DRAG DROP

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

The model must be retrained monthly with the new image data fetched from a public web portal. You create an Azure Machine Learning pipeline to fetch new data, standardize the size of images, and retrain the model.

You need to use the Azure Machine Learning SDK to configure the schedule for the pipeline.

Which four 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.

58. HOTSPOT

You create a script for training a machine learning model in Azure Machine Learning service.

You create an estimator by running the following code:

For each of the following statements, select Yes if the statement is true. Otherwise, select No. NOTE: Each correct selection is worth one point.

59. 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?

60. You write a Python script that processes data in a comma-separated values (CSV) file.

You plan to run this script as an Azure Machine Learning experiment.

The script loads the data and determines the number of rows it contains using the following code:

You need to record the row count as a metric named row_count that can be returned using the get_metrics method of the Run object after the experiment run completes.

Which code should you use?