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Microsoft DP-100 Exam Syllabus Topics:

SectionWeightObjectives
Prepare a model for deployment20-25%- Manage deployment assets
  • 1. Register models
  • 2. Create inference configurations
- Deploy machine learning models
  • 1. Deploy batch inference pipelines
  • 2. Deploy real-time inference endpoints
Deploy and retrain models10-15%- Monitor deployed models
  • 1. Track data drift
  • 2. Monitor model performance
- Implement retraining pipelines
  • 1. Create scheduled retraining workflows
  • 2. Manage ML pipelines
Design and prepare a machine learning solution20-25%- Prepare development environments
  • 1. Use SDKs and notebooks
  • 2. Configure environments
- Design an Azure Machine Learning workspace
  • 1. Configure workspace resources
  • 2. Manage compute resources
  • 3. Configure security and access
Explore data and train models35-40%- Optimize model performance
  • 1. Improve accuracy and performance
  • 2. Evaluate models
- Prepare data for modeling
  • 1. Manage datasets and datastores
  • 2. Ingest and transform data
- Run experiments and train models
  • 1. Use automated machine learning
  • 2. Perform hyperparameter tuning
  • 3. Track experiments

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Microsoft Designing and Implementing a Data Science Solution on Azure Sample Questions (Q283-Q288):

NEW QUESTION # 283
Hotspot Question
You are performing a classification task in Azure Machine learning Studio.
You must prepare balanced testing and training samples based on a provided data set.
Warning samples based on a provided data set.
You need to split the data with a 0.75:0.25.
Which value should you use for each parameter? To answer, select the appropriate options m the answer area.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:

Explanation:
Box 1: Split rows
Use the Split Rows option if you just want to divide the data into two parts. You can specify the percentage of data to put in each split, but by default, the data is divided 50-50.
You can also randomize the selection of rows in each group, and use stratified sampling. In stratified sampling, you must select a single column of data for which you want values to be apportioned equally among the two result datasets.
Box 2: 0.75
If you specify a number as a percentage, or if you use a string that contains the "%" character, the value is interpreted as a percentage. All percentage values must be within the range (0, 100), not including the values 0 and 100.
Box 3: Yes
To ensure splits are balanced.
Box 4: No
If you use the option for a stratified split, the output datasets can be further divided by subgroups, by selecting a strata column.
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/split-data


NEW QUESTION # 284
You need to identify the methods for dividing the data according, to the testing requirements.
Which properties should you select? To answer, select the appropriate option-, m the answer area. NOTE:
Each correct selection is worth one point.

Answer:

Explanation:


NEW QUESTION # 285
You have an Azure Al Foundry project named Projects
You are developing a web classification Prompt flow named Flow1 in Project 1. The current input of Flow1 is defined as the following:

You plan to add a large language model (LLM) node named Nodel to Flowl. Nodel will use an input named url of type string to classify the url provided as Flowl input. In Nodel. you will add Jinja code to reference the value of its url input.

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