Microsoft Azure Test 14

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Welcome to your Microsoft Azure Test-14

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Phone

Q1. You have the following code. The code prepares an experiment to run a script:

The experiment must be run on local computer using the default environment. You need to add code to start the experiment and run the script. Which code segment should you use?

Select 1 option(s):

A. run = script_experiment.start_logging()

B. run = Run(experiment=script_experiment)

C. ws.get_run(run_id=experiment.id)

D. run = script_experiment.submit(config=script_config)

Q2. You plan to run a Python script as an Azure Machine Learning experiment. The script contains the following code:

You must specify a file dataset as an input to the script. The dataset consists of multiple large image files and must be streamed directly from its source. You need to write code to define a ScriptRunConfig object for the experiment and pass the ds dataset as an argument. Which code segment should you use?

Select 1 option(s):

A. arguments = [‘--input-data‘, ds.to_pandas_dataframe()]

B. arguments = [‘--input-data‘, ds.as_mount()]

C. arguments = [‘--data-data‘, ds]

D. arguments = [‘--input-data‘, ds.as_download()]

Q3. You create a Python script that runs a training experiment in Azure Machine Learning. The script uses the Azure Machine Learning SDK for Python.You must add a statement that retrieves the names of the logs and outputs generated by the script.You need to reference a Python class object from the SDK for the statement.Which class object should you use?

Select 1 option(s):

A. Run

B. ScriptRunConfig

C. Workspace

D. Experiment

Q4. You create and register a model in an Azure Machine Learning workspace.You must use the Azure Machine Learning SDK to implement a batch inference pipeline that uses a ParallelRunStep to score input data using the model. You must specify a value for the ParallelRunConfig compute_target setting of the pipeline step.You need to create the compute target.Which class should you use?

Select 1 option(s):

A. BatchCompute

B. AdlaCompute

C. AmlCompute

D. AksCompute

Q5. You run a script as an experiment in Azure Machine Learning.You have a Run object named run that references the experiment run. You must review the log files that were generated during the experiment run.You need to download the log files to a local folder for review.Which two code segments can you run to achieve this goal? Each correct answer presents a complete solution.NOTE: Each correct selection is worth one point.

Select 2 option(s):

A. run.get_details()

B. run.get_file_names()

C. run.get_metrics()

D. run.download_files(output_directory=‘./runfiles‘)

E. run.get_all_logs(destination=‘./runlogs‘)

Q6. 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.

Select 2 option(s):

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

B. run = Run(pipeline_steps)

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

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

Q7. 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.

Select 2 option(s):

A. Refactor the Jupyter Notebook code into functions

B. Save each function to a separate Python file

C. Define a main() function in the Python script

D. Remove all comments and functions from the Python script

Q8. 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?

Select 1 option(s):

A. Yes

B. No

Q9. 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?

Select 1 option(s):

A. service.get_logs()

B. service.state

C. service.serialize()

D. service.update_deployment_state()

Q10. 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?

Select 1 option(s):

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.

Q11. 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.

Select 2 option(s):

A. run(mini_batch)

B. main()

C. batch()

D. init()

E. score(mini_batch)

Q12. 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?

Select 1 option(s):

A. Azure Container Instance

B. Azure Kubernetes Service

C. Field Programmable Gate Array

D. Machine Learning Compute

Q13. 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.

Select 2 option(s):

A. Run the following code in a notebook:

B. Use the Inference Clusters tab in Machine Learning Studio.

C. Use the Activity log in the Azure portal for the Machine Learning workspace.

D. Run the following code in a notebook:

E. Run the following code and monitor the console output from the PipelineRun object:

Q14. 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?

Select 1 option(s):

A. Azure Container Instance (ACI)

B. attached Azure Databricks cluster

C. Azure Kubernetes Service (AKS) inference cluster

D. Azure Machine Learning compute cluster

Q15. 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?

Select 1 option(s):

A. process_count_per_node= “6“

B. node_count= “6“

C. mini_batch_size= “6“

D. error_threshold= “6“

Q16. 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?

Select 1 option(s):

A. View the explanations for the registered model in Azure ML studio.

B. Enable Azure Application Insights for the service endpoint and view logged data in the Azure portal.

C. View the log files generated by the experiment used to train the model.

D. Create an ML Flow tracking URI that references the endpoint, and view the data logged by ML Flow.

Q17. 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?

Select 1 option(s):

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

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

C. Add a breakpoint to the first line of the entry script and redeploy the service to AKS.

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

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

Q18. 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?

Select 1 option(s):

A. Run the real-time inference pipeline.

B. Create a batch inference pipeline.

C. Clone the training pipeline.

D. Create an Azure Machine Learning compute cluster.

Q19. 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 train and register a machine learning model.You plan to deploy the model as a real-time web service. Applications must use key-based authentication to use the model.You need to deploy the web service.Solution:Create an AciWebservice instance.Set the value of the auth_enabled property to True.Deploy the model to the service.Does the solution meet the goal?

Select 1 option(s):

A. Yes

B. No

Q20. You are a data scientist working for a hotel booking website company. You use the Azure Machine Learning service to train a model that identifies fraudulent transactions.You must deploy the model as an Azure Machine Learning real-time web service using the Model.deploy method in the Azure Machine Learning SDK. The deployed web service must return real-time predictions of fraud based on transaction data input.You need to create the script that is specified as the entry_script parameter for the InferenceConfig class used to deploy the model.What should the entry script do?

Select 1 option(s):

A. Register the model with appropriate tags and properties.

B. Create a Conda environment for the web service compute and install the necessary Python packages.

C. Load the model and use it to predict labels from input data.

D. Start a node on the inference cluster where the web service is deployed.

E. Specify the number of cores and the amount of memory required for the inference compute.

Q21. 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?

Select 1 option(s):

A. 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.

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

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

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

Q22. You use Azure Machine Learning designer to create a training pipeline for a regression model. You need to prepare the pipeline for deployment as an endpoint that generates predictions asynchronously for a dataset of input data values. What should you do?

Select 1 option(s):

A. Clone the training pipeline.

B. Create a batch inference pipeline from the training pipeline.

C. Create a real-time inference pipeline from the training pipeline.

D. Replace the dataset in the training pipeline with an Enter Data Manually module.

Q23. 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 train and register a machine learning model.You plan to deploy the model as a real-time web service. Applications must use key-based authentication to use the model.You need to deploy the web service.Solution:Create an AciWebservice instance.Set the value of the auth_enabled property to False.Set the value of the token_auth_enabled property to True.Deploy the model to the service.Does the solution meet the goal?

Select 1 option(s):

A. Yes

B. No

Q24. 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.

Select 2 option(s):

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.

Q25. 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 train and register a machine learning model.You plan to deploy the model as a real-time web service. Applications must use key-based authentication to use the model.You need to deploy the web service.Solution:Create an AciWebservice instance.Set the value of the ssl_enabled property to True.Deploy the model to the service.Does the solution meet the goal?

Select 1 option(s):

A. Yes

B. No

Q26. You use the following Python code in a notebook to deploy a model as a web service: from azureml.core.webservice import AciWebservice from azureml.core.model import InferenceConfig inference_config = InferenceConfig(runtime=‘python‘, source_directory=‘model_files‘, entry_script=‘score.py‘, conda_file=‘env.yml‘) deployment_config = AciWebservice.deploy_configuration(cpu_cores=1, memory_gb=1) service = Model.deploy(ws, ‘my-service‘, [model], inference_config, deployment_config) service.wait_for_deployment(True)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?

Select 1 option(s):

A. service.state

B. service.get_logs()

C. service.serialize()

D. service.environment

Q27. You use the Azure Machine Learning Python SDK to define a pipeline that consists of multiple steps.When you run the pipeline, you observe that some steps do not run. The cached output from a previous run is used instead.You need to ensure that every step in the pipeline is run, even if the parameters and contents of the source directory have not changed since the previous run.What are two possible ways to achieve this goal? Each correct answer presents a complete solution.NOTE: Each correct selection is worth one point.

Select 2 option(s):

A. Use a PipelineData object that references a datastore other than the default datastore.

B. Set the regenerate_outputs property of the pipeline to True.

C. Set the allow_reuse property of each step in the pipeline to False.

D. Restart the compute cluster where the pipeline experiment is configured to run.

E. Set the outputs property of each step in the pipeline to True.

Q28. You use the designer to create a training pipeline for a classification model. The pipeline uses a dataset that includes the features and labels required for model training.You create a real-time inference pipeline from the training pipeline. You observe that the schema for the generated web service input is based on the dataset and includes the label column that the model predicts. Client applications that use the service must not be required to submit this value.You need to modify the inference pipeline to meet the requirement.What should you do?

Select 1 option(s):

A. Add a Select Columns in Dataset module to the inference pipeline after the dataset and use it to select all columns other than the label.

B. Delete the dataset from the training pipeline and recreate the real-time inference pipeline.

C. Delete the Web Service Input module from the inference pipeline.

D. Replace the dataset in the inference pipeline with an Enter Data Manually module that includes data for the feature columns but not the label column.

Q29. 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?

Select 1 option(s):

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

Q30. 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?

Select 1 option(s):

A. a new Machine Learning Compute resource

B. Azure Kubernetes Services

C. HDInsight

D. the existing Machine Learning Compute resource

E. Azure Databricks

Q31. 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?

Select 1 option(s):

A. Register a model with a different name from the existing model and a custom property named version with the value 2.

B. Register the model with the same name as the existing model.

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

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

Q32. You create an Azure Machine Learning workspace named ML-workspace. You also create an Azure Databricks workspace named DB-workspace. DB-workspace contains a cluster named DB-cluster.You must use DB-cluster to run experiments from notebooks that you import into DB-workspace.You need to use ML-workspace to track MLflow metrics and artifacts generated by experiments running on DB-cluster. The solution must minimize the need for custom code.What should you do?

Select 1 option(s):

A. From DB-cluster, configure the Advanced Logging option.

B. From DB-workspace, configure the Link Azure ML workspace option.

C. From ML-workspace, create an attached compute.

D. From ML-workspace, create a compute cluster.

Q33. 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 train a classification model by using a logistic regression algorithm.You must be able to explain the model‘s predictions by calculating the importance of each feature, both as an overall global relative importance value and as a measure of local importance for a specific set of predictions.You need to create an explainer that you can use to retrieve the required global and local feature importance values.Solution: Create a MimicExplainer.Does the solution meet the goal?

Select 1 option(s):

A. Yes

B. No

Q34. 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?

Select 1 option(s):

A. Azure Machine Learning compute instance

B. Azure Machine Learning compute cluster

C. Azure Kubernetes Service (AKS)-based inference cluster

D. Azure Container Instance (ACI) compute target

Q35. 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?

Select 1 option(s):

A. Create a batch inference pipeline

B. Set the compute target for the pipeline to an inference cluster

C. Create a real-time inference pipeline

D. Clone the pipeline

Q36. 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?

Select 1 option(s):

A. pipeline_run.get.metrics()

B. pipeline_run.wait_for_completion(show_output=True)

C. pipeline_param = PipelineParameter(name=“stdout“, default_value=“console“)

D. pipeline_run.get_status()

Q37. You are a data scientist working for a bank and have used Azure ML to train and register a machine learning model that predicts whether a customer is likely to repay a loan.You want to understand how your model is making selections and must be sure that the model does not violate government regulations such as denying loans based on where an applicant lives.You need to determine the extent to which each feature in the customer data is influencing predictions.What should you do?

Select 1 option(s):

A. Enable data drift monitoring for the model and its training dataset.

B. Score the model against some test data with known label values and use the results to calculate a confusion matrix.

C. Use the Hyperdrive library to test the model with multiple hyperparameter values.

D. Use the interpretability package to generate an explainer for the model.

E. Add tags to the model registration indicating the names of the features in the training dataset.

Q38. 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?

Select 1 option(s):

A. description

B. model_framework

C. tags

D. properties

Q39. 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 train a classification model by using a logistic regression algorithm.You must be able to explain the model‘s predictions by calculating the importance of each feature, both as an overall global relative importance value and as a measure of local importance for a specific set of predictions.You need to create an explainer that you can use to retrieve the required global and local feature importance values.Solution: Create a TabularExplainer.Does the solution meet the goal?

Select 1 option(s):

A. Yes

B. No

Q40. 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 train a classification model by using a logistic regression algorithm.You must be able to explain the model‘s predictions by calculating the importance of each feature, both as an overall global relative importance value and as a measure of local importance for a specific set of predictions.You need to create an explainer that you can use to retrieve the required global and local feature importance values.Solution: Create a PFIExplainer.Does the solution meet the goal?

Select 1 option(s):

A. Yes

B. No

Q41. You are determining if two sets of data are significantly different from one another by using Azure Machine Learning Studio.Estimated values in one set of data may be more than or less than reference values in the other set of data. You must produce a distribution that has a constantType I error as a function of the correlation.You need to produce the distribution.Which type of distribution should you produce?

Select 1 option(s):

A. Unpaired t-test with a two-tail option

B. Unpaired t-test with a one-tail option

C. Paired t-test with a one-tail option

D. Paired t-test with a two-tail option

Q42. You develop a machine learning project on a local machine. The project uses the Azure Machine Learning SDK for Python. You use Git as version control for scripts.You submit a training run that returns a Run object.You need to retrieve the active Git branch for the training run.Which two code segments should you use? Each correct answer presents part of the solution.NOTE: Each correct selection is worth one point.

Select 2 option(s):

A. details = run.get_environment()

B. details.properties[‘azureml.git.branch‘]

C. details.properties[‘azureml.git.commit‘]

D. details = run.get_details()

Q43. You have an Azure Machine Learning workspace named workspace1.You must add a datastore that connects an Azure Blob storage container to workspace1. You must be able to configure a privilege level.You need to configure authentication.Which authentication method should you use?

Select 1 option(s):

A. Service principal

B. Account key

C. SAS token

D. Managed identity

Q44. You are attaching an Azure Databricks-based compute resource to an Azure Machine Learning development workspace.You need to configure parameters to attach the resource.Which three parameters should you use? Each correct answer presents part of the solution.NOTE: Each correct selection is worth one point.

Select 3 option(s):

A. Workspace name

B. Compute name

C. Workspace user credentials

D. Workspace resource ID

E. Access token

Q45. You are a data scientist building a deep convolutional neural network (CNN) for image classification.The CNN model you build shows signs of overfitting.You need to reduce overfitting and converge the model to an optimal fit.Which two actions should you perform? Each correct answer presents a complete solution.NOTE: Each correct selection is worth one point.

Select 2 option(s):

A. Add an additional dense layer with 512 input units.

B. Add L1/L2 regularization.

C. Use training data augmentation.

D. Reduce the amount of training data.

E. Add an additional dense layer with 64 input units.

Q46. 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: Mean Absolute Error, Root Mean Absolute Error, Relative Absolute Error, Relative Squared Error, and the Coefficient ofDetermination.Does the solution meet the goal?

Select 1 option(s):

A. Yes

B. No

Q47. You have a dataset that includes confidential data. You use the dataset to train a model.You must use a differential privacy parameter to keep the data of individuals safe and private.You need to reduce the effect of user data on aggregated results.What should you do?

Select 1 option(s):

A. Decrease the value of the epsilon parameter to reduce the amount of noise added to the data

B. Increase the value of the epsilon parameter to decrease privacy and increase accuracy

C. Decrease the value of the epsilon parameter to increase privacy and reduce accuracy

D. Set the value of the epsilon parameter to 1 to ensure maximum privacy

Q48. 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.

Select 3 option(s):

A. Penalize the classification

B. Resample the dataset using undersampling or oversampling

C. Normalize the training feature set

D. Generate synthetic samples in the minority class

E. Use accuracy as the evaluation metric of the model

Q49. You have a dataset that contains salary information for users. You plan to generate an aggregate salary report that shows average salaries by city.Privacy of individuals must be preserved without impacting accuracy, completeness, or reliability of the data. The aggregation must be statistically consistent with the distribution of the original data. You must return an approximation of the data instead of the raw data.You need to apply a differential privacy approach.What should you do?

Select 1 option(s):

A. Add noise to the salary data during the analysis

B. Encrypt the salary data before analysis

C. Remove the salary data

D. Convert the salary data to the average column value

Q50. 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?

Select 1 option(s):

A. Relative Absolute Error

B. AUC Curve

C. Mean Absolute Error

D. Relative Squared Error

E. Accuracy

F. Root Mean Square Error

Q51. You create a binary classification model. The model is registered in an Azure Machine Learning workspace. You use the Azure Machine Learning Fairness SDK to assess the model fairness.You develop a training script for the model on a local machine.You need to load the model fairness metrics into Azure Machine Learning studio.What should you do?

Select 1 option(s):

A. Implement the download_dashboard_by_upload_id function

B. Implement the create_group_metric_set function

C. Implement the upload_dashboard_dictionary function

D. Upload the training script

Q52. 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: Mean Absolute Error, Root Mean Absolute Error, Relative Absolute Error, Accuracy, Precision, Recall, F1 score, and AUC.Does the solution meet the goal?

Select 1 option(s):

A. Yes

B. No

Q53. You are a data scientist creating a linear regression model.You need to determine how closely the data fits the regression line.Which metric should you review?

Select 1 option(s):

A. Root Mean Square Error

B. Coefficient of determination

C. Recall

D. Precision

E. Mean absolute error

Q54. 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?

Select 1 option(s):

A. Extract N-Gram Features from Text

B. Edit Metadata

C. Preprocess Text

D. Apply SQL Transformation

Q55. 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?

Select 1 option(s):

A. Yes

B. No

[/ppwp]

Microsoft Azure Test 14

Q1. You have the following code. The code prepares an experiment to run a script:

Q2. You plan to run a Python script as an Azure Machine Learning experiment. The script contains the following code:

Q9. You deploy a model as an Azure Machine Learning real-time web service using the following code.

Q10. 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?

Q18. 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?

Q22. You use Azure Machine Learning designer to create a training pipeline for a regression model. You need to prepare the pipeline for deployment as an endpoint that generates predictions asynchronously for a dataset of input data values. What should you do?

Q31. 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?

Q43. You have an Azure Machine Learning workspace named workspace1.You must add a datastore that connects an Azure Blob storage container to workspace1. You must be able to configure a privilege level.You need to configure authentication.Which authentication method should you use?

Q47. You have a dataset that includes confidential data. You use the dataset to train a model.You must use a differential privacy parameter to keep the data of individuals safe and private.You need to reduce the effect of user data on aggregated results.What should you do?

Q50. 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?

Q53. You are a data scientist creating a linear regression model.You need to determine how closely the data fits the regression line.Which metric should you review?

Q54. 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?

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