DP-100: Designing and Implementing a Data Science Solution on Azure (beta) Topic 3
Question #: 39
Topic #: 2
You use Azure Machine Learning Studio to build a machine learning experiment.
You need to divide data into two distinct datasets.
Which module should you use?
A. Split Data
B. Load Trained Model
C. Assign Data to Clusters
D. Group Data into Bins
Selected Answer: A
Question #: 39
Topic #: 3
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?
A. k=1
B. k=10
C. k=0.5
D. k=0.9
Selected Answer: B
Question #: 40
Topic #: 2
You are a lead data scientist for a project that tracks the health and migration of birds. You create a multi-class image classification deep learning model that uses a set of labeled bird photographs collected by experts.
You have 100,000 photographs of birds. All photographs use the JPG format and are stored in an Azure blob container in an Azure subscription.
You need to access the bird photograph files in the Azure blob container from the Azure Machine Learning service workspace that will be used for deep learning model training. You must minimize data movement.
What should you do?
A. Create an Azure Data Lake store and move the bird photographs to the store.
B. Create an Azure Cosmos DB database and attach the Azure Blob containing bird photographs storage to the database.
C. Create and register a dataset by using TabularDataset class that references the Azure blob storage containing bird photographs.
D. Register the Azure blob storage containing the bird photographs as a datastore in Azure Machine Learning service.
E. Copy the bird photographs to the blob datastore that was created with your Azure Machine Learning service workspace.
Selected Answer: D
Question #: 40
Topic #: 1
This question is included in a number of questions that depicts the identical set-up. However, every question has a distinctive result. Establish if the recommendation satisfies the requirements.
You are planning to make use of Azure Machine Learning designer to train models.
You need choose a suitable compute type.
Recommendation: You choose Inference cluster.
Will the requirements be satisfied?
A. Yes
B. No
Selected Answer: B
Question #: 41
Topic #: 2
Note: This question is part of a series of questions that present the same scenario. Each question in the series contains a unique solution that might meet the stated goals. Some question sets might have more than one correct solution, while others might not have a correct solution.
After you answer a question in this section, you will NOT be able to return to it. As a result, these questions will not appear in the review screen.
You are analyzing a numerical dataset which contains missing values in several columns.
You must clean the missing values using an appropriate operation without affecting the dimensionality of the feature set.
You need to analyze a full dataset to include all values.
Solution: Calculate the column median value and use the median value as the replacement for any missing value in the column.
Does the solution meet the goal?
A. Yes
B. No
Selected Answer: A
Question #: 41
Topic #: 1
This question is included in a number of questions that depicts the identical set-up. However, every question has a distinctive result. Establish if the recommendation satisfies the requirements.
You are planning to make use of Azure Machine Learning designer to train models.
You need choose a suitable compute type.
Recommendation: You choose Compute cluster.
Will the requirements be satisfied?
A. Yes
B. No
Selected Answer: A
Question #: 42
Topic #: 4
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 pipeline named pipeline1 with two steps that contain Python scripts. Data processed by the first step is passed to the second step.
You must update the content of the downstream data source of pipeline1 and run the pipeline again.
You need to ensure the new run of pipeline1 fully processes the updated content.
Solution: Set the allow_reuse parameter of the PythonScriptStep object of both steps to False.
Does the solution meet the goal?
A. Yes
B. No
Selected Answer: A
Question #: 42
Topic #: 1
You are making use of the Azure Machine Learning to designer construct an experiment.
After dividing a dataset into training and testing sets, you configure the algorithm to be Two-Class Boosted Decision Tree.
You are preparing to ascertain the Area Under the Curve (AUC).
Which of the following is a sequential combination of the models required to achieve your goal?
A. Train, Score, Evaluate.
B. Score, Evaluate, Train.
C. Evaluate, Export Data, Train.
D. Train, Score, Export Data.
Selected Answer: A
Question #: 43
Topic #: 2
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 a data scientist using Azure Machine Learning Studio.
You need to normalize values to produce an output column into bins to predict a target column.
Solution: Apply an Equal Width with Custom Start and Stop binning mode.
Does the solution meet the goal?
A. Yes
B. No
Selected Answer: B
Question #: 43
Topic #: 4
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 pipeline named pipeline1 with two steps that contain Python scripts. Data processed by the first step is passed to the second step.
You must update the content of the downstream data source of pipeline1 and run the pipeline again.
You need to ensure the new run of pipeline1 fully processes the updated content.
Solution: Set the regenerate_outputs parameter of the pipeline1 experiment’s run submit method to True.
Does the solution meet the goal?
A. Yes
B. No
Selected Answer: A
Question #: 43
Topic #: 3
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?
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
Selected Answer: B
Question #: 44
Topic #: 2
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 a data scientist using Azure Machine Learning Studio.
You need to normalize values to produce an output column into bins to predict a target column.
Solution: Apply a Quantiles binning mode with a PQuantile normalization.
Does the solution meet the goal?
A. Yes
B. No
Selected Answer: A
Question #: 44
Topic #: 3
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?
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},
Selected Answer: B
Question #: 44
Topic #: 4
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 pipeline named pipeline1 with two steps that contain Python scripts. Data processed by the first step is passed to the second step.
You must update the content of the downstream data source of pipeline1 and run the pipeline again.
You need to ensure the new run of pipeline1 fully processes the updated content.
Solution: Change the value of the compute_target parameter of the PythonScriptStep object in the two steps.
Does the solution meet the goal?
A. Yes
B. No
Selected Answer: D
Question #: 45
Topic #: 3
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?
A. Convert to CSV
B. Enter Data Manually
C. Import Data
D. Dataset
Selected Answer: C
Question #: 46
Topic #: 2
You are with a time series dataset in Azure Machine Learning Studio.
You need to split your dataset into training and testing subsets by using the Split Data module.
Which splitting mode should you use?
A. Recommender Split
B. Regular Expression Split
C. Relative Expression Split
D. Split Rows with the Randomized split parameter set to true
Selected Answer: C
Question #: 47
Topic #: 4
You create an MLflow model.
You must deploy the model to Azure Machine Learning for batch inference.
You need to create the batch deployment.
Which two components should you use? Each correct answer presents a complete solution.
NOTE: Each correct selection is worth one point.
A. Environment
B. Model files
C. Online endpoint
D. Kubernetes online endpoint
E. Compute target
Selected Answer: BE
Question #: 47
Topic #: 3
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?
A. Yes
B. No
Selected Answer: A
Question #: 48
Topic #: 3
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?
A. Yes
B. No
Selected Answer: B
Question #: 48
Topic #: 2
You create an Azure Machine Learning workspace. You are preparing a local Python environment on a laptop computer. You want to use the laptop to connect to the workspace and run experiments.
You create the following config.json file.
{
“workspace_name” : “ml-workspace”
}
You must use the Azure Machine Learning SDK to interact with data and experiments in the workspace.
You need to configure the config.json file to connect to the workspace from the Python environment.
Which two additional parameters must you add to the config.json file in order to connect to the workspace? Each correct answer presents part of the solution.
NOTE: Each correct selection is worth one point.
A. login
B. resource_group
C. subscription_id
D. key
E. region
Selected Answer: BC
Question #: 48
Topic #: 4
You create an Azure Machine Learning workspace. The workspace contains a dataset named sample_dataset, a compute instance, and a compute cluster.
You must create a two-stage pipeline that will prepare data in the dataset and then train and register a model based on the prepared data.
The first stage of the pipeline contains the following code:
You need to identify the location containing the output of the first stage of the script that you can use as input for the second stage.
Which storage location should you use?
A. workspaceblobstore datastore
B. workspacefilestore datastore
C. compute instance
D. compute_cluster
Selected Answer: A
Question #: 49
Topic #: 3
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?
A. Yes
B. No
Selected Answer: A
Question #: 50
Topic #: 2
You create an Azure Machine Learning compute resource to train models. The compute resource is configured as follows:
✑ Minimum nodes: 2
✑ Maximum nodes: 4
You must decrease the minimum number of nodes and increase the maximum number of nodes to the following values:
✑ Minimum nodes: 0
✑ Maximum nodes: 8
You need to reconfigure the compute resource.
What are three possible ways to achieve this goal? Each correct answer presents a complete solution.
NOTE: Each correct selection is worth one point.
A. Use the Azure Machine Learning studio.
B. Run the update method of the AmlCompute class in the Python SDK.
C. Use the Azure portal.
D. Use the Azure Machine Learning designer.
E. Run the refresh_state() method of the BatchCompute class in the Python SDK.
Selected Answer: ABC
Question #: 50
Topic #: 3
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.
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
Selected Answer: BD
Question #: 52
Topic #: 4
You create an Azure Machine Learning workspace. You use Azure Machine Learning designer to create a pipeline within the workspace.
You need to submit a pipeline run from the designer.
What should you do first?
A. Create an experiment.
B. Create an attached compute resource.
C. Create a compute cluster.
D. Select a model.
Selected Answer: A
Question #: 52
Topic #: 2
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.
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 ג€”w and ג€”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.
Selected Answer: ABC
Question #: 54
Topic #: 3
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?
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.
Selected Answer: A
Question #: 55
Topic #: 4
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 an Azure Machine Learning model.
You plan to deploy the model to an online endpoint.
You need to ensure that applications will be able to use the authentication method with a non-expiring artifact to access the model.
Solution: Create a managed online endpoint and set the value of its auth_mode parameter to aml_token. Deploy the model to the online endpoint.
Does the solution meet the goal?
A. Yes
B. No
Selected Answer: D
Question #: 55
Topic #: 3
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?
A. Yes
B. No
Selected Answer: B
Question #: 56
Topic #: 4
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 an Azure Machine Learning model.
You plan to deploy the model to an online endpoint.
You need to ensure that applications will be able to use the authentication method with a non-expiring artifact to access the model.
Solution: Create a managed online endpoint and set the value of its auth_mode parameter to key. Deploy the model to the online endpoint.
Does the solution meet the goal?
A. Yes
B. No
Selected Answer: C
Question #: 56
Topic #: 3
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?
A. Yes
B. No
Selected Answer: B
Question #: 57
Topic #: 4
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 an Azure Machine Learning model.
You plan to deploy the model to an online endpoint.
You need to ensure that applications will be able to use the authentication method with a non-expiring artifact to access the model.
Solution: Create a managed online endpoint with the default authentication settings. Deploy the model to the online endpoint.
Does the solution meet the goal?
A. Yes
B. No
Selected Answer: A
Question #: 57
Topic #: 3
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?
A. Yes
B. No
Selected Answer: A
Question #: 57
Topic #: 2
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?
A. run
B. get
C. backfill
D. update
Selected Answer: C
Question #: 58
Topic #: 4
You use the Azure Machine Learning SDK v2 for Python and notebooks to train a model. You use Python code to create a compute target, an environment, and a training script.
You need to prepare information to submit a training job.
Which class should you use?
A. MLClient
B. BuildContext
C. EndpointConnection
D. command
Selected Answer: B
Question #: 58
Topic #: 2
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?
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
Selected Answer: A
Question #: 59
Topic #: 4
You manage an Azure Machine Learning workspace.
You build a custom model you must log with MLflow. The custom model includes the following:
• The model is not natively supported by MLflow.
• The model cannot be serialized in Pickle format.
• The model source code is complex.
• The Python library for the model must be packaged with the model.
You need to create a custom model flavor to enable logging with MLflow.
What should you use?
A. model loader
B. artifacts
C. model wrapper
D. custom signatures
Selected Answer: A
Question #: 60
Topic #: 3
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?
A. k=0.5
B. k=0.01
C. k=5
D. k=1
Selected Answer: C
Question #: 60
Topic #: 4
You create an Azure Machine Learning workspace.
You must use the Python SDK v2 to implement an experiment from a Jupyter notebook in the workspace. The experiment must log string metrics.
You need to implement the method to log the string metrics.
Which method should you use?
A. mlflow.log_artifact()
B. mlflow.log.dict()
C. mlflow.log_metric()
D. mlflow.log_text()
Selected Answer: D
Question #: 61
Topic #: 4
You manage an Azure Machine Learning workspace. You develop a machine learning model.
You must deploy the model to use a low-priority VM with a pricing discount.
You need to deploy the model.
Which compute target should you use?
A. Azure Kubernetes Service (AKS)
B. Azure Machine Learning compute clusters
C. Azure Container Instances (ACI)
D. Local deployment
Selected Answer: B
Question #: 64
Topic #: 2
You are profiling data by using Azure Machine Learning studio.
You need to detect columns with odd or missing values.
Which statistic should you analyze?
A. Profile
B. Std deviation
C. Error count
D. Type
Selected Answer: D
Question #: 64
Topic #: 3
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?
A. Yes
B. No
Selected Answer: A
Question #: 65
Topic #: 4
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 an Azure Machine Learning model.
You plan to deploy the model to an online endpoint.
You need to ensure that applications will be able to use the authentication method with a non-expiring artifact to access the model.
Solution: Create a Kubernetes online endpoint and set the value of its auth_mode parameter to aml_token. Deploy the model to the online endpoint.
Does the solution meet the goal?
A. Yes
B. No
Selected Answer: B
Question #: 65
Topic #: 2
You are authoring a notebook in Azure Machine Learning studio.
You must install packages from the notebook into the currently running kernel. The installation must be limited to the currently running kernel only.
You need to install the packages.
Which magic function should you use?
A. !pip
B. %pip
C. !conda
D. %load
Selected Answer: B
Question #: 65
Topic #: 3
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?
A. Yes
B. No
Selected Answer: B
Question #: 66
Topic #: 4
You manage an Azure Machine Learning workspace.
You must provide explanations for the behavior of the models with feature importance measures.
You need to configure a Responsible AI dashboard in Azure Machine Learning.
Which dashboard component should you configure?
A. Counterfactual what-if
B. Casual inference
C. Fairness assessment
D. Interpretability
Selected Answer: D
Question #: 66
Topic #: 3
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?
A. Yes
B. No
Selected Answer: B
Question #: 67
Topic #: 4
You create an Azure Machine Learning workspace.
You must use the Python SDK v2 to implement an experiment from a Jupyter notebook in the workspace. The experiment must log a list of numeral metrics.
You need to implement a method to log a list of numeral metrics.
Which method should you use?
A. mlflow.log_metric()
B. mlflow.log.batch()
C. mlflow.log_image()
D. mlflow.log_artifact()
Selected Answer: B
Question #: 67
Topic #: 2
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 use Azure Machine Learning designer to load the following datasets into an experiment:
Dataset1 –
Dataset2 –
You need to create a dataset that has the same columns and header row as the input datasets and contains all rows from both input datasets.
Solution: Use the Add Rows module.
Does the solution meet the goal?
A. Yes
B. No
Selected Answer: C
Question #: 67
Topic #: 3
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?
A. Yes
B. No
Selected Answer: B
Question #: 68
Topic #: 2
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 use Azure Machine Learning designer to load the following datasets into an experiment:
Dataset1 –
Dataset2 –
You need to create a dataset that has the same columns and header row as the input datasets and contains all rows from both input datasets.
Solution: Use the Apply Transformation module.
Does the solution meet the goal?
A. Yes
B. No
Selected Answer: B
Question #: 68
Topic #: 3
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?
A. Yes
B. No
Selected Answer: A
Question #: 69
Topic #: 2
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 use Azure Machine Learning designer to load the following datasets into an experiment:
Dataset1 –
Dataset2 –
You need to create a dataset that has the same columns and header row as the input datasets and contains all rows from both input datasets.
Solution: Use the Execute Python Script module.
Does the solution meet the goal?
A. Yes
B. No
Selected Answer: A
Question #: 69
Topic #: 4
You run Azure Machine Learning training experiments. The training scripts directory contains 100 files that includes a file named .amlignore. The directory also contains subdirectories named ./outputs and ./logs.
There are 20 files in the training scripts directory that must be excluded from the snapshot to the compute targets. You create a file named .gitignore in the root of the directory. You add the names of the 20 files to the .gitignore file. These 20 files continue to be copied to the compute targets.
You need to exclude the 20 files.
What should you do?
A. Copy the contents of the file named .gitignore to the file named .amlignore.
B. Move the file named .gitignore to the ./outputs directory.
C. Move the file named .gitignore to the ./logs directory.
D. Add the contents of the file named .amlignore to the file named .gitignore.
Selected Answer: A