Microsoft Azure Test 13

Free Azure Data Scientist Associate (DP-100) Sample Questions. Prepare confidently with a free, timed practice exam that mirrors the Azure Data Scientist Associate test format and difficulty. Get hired as a data scientist

Welcome to your Microsoft Azure Test-13

Name

Phone

Q1. 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: Remove the entire column that contains the missing data point.Does the solution meet the goal?

Select 1 option(s):

A. Yes

B. No

Q2. You are creating a new experiment in Azure Machine Learning Studio. You have a small dataset that has missing values in many columns. The data does not require the application of predictors for each column. You plan to use the Clean Missing Data.You need to select a data cleaning method.Which method should you use?

Select 1 option(s):

A. Replace using Probabilistic PCA

B. Normalization

C. Synthetic Minority Oversampling Technique (SMOTE)

D. Replace using MICE

Q3. You plan to deliver a hands-on workshop to several students. The workshop will focus on creating data visualizations using Python. Each student will use a device that has internet access.Student devices are not configured for Python development. Students do not have administrator access to install software on their devices. Azure subscriptions are not available for students.You need to ensure that students can run Python-based data visualization code.Which Azure tool should you use?

Select 1 option(s):

A. Anaconda Data Science Platform

B. Azure BatchAI

C. Azure Notebooks

D. Azure Machine Learning Service

Q4. 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?

Select 1 option(s):

A. Azure Data Lake Analytics

B. Azure Databricks

C. Azure Container Service

D. Apache Spark for HDInsight

Q5. 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: Use the Last Observation Carried Forward (LOCF) method to impute the missing data points.Does the solution meet the goal?

Select 1 option(s):

A. Yes

B. No

Q6. 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.

Select 3 option(s):

A. Run Python code that uses the Azure ML SDK library and calls the Workspace.create method with name, subscription_id, and resource_group parameters.

B. Navigate to Azure Machine Learning studio and create a workspace.

C. Use the Azure Command Line Interface (CLI) with the Azure Machine Learning extension to call the az group create function with --name and --location parameters, and then the az ml workspace create function, specifying ?“w and ?“g parameters for the workspace name and resource group.

D. Navigate to Azure Machine Learning studio and create a workspace.

E. Run Python code that uses the Azure ML SDK library and calls the Workspace.get method with name, subscription_id, and resource_group parameters.

Q7. You use Azure Machine Learning to train a model based on a dataset named dataset1.You define a dataset monitor and create a dataset named dataset2 that contains new data.You need to compare dataset1 and dataset2 by using the Azure Machine Learning SDK for Python.Which method of the DataDriftDetector class should you use?

Select 1 option(s):

A. run

B. get

C. backfill

D. update

Q8. 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?

Select 1 option(s):

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

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

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

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

Q9. 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.

Select 1 option(s):

A. a Root Mean Square Error value that is low

B. an R-Squared value close to 0

C. an F1 score that is low

D. an R-Squared value close to 1

E. an F1 score that is high

F. a Root Mean Square Error value that is high

Q10. 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?

Select 1 option(s):

A. Yes

B. No

Q11. 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?

Select 1 option(s):

A. Yes

B. No

Q12. 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?

Select 1 option(s):

A. Yes

B. No

Q13. 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?

Select 1 option(s):

A. the digit_identification.py script

B. the debug log

C. the Activity Log in the Azure portal for the Machine Learning workspace

D. the Inference Clusters tab in Machine Learning studio

E. a file named parallel_run_step.txt located in the output folder

Q14. 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?

Select 1 option(s):

Q15. 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?

Select 1 option(s):

A. environment_definition = {“training_data“:training_ds}

B. inputs = [training_ds.as_named_input(‘training_ds‘)]

C. script_params = {“--training_ds“:training_ds}

D. source_directory = training_ds

Q16. You are creating a machine learning model.You need to identify outliers in the data.Which two visualizations can you use? Each correct answer presents a complete solution.NOTE: Each correct selection is worth one point.

Select 2 option(s):

A. Venn diagram

B. Box plot

C. ROC curve

D. Random forest diagram

E. Scatter plot

Q17. You are solving a classification task.You must evaluate your model on a limited data sample by using k-fold cross-validation. You start by configuring a k parameter as the number of splits.You need to configure the k parameter for the cross-validation.Which value should you use?

Select 1 option(s):

A. k=1

B. k=10

C. k=0.5

D. k=0.9

Q18. 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?

Select 1 option(s):

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

B. 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.

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

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

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

Q19. You are evaluating a completed binary classification machine learning model.You need to use the precision as the evaluation metric.Which visualization should you use?

Select 1 option(s):

A. violin plot

B. Gradient descent

C. Scatter plot

D. Receiver Operating Characteristic (ROC) curve

Q20. You are building a regression model for estimating the number of calls during an event.You need to determine whether the feature values achieve the conditions to build a Poisson regression model.Which two conditions must the feature set contain? Each correct answer presents part of the solution.NOTE: Each correct selection is worth one point.

Select 2 option(s):

A. The label data must be a negative value.

B. The label data must be whole numbers.

C. The label data must be non-discrete.

D. The label data must be a positive value.

E. The label data can be positive or negative.

Q21. You are creating a new Azure Machine Learning pipeline using the designer.The pipeline must train a model using data in a comma-separated values (CSV) file that is published on a website. You have not created a dataset for this file.You need to ingest the data from the CSV file into the designer pipeline using the minimal administrative effort.Which module should you add to the pipeline in Designer?

Select 1 option(s):

A. Convert to CSV

B. Enter Data Manually

C. Import Data

D. Dataset

Q22. 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 the 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 Synthetic Minority Oversampling Technique (SMOTE) sampling mode.Does the solution meet the goal?

Select 1 option(s):

A. Yes

B. No

Q23. You create a multi-class image classification deep learning model that uses the PyTorch deep learning framework.You must configure Azure Machine Learning Hyperdrive to optimize the hyperparameters for the classification model.You need to define a primary metric to determine the hyperparameter values that result in the model with the best accuracy score.Which three actions must you perform? Each correct answer presents part of the solution.NOTE: Each correct selection is worth one point.

Select 3 option(s):

A. Set the primary_metric_goal of the estimator used to run the bird_classifier_train.py script to maximize.

B. Add code to the bird_classifier_train.py script to calculate the validation loss of the model and log it as a float value with the key loss.

C. Set the primary_metric_goal of the estimator used to run the bird_classifier_train.py script to minimize.

D. Set the primary_metric_name of the estimator used to run the bird_classifier_train.py script to accuracy.

E. Set the primary_metric_name of the estimator used to run the bird_classifier_train.py script to loss.

F. Add code to the bird_classifier_train.py script to calculate the validation accuracy of the model and log it as a float value with the key accuracy.

Q24. You are performing a filter - based feature selection for a dataset to build a multi - class classifier by using Azure Machine Learning Studio. The dataset contains categorical features that are highly correlated to the output label column. You need to select the appropriate feature scoring statistical method to identify the key predictors. Which method should you use?

Select 1 option(s):

A. Kendall correlation

B. Spearman correlation

C. Chi-squared

D. Pearson correlation

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 are creating a new experiment in Azure Machine Learning Studio.One class has a much smaller number of observations than the 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?

Select 1 option(s):

A. Yes

B. No

Q26. 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 the 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 Stratified split for the sampling mode.Does the solution meet the goal?

Select 1 option(s):

A. Yes

B. No

Q27. You plan to use automated machine learning to train a regression model. You have data that has features which have missing values, and categorical features with few distinct values.You need to configure automated machine learning to automatically impute missing values and encode categorical features as part of the training task.Which parameter and value pair should you use in the AutoMLConfig class?

Select 1 option(s):

A. featurization = ‘auto‘

B. enable_voting_ensemble = True

C. task = ‘classification‘

D. exclude_nan_labels = True

E. enable_tf = True

Q29. You are evaluating a completed binary classification machine learning model.You need to use the precision as the evaluation metric.Which visualization should you use?

Select 1 option(s):

A. Violin plot

B. Gradient descent

C. Box plot

D. Binary classification confusion matrix

Q29. 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. Edit Metadata

B. Filter Based Feature Selection

C. Execute Python Script

D. Latent Dirichlet Allocation

Q30. 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,f2,I 1,1,2,0 2,1,1,1 3,2,1,0 4,2,2,1 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?

Select 1 option(s):

A. Yes

B. No

Q31. You register a file dataset named csv_folder that references a folder. The folder includes multiple comma-separated values (CSV) files in an Azure storage blob container. You plan to use the following code to run a script that loads data from the file dataset. You create and instantiate the following variables:

You have the following code:

You need to pass the dataset to ensure that the script can read the files it references. Which code segment should you insert to replace the code comment?

Select 1 option(s):

A. inputs=[file_dataset.as_named_input(‘training_files‘)],

B. inputs=[file_dataset.as_named_input(‘training_files‘).as_mount()],

C. inputs=[file_dataset.as_named_input(‘training_files‘).to_pandas_dataframe()],

D. script_params={‘--training_files‘: file_dataset},

Q32. 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.upload_file(‘outputs/labels.csv‘, ‘./data.csv‘) Does the solution meet the goal?
multiple-choice

Select 1 option(s):

A. Yes

B. No

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 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_table(‘Label Values‘, label_vals) Does the solution meet the goal?
multiple-choice

Select 1 option(s):

A. Yes

B. No

Q34. 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: for label_val in label_vals: run.log(‘Label Values‘, label_val) Does the solution meet the goal?

Select 1 option(s):

multiple-choice

A. Yes

B. No

Q35. You have a comma-separated values (CSV) file containing data from which you want to train a classification model. You are using the Automated Machine Learning interface in Azure Machine Learning studio to train the classification model. You set the task type to Classification. You need to ensure that the Automated Machine Learning process evaluates only linear models. What should you do?

Select 1 option(s):

A. Add all algorithms other than linear ones to the blocked algorithms list.

B. Set the Exit criterion option to a metric score threshold.

C. Clear the option to perform automatic featurization.

D. Clear the option to enable deep learning.

E. Set the task type to Regression.

Q36. You plan to use the Hyperdrive feature of Azure Machine Learning to determine the optimal hyperparameter values when training a model. You must use Hyperdrive to try combinations of the following hyperparameter values: ? learning_rate: any value between 0.001 and 0.1 ? batch_size: 16, 32, or 64 You need to configure the search space for the Hyperdrive experiment. Which two parameter expressions should you use? Each correct answer presents part of the solution. NOTE: Each correct selection is worth one point.

Select 2 option(s):

A. a choice expression for learning_rate

B. a uniform expression for learning_rate

C. a normal expression for batch_size

D. a choice expression for batch_size

E. a uniform expression for batch_size

Q37. 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,f2,I 1,1,2,0 2,1,1,1 3,2,1,0 4,2,2,1 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?

Select 1 option(s):

A. Yes

B. No

Q38. 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,f2,I 1,1,2,0 2,1,1,1 3,2,1,0 4,2,2,1 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?

Select 1 option(s):

A. Yes

B. No

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 create a model to forecast weather conditions based on historical data. You need to create a pipeline that runs a processing script to load data from a datastore and pass the processed data to a machine learning model training script. Solution: Run the following code:

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 create a model to forecast weather conditions based on historical data. You need to create a pipeline that runs a processing script to load data from a datastore and pass the processed data to a machine learning model training script. Solution: Run the following code:

Select 1 option(s):

A. Yes

B. No

Q41. 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 a model to forecast weather conditions based on historical data. You need to create a pipeline that runs a processing script to load data from a datastore and pass the processed data to a machine learning model training script. Solution: Run the following code:

Does the solution meet the goal?

Select 1 option(s):

A. Yes

B. No

Q42. 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?

Select 1 option(s):

A. Yes

B. No

Q43. You are solving a classification task. You must evaluate your model on a limited data sample by using k-fold cross-validation. You start by configuring a k parameter as the number of splits. You need to configure the k parameter for the cross-validation. Which value should you use?

Select 1 option(s):

A. k=0.5

B. k=0.01

C. k=5

D. k=1

Q44. 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: Relative Squared Error, Coefficient of Determination, Accuracy, Precision, Recall, F1 score, and AUC.Does the solution meet the goal?

Select 1 option(s):

A. Yes

B. No

Q45. 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?

Select 1 option(s):

A. Yes

B. No

Q47. You create a pipeline in designer to train a model that predicts automobile prices. Because of non-linear relationships in the data, the pipeline calculates the natural log (Ln) of the prices in the training data, trains a model to predict this natural log of price value, and then calculates the exponential of the scored label to get the predicted price. The training pipeline is shown in the exhibit. (Click the Training pipeline tab.) Training pipeline –

You create a real-time inference pipeline from the training pipeline, as shown in the exhibit. (Click the Real-time pipeline tab.) Real-time pipeline –
You need to modify the inference pipeline to ensure that the web service returns the exponential of the scored label as the predicted automobile price and that client applications are not required to include a price value in the input values. Which three modifications must you make to the inference pipeline? Each correct answer presents part of the solution. NOTE: Each correct selection is worth one point.

Select 3 option(s):

A. Connect the output of the Apply SQL Transformation to the Web Service Output module.

B. Replace the Web Service Input module with a data input that does not include the price column.

C. Add a Select Columns module before the Score Model module to select all columns other than price.

D. Replace the training dataset module with a data input that does not include the price column.

E. Remove the Apply Math Operation module that replaces price with its natural log from the data flow.

F. Remove the Apply SQL Transformation module from the data flow.

Q47. 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 KubernetesService (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.

Select 2 option(s):

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

B. The name of the inference pipeline for the endpoint.

C. The URL of the endpoint.

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

E. The key for the endpoint.

Q48. 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 MachineLearning 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?

Select 1 option(s):

A. normalized_mean_absolute_error

B. AUC_weighted

C. accuracy

D. normalized_root_mean_squared_error

E. spearman_correlation

Q49. You create a multi-class image classification deep learning model that uses a set of labeled images. You create a script file named train.py that uses the PyTorch1.3 framework to train the model.You must run the script by using an estimator. The code must not require any additional Python libraries to be installed in the environment for the estimator. The time required for model training must be minimized.You need to define the estimator that will be used to run the script.Which estimator type should you use?

Select 1 option(s):

A. TensorFlow

B. PyTorch

C. SKLearn

D. Estimator

Q50. 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 a model to forecast weather conditions based on historical data. You need to create a pipeline that runs a processing script to load data from a datastore and pass the processed data to a machine learning model training script. Solution: Run the following code:

Does the solution meet the goal?

Select 1 option(s):

A. Yes

B. No

Q51. 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?

Select 1 option(s):

A. best_model = automl_run.get_details()

B. best_model = automl_run.get_metrics()

C. best_model = automl_run.get_file_names()[1]

D. best_model = automl_run.get_output()[1]

Q52. You plan to use the Hyperdrive feature of Azure Machine Learning to determine the optimal hyperparameter values when training a model.You must use Hyperdrive to try combinations of the following hyperparameter values. You must not apply an early termination policy.? learning_rate: any value between 0.001 and 0.1? batch_size: 16, 32, or 64You need to configure the sampling method for the Hyperdrive experiment.Which two sampling methods can you use? Each correct answer is a complete solution.NOTE: Each correct selection is worth one point.

Select 2 option(s):

A. No sampling

B. Grid sampling

C. Bayesian sampling

D. Random sampling

Q53. You use the Two-Class Neural Network module in Azure Machine Learning Studio to build a binary classification model. You use the Tune ModelHyperparameters module to tune accuracy for the model.You need to configure the Tune Model Hyperparameters module.Which two values should you use? Each correct answer presents part of the solution.NOTE: Each correct selection is worth one point.

Select 2 option(s):

A. Number of hidden nodes

B. Learning Rate

C. The type of the normalizer

D. Number of learning iterations

E. Hidden layer specification

Q54. You are training machine learning models in Azure Machine Learning. You use Hyperdrive to tune the hyperparameters. In previous model training and tuning runs, many models showed similar performance. You need to select an early termination policy that meets the following requirements: ? accounts for the performance of all previous runs when evaluating the current run avoids comparing the current run with only the best performing run to date Which two early termination policies should you use? Each correct answer presents part of the solution. NOTE: Each correct selection is worth one poin

Select 2 option(s):

A. Median stopping

B. Bandit

C. Default

D. Truncation selection

Q55. 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.

Select 2 option(s):

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

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

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

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

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

Q56. 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?

Select 1 option(s):

A. Yes

B. No

Q57. You create a binary classification model by using Azure Machine Learning Studio.You must tune hyperparameters by performing a parameter sweep of the model. The parameter sweep must meet the following requirements:? iterate all possible combinations of hyperparameters? minimize computing resources required to perform the sweepYou need to perform a parameter sweep of the model.Which parameter sweep mode should you use?

Select 1 option(s):

A. Random sweep

B. Sweep clustering

C. Entire grid

D. Random grid

Q58. You use the Azure Machine Learning Python SDK to define a pipeline to train a model.The data used to train the model is read from a folder in a datastore.You need to ensure the pipeline runs automatically whenever the data in the folder changes.What should you do?

Select 1 option(s):

A. Set the regenerate_outputs property of the pipeline to True

B. Create a ScheduleRecurrance object with a Frequency of auto. Use the object to create a Schedule for the pipeline

C. Create a PipelineParameter with a default value that references the location where the training data is stored

D. Create a Schedule for the pipeline. Specify the datastore in the datastore property, and the folder containing the training data in the path_on_datastore property

Q59. You are performing clustering by using the K-means algorithm.You need to define the possible termination conditions.Which three conditions can you use? Each correct answer presents a complete solution.NOTE: Each correct selection is worth one point.

Select 3 option(s):

A. Centroids do not change between iterations.

B. The residual sum of squares (RSS) rises above a threshold.

C. The residual sum of squares (RSS) falls below a threshold.

D. A fixed number of iterations is executed.

E. The sum of distances between centroids reaches a maximum.

Q60. You create a script that trains a convolutional neural network model over multiple epochs and logs the validation loss after each epoch. The script includes arguments for batch size and learning rate.You identify a set of batch size and learning rate values that you want to try.You need to use Azure Machine Learning to find the combination of batch size and learning rate that results in the model with the lowest validation loss.What should you do?

Select 1 option(s):

A. Run the script in an experiment based on an AutoMLConfig object

B. Create a PythonScriptStep object for the script and run it in a pipeline

C. Use the Automated Machine Learning interface in Azure Machine Learning studio

D. Run the script in an experiment based on a ScriptRunConfig object

E. Run the script in an experiment based on a HyperDriveConfig object

Q61. You plan to run a Python script as an Azure Machine Learning experiment.The script must read files from a hierarchy of folders. The files will be passed to the script as a dataset argument.You must specify an appropriate mode for the dataset argument.Which two modes can you use? Each correct answer presents a complete solution.NOTE: Each correct selection is worth one point.

Select 1 option(s):

A. to_pandas_dataframe()

B. as_download()

C. as_upload()

D. as_mount()

Q62. You create a binary classification model.You need to evaluate the model performance.Which two metrics can you use? Each correct answer presents a complete solution.NOTE: Each correct selection is worth one point.

Select 2 option(s):

A. relative absolute error

B. precision

C. accuracy

D. mean absolute error

E. coefficient of determination

Q63. You are building a recurrent neural network to perform a binary classification.You review the training loss, validation loss, training accuracy, and validation accuracy for each training epoch.You need to analyze model performance.You need to identify whether the classification model is overfitted.Which of the following is correct?

Select 1 option(s):

A. The training loss stays constant and the validation loss stays on a constant value and close to the training loss value when training the model.

B. The training loss decreases while the validation loss increases when training the model.

C. The training loss stays constant and the validation loss decreases when training the model.

D. The training loss increases while the validation loss decreases when training the model.

Q64. 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?

Select 1 option(s):

A. Multilayer Perceptions (MLPs)

B. Convolutional Neural Networks (CNNs)

C. Recurrent Neural Networks (RNNs)

D. Generative Adversarial Networks (GANs)

Microsoft Azure Test 13

Q4. 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?

Q6. 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.

Q8. 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?

Q13. You create a batch inference pipeline by using the Azure ML SDK. You configure the pipeline parameters by executing the following code:

Q14. 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:

Q16. You are creating a machine learning model.You need to identify outliers in the data.Which two visualizations can you use? Each correct answer presents a complete solution.NOTE: Each correct selection is worth one point.

Q17. You are solving a classification task.You must evaluate your model on a limited data sample by using k-fold cross-validation. You start by configuring a k parameter as the number of splits.You need to configure the k parameter for the cross-validation.Which value should you use?

Q19. You are evaluating a completed binary classification machine learning model.You need to use the precision as the evaluation metric.Which visualization should you use?

Q29. You are evaluating a completed binary classification machine learning model.You need to use the precision as the evaluation metric.Which visualization should you use?

Q29. 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?

Q43. You are solving a classification task. You must evaluate your model on a limited data sample by using k-fold cross-validation. You start by configuring a k parameter as the number of splits. You need to configure the k parameter for the cross-validation. Which value should you use?

Q51. 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:

Q55. 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.

Q58. You use the Azure Machine Learning Python SDK to define a pipeline to train a model.The data used to train the model is read from a folder in a datastore.You need to ensure the pipeline runs automatically whenever the data in the folder changes.What should you do?

Q59. You are performing clustering by using the K-means algorithm.You need to define the possible termination conditions.Which three conditions can you use? Each correct answer presents a complete solution.NOTE: Each correct selection is worth one point.

Q62. You create a binary classification model.You need to evaluate the model performance.Which two metrics can you use? Each correct answer presents a complete solution.NOTE: Each correct selection is worth one point.

Q64. 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?

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