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Microsoft DP-100 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Prepare a model for deployment | 20-25% | - Deploy machine learning models
|
| Deploy and retrain models | 10-15% | - Implement retraining pipelines
|
| Explore data and train models | 35-40% | - Prepare data for modeling
|
| Design and prepare a machine learning solution | 20-25% | - Prepare development environments
|
Microsoft Designing and Implementing a Data Science Solution on Azure Exam FAQ — Strong Support
100 minutes for 40-60 questions. The ExamTorrent SOFT engine simulates the formal test model, so each exercise set trains the pacing you'll need.
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Candidates should have experience with Azure services, Python programming, and machine learning frameworks such as Scikit-Learn, PyTorch, or TensorFlow. Eligibility rules change over time, so verify the current requirements on the official page (official DP-100 exam page) before registering.
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The Microsoft Designing and Implementing a Data Science Solution on Azure blueprint spans 4 domains — including Prepare a model for deployment (20-25%), Explore data and train models (35-40%), Deploy and retrain models (10-15%). All three versions of our material cover the same content, with no discrepancy; the complete outline above lists every subtopic.
The Microsoft Designing and Implementing a Data Science Solution on Azure is Microsoft's certification exam for Microsoft Azure, at the Associate level. It's a fundamental element for a higher position and a more promising future. Related credentials include Microsoft Certified: Azure Data Scientist Associate.
Microsoft Designing and Implementing a Data Science Solution on Azure Sample Questions:
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. You connect to a terminal session from the Notebooks page in Azure Machine Learning studio.
You plan to add a new Jupyter kernel that will be accessible from the same terminal session.
You need to perform the task that must be completed before you can add the new kernel.
Solution: Delete the Python 3.6 - AzureML kernel.
Does the solution meet the goal?
- A. No
- B. Yes
Correct Answer: A 🗳️
You manage an Azure Al Foundry project.
You plan 10 build a RAG solution. The solution must include two models:
* One for text output, named Model1. This model must resemble human language and read naturally.
* One for creating embeddings, named Model2. This model must maximize the retrieval of relevant results (high recall) You need to compare different models by using benchmarking metrics to select the appropriate models for Model1 and Model?
Correct Answer:
Explanation:
You create an Azure Machine Learning workspace.
You must implement dedicated compute for model training in the workspace by using Azure Synapse compute resources. The solution must attach the dedicated compute and start an Azure Synapse session.
You need to implement the compute resources.
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.
Correct Answer:
Explanation:
You create an Azure Machine Learning workspace.
You need to use the shared file system of the workspace to store a clone of a private Git repository.
Which four 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.
Correct Answer:
Explanation:
You manage an Azure Machine Learning workspace
You build an Azure Machine Learning pipeline for image classification by using custom components. You need to define the interface, metadata, and code to execute components from a Python function. Which function should you use?
- A. create_or_update()
- B. command_component()
- C. from config()
- D. Ioad_component()
Correct Answer: B 🗳️
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