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Microsoft DP-100 Exam Syllabus Topics:

Section	Weight	Objectives
Design and prepare a machine learning solution	20-25%	- Manage compute resources 1. Create and configure compute targets 2. Select environments 3. Attach and monitor compute - Manage data assets 1. Create and maintain data assets 2. Register and manage datastores 3. Select storage services - Manage Azure Machine Learning workspace 1. Use developer tools and CLI 2. Work with registries 3. Set up Git integration 4. Create and configure workspace - Design a machine learning solution 1. Determine dataset structure and format 2. Define compute specifications for workloads 3. Plan model deployment requirements 4. Select development approach
Train and deploy models	25-30%	- Monitor and maintain models 1. Implement MLOps practices 2. Update and retrain models 3. Monitor performance and data drift - Manage models 1. Interpret models and explain predictions 2. Package and validate models 3. Register and version models - Train models 1. Run training scripts 2. Use HyperDrive for hyperparameter tuning 3. Apply responsible AI principles 4. Configure jobs and environments - Deploy models 1. Secure endpoints and manage access 2. Deploy to online endpoints 3. Deploy to batch endpoints 4. Configure compute and scaling
Explore data and run experiments	20-25%	- Run experiments 1. Track runs with MLflow 2. Configure experiment runs 3. Define parameters and configurations 4. Use automated machine learning - Implement pipelines 1. Build reusable components 2. Schedule and monitor pipelines 3. Create and publish pipelines 4. Pass data between steps - Explore and visualize data 1. Profile and validate data 2. Detect anomalies and outliers 3. Identify features and relationships
Optimize language models for AI applications	25-30%	- Optimize with Retrieval Augmented Generation 1. Configure Azure AI Search 2. Prepare and process data 3. Create vector stores and indexes - Evaluate and improve models 1. Test and evaluate responses 2. Optimize for accuracy and safety 3. Apply responsible generative AI - Implement generative AI solutions 1. Use Azure AI Foundry 2. Apply prompt engineering 3. Build prompt flows

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Microsoft Designing and Implementing a Data Science Solution on Azure Sample Questions (Q233-Q238):

NEW QUESTION # 233
You are creating data wrangling and model training solutions in an Azure Machine Learning workspace.
You must use the same Python notebook to perform both data wrangling and model training.
You need to use the Azure Machine Learning Python SDK v2 to define and configure the Synapse Spark pool asynchronously in the workspace as dedicated compute How should you complete the rode segment? To answer, select the appropriate options in the answer area.
NOTE: Lach correct selection is worth one point.

Answer:

Explanation:

Explanation

NEW QUESTION # 234
You deploy a real-time inference service for a trained model.
The deployed model supports a business-critical application, and it is important to be able to monitor the data submitted to the web service and the predictions the data generates.
You need to implement a monitoring solution for the deployed model using minimal administrative effort.
What should you do?

A. View the explanations for the registered model in Azure ML studio.
B. Enable Azure Application Insights for the service endpoint and view logged data in the Azure portal.
C. Create an ML Flow tracking URI that references the endpoint, and view the data logged by ML Flow.
D. View the log files generated by the experiment used to train the model.

Answer: B

Explanation:
Configure logging with Azure Machine Learning studio
You can also enable Azure Application Insights from Azure Machine Learning studio. When you're ready to deploy your model as a web service, use the following steps to enable Application Insights:
1. Sign in to the studio at https://ml.azure.com.
2. Go to Models and select the model you want to deploy.
3. Select +Deploy.
4. Populate the Deploy model form.
5. Expand the Advanced menu.
6. Select Enable Application Insights diagnostics and data collection.

Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/how-to-enable-app-insights

NEW QUESTION # 235
You have an Azure Machine Learning workspace.
You run the following code in a Python environment in which the configuration file for your workspace has been downloaded.

instructions: For each of the following statements, select Yes if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:

Explanation:

NEW QUESTION # 236
You have an Azure subscription named Sub1 that contains an Azure
* a registered MLflow model named Modell
* an online endpoint named Endpointl
Outbound network connectivity from Endpointl is blocked. You need to deploy ModeM to Endpointl. What should you do first?

A. In Subl, create a private endpoint
B. In Subl, create an Azure Machine Learning registry.
C. In Workspace! create a package.
D. In Workspacel. create a linked service.
E. In Workspacel. create a package.

Answer: A

NEW QUESTION # 237
You have a dataset that contains 2,000 rows. You are building a machine learning classification model by using Azure Learning Studio. You add a Partition and Sample module to the experiment.
You need to configure the module. You must meet the following requirements:
Divide the data into subsets
Assign the rows into folds using a round-robin method
Allow rows in the dataset to be reused
How should you configure the module? To answer, select the appropriate options in the dialog box in the answer area.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:

Explanation:

Use the Split data into partitions option when you want to divide the dataset into subsets of the data. This option is also useful when you want to create a custom number of folds for cross-validation, or to split rows into several groups.
Add the Partition and Sample module to your experiment in Studio (classic), and connect the dataset.
For Partition or sample mode, select Assign to Folds.
Use replacement in the partitioning: Select this option if you want the sampled row to be put back into the pool of rows for potential reuse. As a result, the same row might be assigned to several folds.
If you do not use replacement (the default option), the sampled row is not put back into the pool of rows for potential reuse. As a result, each row can be assigned to only one fold.
Randomized split: Select this option if you want rows to be randomly assigned to folds.
If you do not select this option, rows are assigned to folds using the round-robin method.
References:
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/partition-and-sample

NEW QUESTION # 238
......

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