2022 Latest Free Microsoft DP-100 Sample Questions & Answers

test2022-03-25T03:42:35+00:00

Now you can get the most update DP-100 questions and answers to practice for your Designing and Implementing a Data Science Solution on Azure exam. You’ve come to the correct place if you’re preparing for the DP-100 test and want to get ahead. Our experienced IT lecturers produced DP-100 exam dumps, which includes exam questions and answers and contains the most recent Microsoft DP-100 exam questions which ensure you prepare the DP-100 exam easily.

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1. You use the Azure Machine Learning service to create a tabular dataset named training.data. You plan to use this dataset in a training script.

You create a variable that references the dataset using the following code:

training_ds = workspace.datasets.get("training_data")

You define an estimator to run the script.

You need to set the correct property of the estimator to ensure that your script can access the training.data dataset

Which property should you set?

inputs = [training_ds.as_named_input('training_ds')]

script_params = {"--training_ds":training_ds}

environment_definition = {"training_data":training_ds}

source_directory = training_ds

2. You are creating a classification model for a banking company to identify possible instances of credit card fraud. You plan to create the model in Azure Machine Learning by using automated machine learning.

The training dataset that you are using is highly unbalanced.

You need to evaluate the classification model.

Which primary metric should you use?

normalized_mean_absolute_error

[spearman_correlation

weighted

accuracy

normalized_root_mean_squared_error

3. DRAG DROP

You need to define an evaluation strategy for the crowd sentiment models.

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.

4. You are building a machine learning model for translating English language textual content into French

language textual content.

You need to build and train the machine learning model to learn the sequence of the textual content.

Which type of neural network should you use?

Multilayer Perceptions (MLPs)

Convolutional Neural Networks (CNNs)

Recurrent Neural Networks (RNNs)

Generative Adversarial Networks (GANs)

5. You retrain an existing model.

You need to register the new version of a model while keeping the current version of the model in the registry.

What should you do?

Save the new model in the default datastore with the same name as the existing model. Do not register the new model.

Delete the existing model and register the new one with the same name.

6. HOTSPOT

You create an Azure Databricks workspace and a linked Azure Machine Learning workspace.

You have the following Python code segment in the Azure Machine Learning workspace:

import mlflow

import mlflow.azureml

import azureml.mlflow

import azureml.core

from azureml.core import Workspace

subscription_id = 'subscription_id'

resourse_group = 'resource_group_name'

workspace_name = 'workspace_name'

ws = Workspace.get(name=workspace_name, subscription_id=subscription_id, resource_group=resource_group)

experimentName = "/Users/{user_name}/{experiment_folder}/{experiment_name}" mlflow.set_experiment(experimentName)

uri = ws.get_mlflow_tracking_uri()

mlflow.set_tracking_uri(uri)

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

7. You train and register a machine learning model. You create a batch inference pipeline that uses the model to generate predictions from multiple data files.

You must publish the batch inference pipeline as a service that can be scheduled to run every night.

You need to select an appropriate compute target for the inference service.

Which compute target should you use?

Azure Machine Learning compute instance

Azure Machine Learning compute cluster

Azure Kubernetes Service (AKS)-based inference cluster

Azure Container Instance (ACI) compute target

8. You create a machine learning model by using the Azure Machine Learning designer. You publish the model as a real-time service on an Azure Kubernetes Service (AKS) inference compute cluster. You make no changes to the deployed endpoint configuration.

You need to provide application developers with the information they need to consume the endpoint.

Which two values should you provide to application developers? Each correct answer presents part of the solution. NOTE: Each correct selection is worth one point.

The name of the AKS cluster where the endpoint is hosted.

The name of the inference pipeline for the endpoint.

The URL of the endpoint.

The run ID of the inference pipeline experiment for the endpoint.

The key for the endpoint.

9. You are building a binary classification model by using a supplied training set.

The training set is imbalanced between two classes.

You need to resolve the data imbalance.

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

Penalize the classification

Resample the data set using under sampling or oversampling

Generate synthetic samples in the minority class.

Use accuracy as the evaluation metric of the model.

Normalize the training feature set.

10. HOTSPOT

A biomedical research company plans to enroll people in an experimental medical treatment trial.

You create and train a binary classification model to support selection and admission of patients to the trial. The model includes the following features: Age, Gender, and Ethnicity.

The model returns different performance metrics for people from different ethnic groups.

You need to use Fairlearn to mitigate and minimize disparities for each category in the Ethnicity feature.

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

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11. 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?

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

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

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

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

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.

12. 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 create an Azure Machine Learning service datastore in a workspace.

The datastore contains the following files:

• /data/2018/Q1.csv

• /data/2018/Q2.csv

• /data/2018/Q3.csv

• /data/2018/Q4.csv

• /data/2019/Q1.csv

All files store data in the following format:

id,f1,f2i

1,1.2,0

2,1,1,

1 3,2.1,0

You run the following code:

You need to create a dataset named training_data and load the data from all files into a single data frame by using the following code:

Solution: Run the following code:

Does the solution meet the goal?

Yes

13. 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 analyzing a numerical dataset which contains missing values in several columns.

You must clean the missing values using an appropriate operation without affecting the dimensionality of the feature set.

You need to analyze a full dataset to include all values.

Solution: Replace each missing value using the Multiple Imputation by Chained Equations (MICE) method.

Does the solution meet the goal?

Yes

14. HOTSPOT

You need to identify the methods for dividing the data according to the testing requirements.

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

15. 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:

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?

Yes

16. 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 have a Python script named train.py in a local folder named scripts. The script trains a regression model by using scikit-learn. The script includes code to load a training data file which is also located in the scripts folder.

You must run the script as an Azure ML experiment on a compute cluster named aml-compute.

You need to configure the run to ensure that the environment includes the required packages for model training. You have instantiated a variable named aml-compute that references the target compute cluster.

Solution: Run the following code:

Does the solution meet the goal?

Yes

17. DRAG DROP

You have a model with a large difference between the training and validation error values.

You must create a new model and perform cross-validation.

You need to identify a parameter set for the new model using Azure Machine Learning Studio.

Which module you should use for each step? To answer, drag the appropriate modules to the correct steps. Each module may be used once or 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.

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 plan to use a Python script to run an Azure Machine Learning experiment. The script creates a reference to the experiment run context, loads data from a file, identifies the set of unique values for the label column, and completes the experiment run:

The experiment must record the unique labels in the data as metrics for the run that can be reviewed later.

You must add code to the script to record the unique label values as run metrics at the point indicated by the comment.

Solution: Replace the comment with the following code:

run.log_list('Label Values', label_vals)

Does the solution meet the goal?

Yes

19. HOTSPOT

You create a Python script named train.py and save it in a folder named scripts. The script uses the scikit-learn framework to train a machine learning model.

You must run the script as an Azure Machine Learning experiment on your local workstation.

You need to write Python code to initiate an experiment that runs the train.py script.

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.

20. HOTSPOT

Your Azure Machine Learning workspace has a dataset named real_estate_data.

A sample of the data in the dataset follows.

You want to use automated machine learning to find the best regression model for predicting the price column.

You need to configure an automated machine learning experiment using the Azure Machine Learning SDK.

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

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21. You use the Azure Machine Learning designer to create and run a training pipeline. You then create a real-time inference pipeline.

You must deploy the real-time inference pipeline as a web service.

What must you do before you deploy the real-time inference pipeline?

Run the real-time inference pipeline.

Create a batch inference pipeline.

Clone the training pipeline.

Create an Azure Machine Learning compute cluster.

22. HOTSPOT

You collect data from a nearby weather station.

You have a pandas dataframe named weather_df that includes the following data:

The data is collected every 12 hours: noon and midnight.

You plan to use automated machine learning to create a time-series model that predicts temperature over the next seven days. For the initial round of training, you want to train a maximum of 50 different models.

You must use the Azure Machine Learning SDK to run an automated machine learning experiment to train these models.

You need to configure the automated machine learning run.

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

23. 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.

24. HOTSPOT

You need to replace the missing data in the Accessibility To High way columns.

How should you configure the Clean Missing Data module? To answer, select the appropriate options in the answer area. NOTE: Each correct selection is worth one point.

25. 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 analyzing a numerical dataset which contains missing values in several columns.

You must clean the missing values using an appropriate operation without affecting the dimensionality of the feature set.

You need to analyze a full dataset to include all values.

Solution: Calculate the column median value and use the median value as the replacement for any missing value in the column.

Does the solution meet the goal?

Yes

26. HOTSPOT

You write code to retrieve an experiment that is run from your Azure Machine Learning workspace.

The run used the model interpretation support in Azure Machine Learning to generate and upload a model explanation.

Business managers in your organization want to see the importance of the features in the model.

You need to print out the model features and their relative importance in an output that looks similar to the following.

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

27. You train a model and register it in your Azure Machine Learning workspace. You are ready to deploy the model as a real-time web service.

You deploy the model to an Azure Kubernetes Service (AKS) inference cluster, but the deployment fails because an error occurs when the service runs the entry script that is associated with the model deployment.

You need to debug the error by iteratively modifying the code and reloading the service, without requiring a re-deployment of the service for each code update.

What should you do?

Register a new version of the model and update the entry script to load the new version of the model from its registered path.

Modify the AKS service deployment configuration to enable application insights and re-deploy to AK

Create an Azure Container Instances (ACI) web service deployment configuration and deploy the model on AC

Add a breakpoint to the first line of the entry script and redeploy the service to AK

Create a local web service deployment configuration and deploy the model to a local Docker container.

28. HOTSPOT

You are working on a classification task. You have a dataset indicating whether a student would like to play soccer and associated attributes.

The dataset includes the following columns:

You need to classify variables by type.

Which variable should you add to each category? To answer, select the appropriate options in the answer area. NOTE: Each correct selection is worth one point.

29. 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 have a Python script named train.py in a local folder named scripts. The script trains a regression model by using scikit-learn. The script includes code to load a training data file which is also located in the scripts folder.

You must run the script as an Azure ML experiment on a compute cluster named aml-compute.

You need to configure the run to ensure that the environment includes the required packages for model training. You have instantiated a variable named aml-compute that references the target compute cluster.

Solution: Run the following code:

Does the solution meet the goal?

Yes

30. : 212

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?

process_count_per_node= "6"

node_count= "6"

mini_batch_size= "6"

error_threshold= "6"

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31. DRAG DROP

You have an Azure Machine Learning workspace that contains a CPU-based compute cluster and an Azure Kubernetes Services (AKS) inference cluster. You create a tabular dataset containing data that you plan to use to create a classification model.

You need to use the Azure Machine Learning designer to create a web service through which client applications can consume the classification model by submitting new data and getting an immediate prediction as a response.

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.

32. You use an Azure Machine Learning workspace.

You have a trained model that must be deployed as a web service. Users must authenticate by using Azure Active Directory.

What should you do?

Deploy the model to Azure Kubernetes Service (AKS). During deployment, set the token_auth_enabled parameter of the target configuration object to true

Deploy the model to Azure Container Instances. During deployment, set the auch_enabled parameter of the target configuration object to true

Deploy the model to Azure Container Instances. During deployment, set the coken_auch_enabled parameter of the target configuration object to true

Deploy the model to Azure Kubernetes Service (AKS). During deployment, set the auch. enabled parameter of the target configuration object to true

33. DRAG DROP

You need to correct the model fit issue.

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.

34. You are creating a binary classification by using a two-class logistic regression model.

You need to evaluate the model results for imbalance.

Which evaluation metric should you use?

Relative Absolute Error

AUC Curve

Mean Absolute Error

Relative Squared Error

35. You need to select a feature extraction method.

Which method should you use?

Mutual information

Mood’s median test

Kendall correlation

Permutation Feature Importance

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?

Save the model locally as a.pt file, and deploy the model as a local web service.

Deploy the model on computer that is configured to use the default Azure Machine Learning conda environment.

37. 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 plan to use a Python script to run an Azure Machine Learning experiment.

The script creates a reference to the experiment run context, loads data from a file, identifies the set of unique values for the label column, and completes the experiment run:

from azureml.core import Run

import pandas as pd

run = Run.get_context()

data = pd.read_csv('data.csv')

label_vals = data['label'].unique()

# Add code to record metrics here run.complete()

The experiment must record the unique labels in the data as metrics for the run that can be reviewed later.

You must add code to the script to record the unique label values as run metrics at the point indicated by the comment.

Solution: Replace the comment with the following code:

run.upload_file('outputs/labels.csv', './data.csv')

Does the solution meet the goal?

Yes

38. HOTSPOT

You are using the Azure Machine Learning Service to automate hyperparameter exploration of your neural network classification model.

You must define the hyperparameter space to automatically tune hyperparameters using random sampling according to following requirements:

✑ The learning rate must be selected from a normal distribution with a mean value of 10 and a standard deviation of 3.

✑ Batch size must be 16, 32 and 64.

✑ Keep probability must be a value selected from a uniform distribution between the range of 0.05 and 0.1.

You need to use the param_sampling method of the Python API for the Azure Machine Learning Service.

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.

39. 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?

Split Data

Load Trained Model

Assign Data to Clusters

Group Data into Bins

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.

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?

Yes

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41. 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.

42. You are performing feature engineering on a dataset.

You must add a feature named CityName and populate the column value with the text London.

You need to add the new feature to the dataset.

Which Azure Machine Learning Studio module should you use?

Edit Metadata

Preprocess Text

Execute Python Script

Latent Dirichlet Allocation

43. You use the Azure Machine Learning SDK in a notebook to run an experiment using a script file in an experiment folder.

The experiment fails.

You need to troubleshoot the failed experiment.

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

Use the get_metrics() method of the run object to retrieve the experiment run logs.

Use the get_details_with_logs() method of the run object to display the experiment run logs.

View the log files for the experiment run in the experiment folder.

View the logs for the experiment run in Azure Machine Learning studio.

Use the get_output() method of the run object to retrieve the experiment run logs.

44. You are planning to register a trained model in an Azure Machine Learning workspace.

You must store additional metadata about the model in a key-value format. You must be able to add new metadata and modify or delete metadata after creation.

You need to register the model.

Which parameter should you use?

description

model_framework

cags

properties

45. HOTSPOT

You create a binary classification model to predict whether a person has a disease.

You need to detect possible classification errors.

Which error type should you choose for each description? To answer, select the appropriate options in the answer area. NOTE: Each correct selection is worth one point.

46. You run an automated machine learning experiment in an Azure Machine Learning workspace.

Information about the run is listed in the table below:

You need to write a script that uses the Azure Machine Learning SDK to retrieve the best iteration of the experiment run .

Which Python code segment should you use?

A)

Option A

Option B

Option C

Option D

47. 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?

attached Azure Databricks cluster

Azure Container Instance (ACI)

Azure Kubernetes Service (AKS) inference cluster

Azure Machine Learning Compute Instance

attached virtual machine in a different region

48. You use the following code to define the steps for a pipeline:

from azureml.core import Workspace, Experiment, Run from azureml.pipeline.core import Pipeline

from azureml.pipeline.steps import PythonScriptStep

ws = Workspace.from_config()

. . .

step1 = PythonScriptStep(name="step1", ...)

step2 = PythonScriptsStep(name="step2", ...)

pipeline_steps = [step1, step2]

You need to add code to run the steps.

Which two code segments can you use to achieve this goal? Each correct answer presents a complete solution. NOTE: Each correct selection is worth one point.

experiment = Experiment(workspace=ws, name='pipeline-experiment') run = experiment.submit(config=pipeline_steps)

run = Run(pipeline_steps)

pipeline = Pipeline(workspace=ws, steps=pipeline_steps) experiment = Experiment(workspace=ws, name='pipeline-experiment') run = experiment.submit(pipeline)

pipeline = Pipeline(workspace=ws, steps=pipeline_steps) run = pipeline.submit(experiment_name='pipeline-experiment')

49. You have a Python script that executes a pipeline. The script includes the following code:

from azureml.core import Experiment

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

You want to test the pipeline before deploying the script.

You need to display the pipeline run details written to the STDOUT output when the pipeline completes.

Which code segment should you add to the test script?

pipeline_run.get.metrics()

pipeline_run.wait_for_completion(show_output=True)

pipeline_param = PipelineParameter(name="stdout", default_value="console")

pipeline_run.get_status()

50. 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 creating a new experiment in Azure Machine Learning Studio.

One class has a much smaller number of observations than tin- other classes in the training set.

You need to select an appropriate data sampling strategy to compensate for the class imbalance.

Solution: You use the Principal Components Analysis (PCA) sampling mode.

Does the solution meet the goal?

Yes

Page 6 of 7

51. You are analyzing a dataset containing historical data from a local taxi company. You arc developing a regression 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.

an F1 score that is high

an R Squared value dose to 1

an R-Squared value close to 0

a Root Mean Square Error value that is high

a Root Mean Square Error value that is low

an F 1 score that is low.

52. You use the Azure Machine Learning designer to create and run a training pipeline.

The pipeline must be run every night to inference predictions from a large volume of files.

The folder where the files will be stored is defined as a dataset.

You need to publish the pipeline as a REST service that can be used for the nightly inferencing run.

What should you do?

Create a batch inference pipeline

Set the compute target for the pipeline to an inference cluster

Create a real-time inference pipeline

Clone the pipeline

53. You run an experiment that uses an AutoMLConfig class to define an automated machine learning task with a maximum of ten model training iterations. The task will attempt to find the best performing model based on a metric named accuracy.

You submit the experiment with the following code:

You need to create Python code that returns the best model that is generated by the automated machine learning task .

Which code segment should you use?

best_model = automl_run.get_details()

best_model = automl_run.get_output()[1]

best_model = automl_run.get_file_names()[1]

best_model = automl_run.get_metrics()

54. HOTSPOT

You are retrieving data from a large datastore by using Azure Machine Learning Studio.

You must create a subset of the data for testing purposes using a random sampling seed based on the system clock.

You add the Partition and Sample module to your experiment.

You need to select the properties for the module.

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

55. 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?

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 namesales_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.

Create a tabular dataset that references the datastore and specifies the path 'sales/*/sales.csv', register the dataset with the namesales_dataset and a tag named month indicating the month and year it was registered, and use this dataset for all experiments.

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.

Create a tabular dataset that references the datastore and explicitly specifies each 'sales/ mm-yyyy / sales.csv' file. Register the dataset with the namesales_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.

56. HOTSPOT

You are using a decision tree algorithm. You have trained a model that generalizes well at a tree depth equal to 10.

You need to select the bias and variance properties of the model with varying tree depth values.

Which properties should you select for each tree depth? To answer, select the appropriate options in the answer area.

57. 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?

Intel Software Guard Extensions (Intel SGX) technology

Solid State Drives (SSD)

Graphic Processing Unit (GPU)

Computer Processing Unit (CPU) speed increase by using overcloking

High Random Access Memory (RAM) configuration

58. 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?

Build the environment in Apache Hive for HDInsight and use Azure Data Factory for orchestration.

Build the environment in Azure Databricks and use Azure Data Factory for orchestration.

Build the environment in Apache Spark for HDInsight and use Azure Container Instances for orchestration.

Build the environment in Azure Databricks and use Azure Container Instances for orchestration.

59. You have a Jupyter Notebook that contains Python code that is used to train a model.

You must create a Python script for the production deployment. The solution must minimize code maintenance.

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

Refactor the Jupyter Notebook code into functions

Save each function to a separate Python file

Define a main() function in the Python script

Remove all comments and functions from the Python script

60. HOTSPOT

You create a new Azure Databricks workspace.

You configure a new cluster for long-running tasks with mixed loads on the compute cluster as shown in the image below.

Use the drop-down menus to select the answer choice that completes each statement based on the information presented in the graphic. NOTE: Each correct selection is worth one point.

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61. DRAG DROP

You need to define an evaluation strategy for the crowd sentiment models.

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.

62. 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 creating a model to predict the price of a student’s artwork depending on the following variables: the student’s length of education, degree type, and art form.

You start by creating a linear regression model.

You need to evaluate the linear regression model.

Solution: Use the following metrics: Accuracy, Precision, Recall, F1 score and AUC.

Does the solution meet the goal?

Yes

63. 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.