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Microsoft DP-100 Exam Reference
Reference: https://www.microsoft.com/en-us/learning/exam-dp-100.aspx
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Skills Covered
To nail DP-100, you will need to scrutinize the below-mentioned areas:
- Deploy and Consume Models
The last segment is all about deployment and consumption models. Topics like evaluating compute options, creating production compute targets, batch inferencing pipeline creation, and running this pipeline efficiently are well covered within such a scope.
- Manage and Optimize Models
Using automated ML for the optimal model creation, hyperdrive to tune hyperparameters, model management, and knowing the crucial model explainers to interpret models are some of the key topics explained in this portion.
- Execute Experiments & Train Models
This objective imparts updated understanding about the concepts like creating models by using Azure ML Designer, custom code modules in Designer, defining a pipeline data flow, and an experiment running by using Azure Machine Learning SDK.
- Set up Azure ML Workspace
The first domain gives considerable attention to skills related to the Azure ML workspace. So, the test-takers have a chance to learn about workspace settings, the management of workspace using Azure ML, and registering in addition to maintaining the datastores.
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Microsoft DP-100 Exam Syllabus Topics:
| Section | Objectives |
|---|---|
| Design and prepare a machine learning solution | - Select appropriate Azure services for machine learning workloads - Manage compute and data assets - Plan and configure Azure Machine Learning workspace |
| Optimize and manage models | - Track experiments and manage model lifecycle - Improve model performance |
| Train machine learning models | - Train models using Azure Machine Learning - Tune hyperparameters and evaluate models |
| Explore and analyze data | - Perform exploratory data analysis - Ingest and prepare data for modeling |
| Deploy and consume models | - Deploy models to endpoints - Monitor deployed models and endpoints |
Microsoft Designing and Implementing a Data Science Solution on Azure Sample Questions:
You plan to implement a two-step pipeline by using the Azure Machine Learning SDK for Python.
The pipeline will pass temporary data from the first step to the second step.
You need to identify the class and the corresponding method that should be used in the second step to access temporary data generated by the first step in the pipeline.
Which class and method should you identify? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point
Correct Answer:
Explanation:
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. Model files
- B. Environment
- C. Online endpoint
- D. Compute target
- E. Kubernetes online endpoint
Correct Answer: A,D 🗳️
You manage an Azure Machine Learning workspace by using the Azure CLI ml extension v2. You need to define a YAML schema to create a compute cluster. Which schema should you use?
- A. https://azuremlschemas.azureedge.net/latest/amlCompute.schemajson
- B. https://azuremlschemas.azureedge.net/latest/computdnstarKeichema.json
- C. https://azuremlschemas.azureedge.net/latest/vmCompute.schema.json
- D. https://azuremlschemas.azureedge.net/latest/kubernetesCompute.schema.json
Correct Answer: A 🗳️
You manage an Azure Machine Learning workspace. The development environment tor managing the workspace is configured to use Python SDK v2 in Azure Machine Learning Notebooks A Synapse Spark Compute is currently attached and uses system-assigned identity You need to use Python code to update the Synapse Spark Compute 10 use a user-assigned identity.
Solution: Configure the IdentityConfiguration class with the appropriate identity type.
Does the solution meet the goal?
- A. No
- B. Yes
Correct Answer: B 🗳️
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 serveriess 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.
Correct Answer:
Explanation:
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