Unparalleled Microsoft - DP-100 - Exam Designing and Implementing a Data Science Solution on Azure Questions Pdf

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Microsoft DP-100 Exam Syllabus Topics:

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

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DP-100 Practical Information & Prep DP-100 Guide

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Microsoft Designing and Implementing a Data Science Solution on Azure Sample Questions (Q208-Q213):

NEW QUESTION # 208
You create a new Azure subscription. No resources are provisioned in the subscription.
You need to create an Azure Machine Learning workspace.
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 an Azure Resource Management template that includes a Microsoft.MachineLearningServices
/workspaces resource and its dependencies.
B. Use the Azure Command Line Interface (CLI) with the Azure Machine Learning extension to call the azgroup create function with --name and --location parameters, and then the az ml workspace createfunction, specifying -w and -g parameters for the workspace name and resource group.
C. Navigate to Azure Machine Learning studio and create a workspace.
D. Run Python code that uses the Azure ML SDK library and calls the Workspace.get method with name, subscription_id, and resource_group parameters.
E. Run Python code that uses the Azure ML SDK library and calls the Workspace.create method with name, subscription_id, resource_group, and location parameters.

Answer: A,B,C

Explanation:
B: You can use an Azure Resource Manager template to create a workspace for Azure Machine Learning.
Example:
{"type": "Microsoft.MachineLearningServices/workspaces",
...
C: You can create a workspace for Azure Machine Learning with Azure CLI Install the machine learning extension.
Create a resource group: az group create --name <resource-group-name> --location <location> To create a new workspace where the services are automatically created, use the following command: az ml workspace create -w <workspace-name> -g <resource-group-name> D: You can create and manage Azure Machine Learning workspaces in the Azure portal.
Sign in to the Azure portal by using the credentials for your Azure subscription.
In the upper-left corner of Azure portal, select + Create a resource.
Use the search bar to find Machine Learning.
Select Machine Learning.
In the Machine Learning pane, select Create to begin.

Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/how-to-create-workspace-template
https://docs.microsoft.com/en-us/azure/machine-learning/how-to-manage-workspace-cli
https://docs.microsoft.com/en-us/azure/machine-learning/how-to-manage-workspace

NEW QUESTION # 209
You are producing a multiple linear regression model in Azure Machine Learning Studio.
Several independent variables are highly correlated.
You need to select appropriate methods for conducting effective feature engineering on all the data.
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

Step 1: Use the Filter Based Feature Selection module
Filter Based Feature Selection identifies the features in a dataset with the greatest predictive power.
The module outputs a dataset that contains the best feature columns, as ranked by predictive power. It also outputs the names of the features and their scores from the selected metric.
Step 2: Build a counting transform
A counting transform creates a transformation that turns count tables into features, so that you can apply the transformation to multiple datasets.
Step 3: Test the hypothesis using t-Test
References:
https://docs.microsoft.com/bs-latn-ba/azure/machine-learning/studio-module-reference/filter-based-feature-selec
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/build-counting-transform

NEW QUESTION # 210
A biomedical research company plans to enroll people in an experimental medical treatment trial.
You create and train a binary classification model to support selection and admission of patients to the trial. The model includes the following features: Age, Gender, and Ethnicity.
The model returns different performance metrics for people from different ethnic groups.
You need to use Fairlearn to mitigate and minimize disparities for each category in the Ethnicity feature.
Which technique and constraint should you use? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:

Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/concept-fairness-ml

NEW QUESTION # 211
You create an Azure Databricks workspace and a linked Azure Machine Learning workspace.
You have the following Python code segment in the Azure Machine Learning workspace:
import mlflow
import mlflow.azureml
import azureml.mlflow
import azureml.core
from azureml.core import Workspace
subscription_id = 'subscription_id'
resourse_group = 'resource_group_name'
workspace_name = 'workspace_name'
ws = Workspace.get(name=workspace_name,
subscription_id=subscription_id,
resource_group=resource_group)
experimentName = "/Users/{user_name}/{experiment_folder}/{experiment_name}" mlflow.set_experiment(experimentName) uri = ws.get_mlflow_tracking_uri() mlflow.set_tracking_uri(uri) Instructions: For each of the following statements, select Yes if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:

Reference:
https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.core.workspace.workspace
https://docs.microsoft.com/en-us/azure/machine-learning/how-to-use-mlflow

NEW QUESTION # 212
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 an Azure Machine Learning workspace that includes an AmlCompute cluster and a batch endpoint.
You clone a repository that contains an MLflow model to your local computer.
You need to ensure that you can deploy the model to the batch endpoint.
Solution: Register the model in the workspace.
Does the solution meet the goal?

A. Yes
B. No

Answer: A

Explanation:
Registering the MLflow model in the Azure Machine Learning workspace is a necessary step before deploying the model to a batch endpoint. The model must be registered so that it can be referenced during deployment. Once registered, you can create a batch deployment that uses the model along with the appropriate compute resources. Therefore, registering the model meets the goal of ensuring that the model can be deployed to the batch endpoint.

NEW QUESTION # 213
......

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