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Microsoft DP-100 Exam Syllabus Topics:

Section	Weight	Objectives
Prepare a model for deployment	20-25%	- Manage deployment assets 1. Register models 2. Create inference configurations - Deploy machine learning models 1. Deploy real-time inference endpoints 2. Deploy batch inference pipelines
Deploy and retrain models	10-15%	- Implement retraining pipelines 1. Manage ML pipelines 2. Create scheduled retraining workflows - Monitor deployed models 1. Monitor model performance 2. Track data drift
Design and prepare a machine learning solution	20-25%	- Design an Azure Machine Learning workspace 1. Configure security and access 2. Configure workspace resources 3. Manage compute resources - Prepare development environments 1. Configure environments 2. Use SDKs and notebooks
Explore data and train models	35-40%	- Prepare data for modeling 1. Ingest and transform data 2. Manage datasets and datastores - Optimize model performance 1. Evaluate models 2. Improve accuracy and performance - Run experiments and train models 1. Track experiments 2. Perform hyperparameter tuning 3. Use automated machine learning

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Microsoft Designing and Implementing a Data Science Solution on Azure Sample Questions (Q393-Q398):

NEW QUESTION # 393
You are the owner of an Azure Machine Learning workspace.
You must prevent the creation or deletion of compute resources by using a custom role. You must allow all other operations inside the workspace.
You need to configure the custom role.
How should you complete the configuration? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:

Explanation
Graphical user interface, application Description automatically generated

Graphical user interface, application Description automatically generated

Box 1: Microsoft.MachineLearningServices/workspaces/*/read
Reader role: Read-only actions in the workspace. Readers can list and view assets, including datastore credentials, in a workspace. Readers can't create or update these assets.
Box 2: Microsoft.MachineLearningServices/workspaces/*/write
If the roles include Actions that have a wildcard (*), the effective permissions are computed by subtracting the NotActions from the allowed Actions.
Box 3: Box 2: Microsoft.MachineLearningServices/workspaces/computes/*/delete Box 4: Microsoft.MachineLearningServices/workspaces/computes/*/write Reference:
https://docs.microsoft.com/en-us/azure/role-based-access-control/overview#how-azure-rbac-determines-if-a-user

NEW QUESTION # 394
You manage an Azure Machine Learning workspace.
You need to define an environment from a Docker image by using the Azure Machine Learning Python SDK v2.
Which parameter should you use?

A. image
B. properties
C. conda_file
D. build

Answer: D

Explanation:
The main difference between using the build and image parameters when defining an environment using the Azure Machine Learning Python SDK v2 is whether you want to define the environment from a Dockerfile or a Docker image.
If you want to define an environment from a Dockerfile, you should use the build parameter and set it to a dictionary that specifies the contents of the Dockerfile using the dockerfile key.
If you want to define an environment from a Docker image, you should use the image parameter and set it to the name of the Docker image you want to use.

NEW QUESTION # 395
You are a data scientist creating a linear regression model.
You need to determine how closely the data fits the regression line.
Which metric should you review?

A. Recall
B. Mean absolute error
C. Root Mean Square Error
D. Precision
E. Coefficient of determination

Answer: E

Explanation:
Explanation
Coefficient of determination, often referred to as R2, represents the predictive power of the model as a value between 0 and 1. Zero means the model is random (explains nothing); 1 means there is a perfect fit. However, caution should be used in interpreting R2 values, as low values can be entirely normal and high values can be suspect.
References:
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/evaluate-model

NEW QUESTION # 396
You have an Azure Machine Learning workspace that includes an AmICompute 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: Create a datastore in the workspace.
Does the solution meet the goal?

A. Yes
B. No

Answer: B

NEW QUESTION # 397
You download a .csv file from a notebook in an Azure Machine Learning workspace to a data/sample.csv folder on a compute instance. The file contains 10,000 records. You must generate the summary statistics for the data in the file. The statistics must include the following for each numerical column:
* number of non-empty values
* average value
* standard deviation
* minimum and maximum values
* 25th. 50th. and 75th percentiles
You need to complete the Python code that will generate the summary statistics.
Which code segments should you use? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:

Explanation:

NEW QUESTION # 398
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