You manage an Azure Machine Learning workspace. You build a model for which you must configure a Responsible AI dashboard.
Based on what you learn from the dashboard, you must perform the following activities:
• Determine what must be done to get a desirable outcome from the model.
• Identify the features that have the most direct effect on your outcome of interest.
You need to select the components to use for the Responsible AI dashboard configuration.
Which two components should you add? Each correct answer presents part of the solution.
NOTE: Each correct selection is worth one point.
A. error analysis
B. counterfactuals
C. explanation
D. causal
Show Answer
Correct Answer: B, D
Explanation: To determine what must be done to achieve a desirable model outcome, the Responsible AI dashboard uses counterfactuals, which show the minimal changes to input features needed to change a prediction. To identify which features have the most direct effect on the outcome of interest, the causal component is used, as it estimates direct causal relationships rather than just correlations. Therefore, the correct components are Counterfactuals and Causal.
Question 34
You manage an Azure Machine Learning Workspace named Workspase1 and an Azure Files share named Share1.
You plan to create an Azure Files datastore in Workspace1 to target Share1.
You need to configure permanent access to Share1 from the Azure Files datastore.
Which authorization method should you use?
A. Secondary access key
B. Anonymous access
C. Account SAS key
D. Service SAS key
Show Answer
Correct Answer: A
Explanation: Permanent access to an Azure Files share from an Azure Machine Learning datastore requires credentials that do not expire automatically. Storage account access keys (primary or secondary) provide long-term access until manually rotated. SAS tokens (account or service SAS) are time-bound and require renewal, and anonymous access isn’t supported for Azure Files. Using the secondary access key is a common best practice to allow key rotation without disruption.
Question 35
DRAG DROP
-
You have an Azure Machine Learning workspace.
You plan to use the terminal to configure a compute instance to run a notebook.
You need to add a new R kernel to the compute instance.
In which order should you perform the actions? To answer, move all actions from the list of actions to the answer area and arrange them in the correct order.
Explanation: Create an R Conda environment, activate it, start the R console, register the IRkernel with Jupyter, then exit R.
Question 36
DRAG DROP -
You manage an Azure Machine Learning workspace named workspace1.
You plan to create a registry named registry01 with the help of the following registry.yml (line numbers are used for reference only):
You need to use Azure Machine Learning Python SDK v2 with Python 3.10 in a notebook to interact with workspace1.
Which three actions should you perform in sequence? To answer, move the appropriate actions from the list of actions to the answer area and arrange them in the correct order.
Show Answer
Correct Answer: Install Azure Machine Learning Python SDK v2.
Connect to the workspace.
Use begin_create() to create a registry.
Explanation: You must first install the Azure ML SDK v2 to use Python 3.10 features. Next, connect to the Azure Machine Learning workspace to authenticate and set context. Finally, create the registry by calling begin_create() using the registry YAML configuration.
Question 37
HOTSPOT
-
You design a data processing strategy for a machine learning project.
The data that must be processed includes unstructured flat files that must be processed in real time.
The data transformation must be executed on a serverless compute and optimized for big data analytical workloads.
You need to select the Azure services for the data science team.
Which storage and data processing service should you use? To answer, select the appropriate option in the answer area.
NOTE: Each correct selection is worth one point.
Show Answer
Correct Answer: Data storage for model training workloads:
Azure Data Lake Storage Gen2
Data processing solution:
Azure Databricks
Explanation: Azure Data Lake Storage Gen2 is optimized for big data analytics and unstructured data used in model training. Azure Databricks provides scalable, serverless Spark-based processing suitable for real-time transformation and large analytical workloads.
Question 38
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 an Azure Machine Learning workspace that includes an AmlCompute cluster and a batch endpoint.
You clone a repository that contains an MLflow model to your local computer.
You need to ensure that you can deploy the model to the batch endpoint.
Solution: Register the model in the workspace.
Does the solution meet the goal?
A. Yes
B. No
Show Answer
Correct Answer: A
Explanation: Yes. In Azure Machine Learning, a model must be registered in the workspace before it can be referenced and deployed to a batch endpoint. Registering the MLflow model makes it available for deployment, so this solution meets the goal.
Question 39
HOTSPOT
-
You manage an Azure subscription that contains the following resources:
You plan to implement a solution that will automatically trigger the retraining of the model implemented by MLPipeline1. The trigger must be invoked if data drift is detected in Dataset1.
You need to select the components to invoke and run the solution. The solution must minimize coding implementation and maintenance efforts.
Which components should you select? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Show Answer
Correct Answer: Component used in invoke:
Azure Event Grid subscription
Component used to run:
Azure Logic app
Explanation: Azure Machine Learning data drift detection can publish events to Azure Event Grid, which requires minimal setup via an Event Grid subscription. To minimize coding and maintenance, a Logic App is preferred to handle the event and trigger the retraining pipeline using built-in connectors and a visual, low-code workflow.
Question 40
You manage an Azure Machine Learning workspace.
You plan to train a natural language processing (NLP) text classification model in multiple languages by using Azure Machine Learning Python SDK v2.
You need to configure the language of the text classification job by using automated machine learning.
Which method of the TextClassificationJob class should you use?
A. set_data
B. set_featurization
C. set_sweep
D. set_training_parameters
Show Answer
Correct Answer: D
Explanation: In Azure ML AutoML NLP, the language for a text classification job is configured through training parameters. The TextClassificationJob.set_training_parameters() method includes the language setting (for example, language="en" or a list of languages). set_featurization() controls feature engineering options, not the job language.
Question 41
You manage an Azure Machine Learning workspace.
You choose the uri_folder data type as an output of a pipeline component.
You need to define the data access mode that is supported by your configuration.
Which mode should you define?
A. eval_upload
B. rw_mount
C. download
D. ro_mount
Show Answer
Correct Answer: B
Explanation: For an Azure ML pipeline component with an output of type uri_folder, the component must be able to write files to the output location. The supported and appropriate data access mode for writable outputs is rw_mount, which mounts the storage location with read/write access. The other modes are either read-only (ro_mount), intended for inputs (download), or not applicable to this output scenario (eval_upload).
Question 42
You manage an Azure Machine Learning workspace.
You experiment with an MLflow model that trains interactively by using a notebook in the workspace.
You need to log dictionary type artifacts of the experiments in Azure Machine Learning by using MLflow.
Which syntax should you use?
A. mlflow.log_input(my_dict)
B. mlflow.log_metric("my_metric", my_dict)
C. mlflow.log_metrics(my_dict)
D. mlflow.log_text("my_metric", my_dict)
Show Answer
Correct Answer: C
Explanation: In MLflow, logging a dictionary of key–value pairs is done with `mlflow.log_metrics()`, which accepts a Python dict and records each entry as a metric. The other options either expect a single value, a string, or serve a different purpose and do not support logging a dictionary directly.
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