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Microsoft DP-100 Exam Syllabus Topics:

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

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Microsoft Designing and Implementing a Data Science Solution on Azure DP-100 Prüfungsfragen mit Lösungen (Q138-Q143):

138. Frage
Drag and Drop Question
You manage an Azure Machine Learning workspace named workspace1 by using the Python SDK v2.
You must register datastores in workspace1 for Azure Blob storage and Azure Files storage to meet the following requirements:
- Azure Active Directory (Azure AD) authentication must be used for
access to storage when possible.
- Credentials and secrets stored in workspace1 must be valid for a
specified time period when accessing Azure Files storage.
You need to configure a security access method used to register the Azure Blob and Azure Files storage in workspace1.
Which security access method should you configure? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Antwort:

Begründung:

Explanation:
Azure Blob storage: Identity-based access (Azure AD authentication) allows you to use Azure AD credentials to authenticate and access Azure Blob storage. This is the recommended and more secure method for accessing Blob storage, as it avoids exposing account keys directly.
Azure File Storage: Shared access signature (SAS) is used to provide temporary access to resources in Azure File Storage. When using SAS, you can generate a token with specific permissions and expiration time, which can be used to access the Azure File share. This approach allows you to control the access to the file share for a specified time period without exposing the storage account key.

139. Frage
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 train a classification model by using a logistic regression algorithm.
You must be able to explain the model's predictions by calculating the importance of each feature, both as an overall global relative importance value and as a measure of local importance for a specific set of predictions.
You need to create an explainer that you can use to retrieve the required global and local feature importance values.
Solution: Create a TabularExplainer.
Does the solution meet the goal?

A. No
Instead use Permutation Feature Importance Explainer (PFI).
Note 1:

Note 2: Permutation Feature Importance Explainer (PFI): Permutation Feature Importance is a technique used to explain classification and regression models. At a high level, the way it works is by randomly shuffling data one feature at a time for the entire dataset and calculating how much the performance metric of interest changes. The larger the change, the more important that feature is. PFI can explain the overall behavior of any underlying model but does not explain individual predictions.
B. Yes

Antwort: A

Begründung:
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/how-to-machine-learning-interpretability

140. Frage
You have a dataset that contains over 150 features. You use the dataset to train a Support Vector Machine (SVM) binary classifier.
You need to use the Permutation Feature Importance module in Azure Machine Learning Studio to compute a set of feature importance scores for the dataset.
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.

Antwort:

Begründung:

1 - Add a Two-Class Support Vector Machine module to initialize the SVM classifier.
2 - Add a dataset to the experiment
3 - Add a Split Data module to create training and test dataset.
4 - Add a Permutation Feature Importance module and connect to the trained model and test dataset.
5 - Set the Metric for measuring performance property to Classification - Accuracy and then run the experiment.
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/two-class-support-vector-machine
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141. Frage
You have an Azure Machine Learning workspace and a serverless Spark compute resource.
You plan to run the same Spark session every 40 minutes. Each session runs for 15 minutes.
During testing, you observe a 15-minute delay at the start of the Spark sessions.
You need to reduce the delay to less than 1 minute.
What should you do?

A. Enable an isolated compute.
B. Configure the session timeout to be 25 minutes.
C. Enable dynamically allocated executors.
D. Increase the number of nodes to 16.

Antwort: B

Begründung:
By configuring the session timeout to be 25 minutes, you allow the Spark session to remain active between runs, preventing it from shutting down between each 40-minute interval. Since each session runs for 15 minutes, setting the timeout to 25 minutes keeps the session warm and reduces the initialization delay.

142. Frage
A coworker registers a datastore in a Machine Learning services workspace by using the following code:

You need to write code to access the datastore from a notebook.

Antwort:

Begründung:

Explanation:

Box 1: DataStore
To get a specific datastore registered in the current workspace, use the get() static method on the Datastore class:
# Get a named datastore from the current workspace
datastore = Datastore.get(ws, datastore_name='your datastore name')
Box 2: ws
Box 3: demo_datastore
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/how-to-access-data

143. Frage
......

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