Microsoft Microsoft Certifications DP-100 Questions & Answers

• Question 401:

  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()

  Correct Answer: D

  Logging files

  In general, files in MLflow are called artifacts. You can log artifacts in multiple ways in Mlflow:

  *

  Log text in a text file

  mlflow.log_text("text string", "notes.txt")

  Text is persisted inside of the run in a text file with name notes.txt.

  Incorrect:

  *

  mlflow.log.dict

  Log dictionaries as JSON and YAML files

  mlflow.log_dict(dictionary, "file.yaml"

  dictionary is a dictionary object containing all the structure that you want to persist as JSON or YAML file.

  *

  mlflow.log_artifact

  Log a trivial file already existing

  mlflow.log_artifact("path/to/file.pkl")

  Files are always logged in the root of the run. If artifact_path is provided, then the file is logged in a folder as indicated in that parameter.

  *

  Log all the artifacts in an existing folder

  mlflow.log_artifacts("path/to/folder")

  Folder structure is copied to the run, but the root folder indicated is not included.

  Reference:

  https://learn.microsoft.com/en-us/azure/machine-learning/how-to-log-view-metrics

• Question 402:

  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

  Correct Answer: B

  How batch deployment works with low priority VMs

  Azure Machine Learning Batch Deployments provides several capabilities that make it easy to consume and benefit from low priority VMs:

  *

  Batch deployment jobs consume low priority VMs by running on Azure Machine Learning compute clusters created with low priority VMs. Once a deployment is associated with a low priority VMs' cluster, all the jobs produced by such deployment will use low priority VMs. Per-job configuration is not possible.

  *

  Batch deployment jobs automatically seek the target number of VMs in the available compute cluster based on the number of tasks to submit. If VMs are preempted or unavailable, batch deployment jobs attempt to replace the lost capacity by queuing the failed tasks to the cluster.

  *

  Etc.

  Reference: https://learn.microsoft.com/en-us/azure/machine-learning/how-to-use-low-priority-batch

• Question 403:

  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

  Correct Answer: B

  Correct Solution: Create a managed online endpoint and set the value of its auth_mode parameter to key. Deploy the model to the online endpoint.

  Authentication mode: The authentication method for the endpoint. Choose between key-based authentication and Azure Machine Learning token-based authentication. A key doesn't expire, but a token does expire.

  Reference:

  https://learn.microsoft.com/en-us/azure/machine-learning/how-to-deploy-online-endpoints

• Question 404:

  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

  Correct Answer: D

  Use interpretability when you need to:

  Determine how trustworthy your AI system's predictions are by understanding what features are most important for the predictions. Approach the debugging of your model by understanding it first and identifying whether the model is using

  healthy features or merely false correlations. Uncover potential sources of unfairness by understanding whether the model is basing predictions on sensitive features or on features that are highly correlated with them.

  Etc.

  Note: Responsible AI dashboard components

  The Responsible AI dashboard brings together, in a comprehensive view, various new and pre-existing tools. The dashboard integrates these tools with Azure Machine Learning CLI v2, Azure Machine Learning Python SDK v2, and Azure

  Machine Learning studio. The tools include:

  *

  Model interpretability (importance values for aggregate and individual features), to understand your model's predictions and how those overall and individual predictions are made.

  *

  Counterfactual what-if, to observe how feature perturbations would affect your model predictions while providing the closest data points with opposing or different model predictions.

  *

  Causal analysis, to use historical data to view the causal effects of treatment features on real-world outcomes.

  *

  Etc.

  Reference: https://learn.microsoft.com/en-us/azure/machine-learning/how-to-machine-learning-interpretability https://learn.microsoft.com/en-us/azure/machine-learning/concept-responsible-ai-dashboard

• Question 405:

  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()

  Correct Answer: A

  Logging metrics

  Metrics, as opposite to parameters, are always numeric. The following table describes how to log specific numeric types:

  Log a numeric value (int or float)

  mlflow.log_metric("my_metric", 1)

  Log a numeric value (int or float) over time

  mlflow.log_metric("my_metric", 1, step=1)

  Use parameter step to indicate the step at which you are logging the metric value. It can be any integer number. It defaults to zero.

  Reference:

  https://learn.microsoft.com/en-us/azure/machine-learning/how-to-log-view-metrics

• Question 406:

  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.

  Correct Answer: A

  To prevent unnecessary files from being included in the snapshot, make an ignore file (.gitignore or .amlignore) in the directory. Add the files and directories to exclude to this file. For more information on the syntax to use inside this file, see syntax and patterns for .gitignore. The .amlignore file uses the same syntax. If both files exist, the .amlignore file is used and the .gitignore file is unused.

  Reference: https://learn.microsoft.com/en-us/azure/machine-learning/how-to-save-write-experiment-files

• Question 407:

  You build a data pipeline in an Azure Machine Learning workspace by using the Azure Machine Learning SDK for Python.

  You need to run a Python script as a pipeline step.

  Which two classes could you use? Each correct answer presents a complete solution.

  NOTE: Each correct selection is worth one point.

  A. PythonScriptStep

  B. AutoMLStep

  C. CommandStep

  D. StepRun

  Correct Answer: CD

  The steps Package contains pre-built steps that can be executed in an Azure Machine Learning Pipeline.

  Azure ML Pipeline steps can be configured together to construct a Pipeline, which represents a shareable and reusable Azure Machine Learning workflow. Each step of a pipeline can be configured to allow reuse of its previous run results if the step contents (scripts and dependencies) as well as inputs and parameters remain unchanged.

  D: The classes in this package are typically used together with the classes in the core package. The core package contains classes for configuring data (PipelineData), scheduling (Schedule), and managing the output of steps (StepRun).

  StepRun Class

  A run of a step in a Pipeline.

  This class can be used to manage, check status, and retrieve run details once the parent pipeline run is submitted and the pipeline has submitted the step run.

  Constructor

  Python:

  StepRun(experiment, step_run_id, pipeline_run_id, node_id, _service_endpoint=None, _is_reused=False, _current_node_id=None, _reused_run_id=None, _reused_node_id=None, _reused_pipeline_run_id=None, **kwargs)

  Classes include:

  * AutoMLStep (incorrect)

  Creates an Azure ML Pipeline step that encapsulates an automated ML run.

  Initialize an AutoMLStep.

  (C) * CommandStep Class

  Create an Azure ML Pipeline step that runs a command.

  Constructor Python:

  CommandStep(command=None, name=None, compute_target=None, runconfig=None, runconfig_pipeline_params=None, inputs=None, outputs=None, params=None, source_directory=None, allow_reuse=True, version=None)

  Reference: https://learn.microsoft.com/en-us/python/api/azureml-pipeline-core/azureml.pipeline.core.steprun https://learn.microsoft.com/en-us/python/api/azureml-pipeline-steps/azureml.pipeline.steps.commandstep

• Question 408:

  You develop a machine learning project on a local machine. The project uses the Azure Machine Learning SDK for Python. You use Git as version control for scripts.

  You submit a training run that returns a Run object.

  You need to retrieve the active Git branch for the training run.

  Which two code segments should you use? Each correct answer presents part of the solution.

  NOTE: Each correct selection is worth one point.

  A. details = run.get_environment()

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

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

  D. details = run.get_details()

  Correct Answer: BC

• Question 409:

  You are attaching an Azure Databricks-based compute resource to an Azure Machine Learning development workspace.

  You need to configure parameters to attach the resource.

  Which three parameters should you use? Each correct answer presents part of the solution.

  NOTE: Each correct selection is worth one point.

  A. Workspace name

  B. Compute name

  C. Workspace user credentials

  D. Workspace resource ID

  E. Access token

  Correct Answer: ABE

• Question 410:

  You create a binary classification model. You use the Fairlearn package to assess model fairness.

  You must eliminate the need to retrain the model.

  You need to implement the Fairlearn package.

  Which algorithm should you use?

  A. fairlearn.reductions.ExponentiatedGradient

  B. fairlearn.postprocessing.ThresholdOptimizer

  C. fairlearn.preprocessing.CorrelationRemover

  D. fairlearn.reductions.GridSearch

  Correct Answer: C