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Microsoft DP-100 Exam Syllabus Topics:

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

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Microsoft Designing and Implementing a Data Science Solution on Azure Sample Questions (Q447-Q452):

NEW QUESTION # 447
You are developing a linear regression model in Azure Machine Learning Studio. You run an experiment to compare different algorithms.
The following image displays the results dataset output:

Use the drop-down menus to select the answer choice that answers each question based on the information presented in the image.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:

Explanation

Box 1: Boosted Decision Tree Regression
Mean absolute error (MAE) measures how close the predictions are to the actual outcomes; thus, a lower score is better.
Box 2:
Online Gradient Descent: If you want the algorithm to find the best parameters for you, set Create trainer mode option to Parameter Range. You can then specify multiple values for the algorithm to try.
References:
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/evaluate-model
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/linear-regression

NEW QUESTION # 448
You tram and register a model by using the Azure Machine Learning Python SDK v2 in a local workstation.
Python 3.7 and Visual Studio Code are instated on the workstation.
When you try to deploy the model into production to a Kubernetes online endpoint 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 three actions should you perform m sequence? To answer, move the appropriate actions from the list of actions from the answer area and arrange them in the correct order.

Answer:

Explanation:

Explanation:

NEW QUESTION # 449
You create an Azure Machine Learning workspace. You are preparing a local Python environment on a laptop computer. You want to use the laptop to connect to the workspace and run experiments.
You create the following config.json file.

You must use the Azure Machine Learning SDK to interact with data and experiments in the workspace.
You need to configure the config.json file to connect to the workspace from the Python environment.
Which two additional parameters must you add to the config.json file in order to connect to the workspace? Each correct answer presents part of the solution.
NOTE: Each correct selection is worth one point.

A. resource_group
B. region
C. key
D. login
E. subscription_id

Answer: A,E

Explanation:
To use the same workspace in multiple environments, create a JSON configuration file. The configuration file saves your subscription (subscription_id), resource (resource_group), and workspace name so that it can be easily loaded.
The following sample shows how to create a workspace.
from azureml.core import Workspace
ws = Workspace.create(name='myworkspace',
subscription_id='<azure-subscription-id>',
resource_group='myresourcegroup',
create_resource_group=True,
location='eastus2'
)
Reference:
https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.core.workspace.workspace

NEW QUESTION # 450
You use the following code to run a script as an experiment in Azure Machine Learning:

You must identify the output files that are generated by the experiment run.
You need to add code to retrieve the output file names.
Which code segment should you add to the script?

A. files = run.get_properties()
B. files= run.get_file_names()
C. files = run.get_details_with_logs()
D. files = run.get_metrics()
E. files = run.get_details()

Answer: B

Explanation:
You can list all of the files that are associated with this run record by called run.get_file_names() Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/how-to-track-experiments

NEW QUESTION # 451
You plan to preprocess text from CSV files. You load the Azure Machine Learning Studio default stop words list.
You need to configure the Preprocess Text module to meet the following requirements:
Ensure that multiple related words from a single canonical form.
Remove pipe characters from text.
Remove words to optimize information retrieval.
Which three options should you select? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:

Explanation:

Box 1: Remove stop words
Remove words to optimize information retrieval.
Remove stop words: Select this option if you want to apply a predefined stopword list to the text column.
Stop word removal is performed before any other processes.
Box 2: Lemmatization
Ensure that multiple related words from a single canonical form.
Lemmatization converts multiple related words to a single canonical form Box 3: Remove special characters Remove special characters: Use this option to replace any non-alphanumeric special characters with the pipe | character.
References:
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/preprocess-text

NEW QUESTION # 452
......

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