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Retirement date: none
This exam measures your ability to accomplish the following technical tasks: manage Azure resources for machine learning; run experiments and train models; deploy and operationalize machine learning solutions; and implement responsible machine learning.
Reference: https://www.microsoft.com/en-us/learning/exam-dp-100.aspx
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3. Models Management and Optimization (20-25%):
- Usage of hyperdrive for the tuning of hyperparameters: This domain will evaluate the ability of the applicants to define search space, primary metrics, and early termination alternatives. It also expects their skills in sampling techniques selection and model discovery that require optimal hyper-parameter values.
- Usage of model explainers for the interpretation of models: The learners have to demonstrate their competence in choosing model interpreters and generating the features of important data.
- Usage of Automated Machine Learning for the creation of optimal models: This section requires your skills in retrieving the best models and getting data for Automated ML runs. It also covers competence in defining primary metrics, selecting pre-processing alternatives, and determining the algorithms to be searched. The candidates should be able to use the Automated Machine Learning from Azure ML SDK as well as Automated ML interface within Azure ML studios.
- Models management: This objective focuses on trained model registration and monitoring of data drift and model usage.
Microsoft DP-100 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Deploy and retrain models | 10-15% | - Implement retraining pipelines
|
| Topic 2: Design and prepare a machine learning solution | 20-25% | - Prepare development environments
|
| Topic 3: Explore data and train models | 35-40% | - Optimize model performance
|
| Topic 4: Prepare a model for deployment | 20-25% | - Manage deployment assets
|
Microsoft Designing and Implementing a Data Science Solution on Azure Sample Questions:
You create a pipeline in designer to train a model that predicts automobile prices.
Because of non-linear relationships in the data, the pipeline calculates the natural log (Ln) of the prices in the training data, trains a model to predict this natural log of price value, and then calculates the exponential of the scored label to get the predicted price.
The training pipeline is shown in the exhibit. (Click the Training pipeline tab.) Training pipeline
You create a real-time inference pipeline from the training pipeline, as shown in the exhibit. (Click the Real- time pipeline tab.) Real-time pipeline
You need to modify the inference pipeline to ensure that the web service returns the exponential of the scored label as the predicted automobile price and that client applications are not required to include a price value in the input values.
Which three modifications must you make to the inference pipeline? Each correct answer presents part of the solution.
NOTE: Each correct selection is worth one point.
- A. Replace the training dataset module with a data input that does not include the price column.
- B. Remove the Apply SQL Transformation module from the data flow.
- C. Add a Select Columns module before the Score Model module to select all columns other than price.
- D. Remove the Apply Math Operation module that replaces price with its natural log from the data flow.
- E. Replace the Web Service Input module with a data input that does not include the price column.
- F. Connect the output of the Apply SQL Transformation to the Web Service Output module.
Correct Answer: C,D,F 🗳️
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 use Azure Machine Learning designer to load the following datasets into an experiment:
You need to create a dataset that has the same columns and header row as the input datasets and contains all rows from both input datasets.
Solution: Use the Add Rows module.
Does the solution meet the goal?
- A. No
- B. Yes
Correct Answer: A 🗳️
You use Azure Machine Learning to implement hyperparameter tuning for an Azure ML Python SDK v2- based model training.
Training runs must terminate when the primary metric is lowered by 25 percent or more compared to the best performing run.
You need to configure an early termination policy to terminate training jobs.
Which values should you use? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Correct Answer:
Explanation:
You have an Azure Machine Learning workspace. You plan to tune model hyperparameters by using a sweep job.
You need to find a sampling method that supports early termination of low-performance jobs and continuous hyperpara meters.
Solution: Use the Bayesian sampling method over the hyperparameter space.
Does the solution meet the goal?
- A. No
- B. Yes
Correct Answer: B 🗳️
You manage an Azure Machine Learning workspace. You configure an automated machine learning regression training job by using the Azure Machine Learning Python SDK v2. You configure the regression job by using the following script:
For each of the following statements, select Yes if the statement is true. Otherwise, select No.
Correct Answer:
Explanation:
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