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Microsoft DP-100 Exam Syllabus Topics:

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

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Microsoft Designing and Implementing a Data Science Solution on Azure Sample Questions (Q378-Q383):

NEW QUESTION # 378
You manage an Azure Machine Learning workspace named workspace1by using the Python SDK v2.
You must register datastores in workspace 1 for Azure Blot storage and Azure Fetes storage to meet the following requirements.
* Azure Active Directory (Azure AD) authentication must be used for access to storage when possible.
* Credentials and secrets steed in workspace1 must be valid lot 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.

Answer:

Explanation:

NEW QUESTION # 379
You use Azure Machine Learning to implement hyperparameter tuning for an Azure ML Python SDK v2-based model training.
Training runs must terminate when the primary metric is lowered by 25 percent or more compared to the best performing run.
You need to configure an early termination policy to terminate training jobs.
Which values should you use? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:

NEW QUESTION # 380
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 are creating a new experiment in Azure Machine Learning Studio.
One class has a much smaller number of observations than tin- other classes in the training set.
You need to select an appropriate data sampling strategy to compensate for the class imbalance.
Solution: You use the Principal Components Analysis (PCA) sampling mode.
Does the solution meet the goal?

A. No
B. Yes

Answer: A

Explanation:
Instead use the Synthetic Minority Oversampling Technique (SMOTE) sampling mode.
Note: SMOTE is used to increase the number of underepresented cases in a dataset used for machine learning.
SMOTE is a better way of increasing the number of rare cases than simply duplicating existing cases.

NEW QUESTION # 381
You are building a binary classification model by using a supplied training set.
The training set is imbalanced between two classes.
You need to resolve the data imbalance.
What are three possible ways to achieve this goal? Each correct answer presents a complete solution NOTE:
Each correct selection is worth one point.

A. Use accuracy as the evaluation metric of the model.
B. Generate synthetic samples in the minority class.
C. Normalize the training feature set.
D. Penalize the classification
E. Resample the data set using under sampling or oversampling

Answer: A,D,E

Explanation:
Explanation
References:
https://machinelearningmastery.com/tactics-to-combat-imbalanced-classes-in-your-machine-learning-dataset/

NEW QUESTION # 382
You manage an Azure Al Foundry project. You fine-tune the base model
During evaluation, you observe that the model is overfitting and its responses are highly varying You need to improve the fine-tuned model.
Which hyperparameters should you use? To answer, move the appropriate hyper para meters to the correct requirements. You may use each hyperparameter once, more than once, or not at all. You may need to move the split bar between panes or scroll to view content NOTE: Each correct selection is worth one point.

Answer:

Explanation:

Explanation:

NEW QUESTION # 383
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