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Microsoft DP-100 Exam Syllabus Topics:

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

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DP-100 Übungsfragen: Designing and Implementing a Data Science Solution on Azure & DP-100 Dateien Prüfungsunterlagen

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Microsoft Designing and Implementing a Data Science Solution on Azure DP-100 Prüfungsfragen mit Lösungen (Q132-Q137):

132. Frage
You are implementing hyperparameter tuning by using Bayesian sampling for an Azure ML Python SDK v2-based model training from a notebook. The notebook is in an Azure Machine Learning workspace. The notebook uses a training script that runs on a compute cluster with 20 nodes.
The code implements Bandit termination policy with slack_factor set to 0.2 and a sweep job with max_concurrent_trials set to 10.
You must increase effectiveness of the tuning process by improving sampling convergence.
You need to select which sampling convergence to use.
What should you select?

A. Set the value of slack_factor of early_termination policy to 0.1.
B. Set the value of max_concurrent_trials to 20.
C. Set the value of slack_factor of early_termination policy to 0.9.
D. Set the value of max_concurrent_trials to 4.

Antwort: D

133. Frage
You have the following code. The code prepares an experiment to run a script:

The experiment must be run on local computer using the default environment.
You need to add code to start the experiment and run the script.
Which code segment should you use?

A. run = Run(experiment=script_experiment)
B. run = script_experiment.submit(config=script_config)
C. run = script_experiment.start_logging()
D. ws.get_run(run_id=experiment.id)

Antwort: B

Begründung:
The experiment class submit method submits an experiment and return the active created run.
Syntax: submit(config, tags=None, **kwargs)
Reference:
https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.core.experiment.experiment

134. Frage
You train and register a model by using the Azure Machine Learning SDK on a local workstation. Python 3.6 and Visual Studio Code are installed on the workstation.
When you try to deploy the model into production as an Azure Kubernetes Service (AKS)-based web service, you experience an error in the scoring script that causes deployment to fail.
You need to debug the service on the local workstation before deploying the service to production.
Which four 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.

Antwort:

Begründung:

Explanation:

Step 1: Install Docker on the workstation
Prerequisites include having a working Docker installation on your local system.
Build or download the dockerfile to the compute node.
Step 2: Create an AksWebservice deployment configuration and deploy the model to it To deploy a model to Azure Kubernetes Service, create a deployment configuration that describes the compute resources needed.
# If deploying to a cluster configured for dev/test, ensure that it was created with enough
# cores and memory to handle this deployment configuration. Note that memory is also used by
# things such as dependencies and AML components.
deployment_config = AksWebservice.deploy_configuration(cpu_cores = 1, memory_gb = 1) service = Model.deploy(ws, "myservice", [model], inference_config, deployment_config, aks_target) service.wait_for_deployment(show_output = True) print(service.state) print(service.get_logs()) Step 3: Create a LocalWebservice deployment configuration for the service and deploy the model to it To deploy locally, modify your code to use LocalWebservice.deploy_configuration() to create a deployment configuration. Then use Model.deploy() to deploy the service.
Step 4: Debug and modify the scoring script as necessary. Use the reload() method of the service after each modification.
During local testing, you may need to update the score.py file to add logging or attempt to resolve any problems that you've discovered. To reload changes to the score.py file, use reload(). For example, the following code reloads the script for the service, and then sends data to it.
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/how-to-deploy-azure-kubernetes-service
https://docs.microsoft.com/en-us/azure/machine-learning/how-to-troubleshoot-deployment-local

135. Frage
You are using the Hyperdrive feature in Azure Machine Learning to train a model.
You configure the Hyperdrive experiment by running the following code:

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

Antwort:

Begründung:

Explanation:

Box 1: Yes
In random sampling, hyperparameter values are randomly selected from the defined search space. Random sampling allows the search space to include both discrete and continuous hyperparameters.
Box 2: Yes
learning_rate has a normal distribution with mean value 10 and a standard deviation of 3.
Box 3: No
keep_probability has a uniform distribution with a minimum value of 0.05 and a maximum value of 0.1.
Box 4: No
number_of_hidden_layers takes on one of the values [3, 4, 5].
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/how-to-tune-hyperparameters

136. Frage
You create an Azure Machine Learning workspace.
You need to use the shared file system of the workspace to store a clone of a private Git repository.
Which four 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.

Antwort:

Begründung:

Explanation

137. Frage
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