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Microsoft DP-100 Exam Syllabus Topics:

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

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Microsoft Designing and Implementing a Data Science Solution on Azure Sample Questions (Q64-Q69):

NEW QUESTION # 64
You are building a recurrent neural network to perform a binary classification.
You review the training loss, validation loss, training accuracy, and validation accuracy for each training epoch.
You need to analyze model performance.
You need to identify whether the classification model is overfitted.
Which of the following is correct?

A. The training loss decreases while the validation loss increases when training the model.
B. The training loss stays constant and the validation loss stays on a constant value and close to the training loss value when training the model.
C. The training loss stays constant and the validation loss decreases when training the model.
D. The training loss increases while the validation loss decreases when training the model.

Answer: A

Explanation:
An overfit model is one where performance on the train set is good and continues to improve, whereas performance on the validation set improves to a point and then begins to degrade.
Reference:
https://machinelearningmastery.com/diagnose-overfitting-underfitting-lstm-models/

NEW QUESTION # 65
You manage an Azure Machine Learning workspace. You train a model named model1.
You must identify the features to modify for a differing model prediction result.
You need to configure the Responsible Al (RAI) dashboard for model1.
Which three 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.

Answer:

Explanation:

Explanation:

NEW QUESTION # 66
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 have a Python script named train.py in a local folder named scripts. The script trains a regression model by using scikit-learn. The script includes code to load a training data file which is also located in the scripts folder.
You must run the script as an Azure ML experiment on a compute cluster named aml-compute.
You need to configure the run to ensure that the environment includes the required packages for model training. You have instantiated a variable named aml-compute that references the target compute cluster.
Solution: Run the following code:

Does the solution meet the goal?

A. Yes
B. No

Answer: B

Explanation:
The scikit-learn estimator provides a simple way of launching a scikit-learn training job on a compute target. It is implemented through the SKLearn class, which can be used to support single-node CPU training.
Example:
from azureml.train.sklearn import SKLearn
}
estimator = SKLearn(source_directory=project_folder,
compute_target=compute_target,
entry_script='train_iris.py'
)
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/how-to-train-scikit-learn

NEW QUESTION # 67
You create an Azure Machine Learning dataset containing automobile price data The dataset includes 10,000 rows and 10 columns You use Azure Machine Learning Designer to transform the dataset by using an Execute Python Script component and custom code.
The code must combine three columns to create a new column.
You need to configure the code function.
Which configurations should you use? lo answer, select the appropriate options in the answer area NOTE: Each correct selection is worth one point.

Answer:

Explanation:

Explanation:

NEW QUESTION # 68
You have an Azure Machine Learning workspace and a data source file ./data/cc_data.csv in the local storage.
You plan to use Azure Machine Learning Python SDK v2 to store the content of the cc.data.csv file in a data asset named cc_data_asset in the workspace.
You write code to connect to the workspace and import all required libraries.
You need to complete the remaining code to ensure it will result in the cc_data_asset that contains the data from cc_data.csv.
How should you complete the code? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:

Explanation:

NEW QUESTION # 69
......

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