Testing Process Details of Microsoft DP-100
As for the exam format, there are usually about 40-60 multiple-choice, active screen, best answer, hot area, build lists, labs, mark review and other question styles. You are expected to complete them in 3 hours. The passing score is 700 out of 1000 maximum possible points. The test is available in the following languages: English, Japanese, Korean, and Chinese (Simplified). To schedule your test, you should first pay the registration fee of $165 and go to the Pearson VUE platform to create an account. Once you log in, choose the “Proctored Exams” section and enter ‘DP-100' as the exam code. Follow the prompts to complete registration. At this stage, you can choose to take your test offline or online. Notice, though, that if you don't succeed on the first try, you'll be required to pay the enrollment fee for the next attempt as well.
Reference: https://www.microsoft.com/en-us/learning/exam-dp-100.aspx
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How to Register For Exam DP-100: Designing and Implementing a Data Science Solution on Azure?
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Microsoft DP-100 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Explore data and train models | 35-40% | - Prepare data for modeling
|
| Deploy and retrain models | 10-15% | - Implement retraining pipelines
|
| Design and prepare a machine learning solution | 20-25% | - Design an Azure Machine Learning workspace
|
| Prepare a model for deployment | 20-25% | - Manage deployment assets
|
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 create an Azure Machine Learning service datastore in a workspace. The datastore contains the following files:
* /data/2018/Q1.csv
* /data/2018/Q2.csv
* /data/2018/Q3.csv
* /data/2018/Q4.csv
* /data/2019/Q1.csv
All files store data in the following format:
id,f1,f2i
1,1.2,0
2,1,1,
1 3,2.1,0
You run the following code:
You need to create a dataset named training_data and load the data from all files into a single data frame by using the following code:
Solution: Run the following code:
Does the solution meet the goal?
- A. Yes
- B. No
Correct Answer: B 🗳️
Explanation: Only visible for BootcampPDF members. You can sign-up / login (it's free).
You manage an Azure Machine Learning workspace. You build a model for which you must configure a Responsible Al dashboard. Based on what you learn from the dashboard, you must perform the following activities:
* Determine what must be done to get a desirable outcome from the model.
* Identify the features that have the most direct effect on your outcome of interest.
You need to select the components to use for the Responsible Al dashboard configuration. Which two components should you add? Each correct answer presents part of the solution. NOTE: Each correct selection is worth one point.
- A. explanation
- B. causal
- C. error analysis
- D. counterfactuals
Correct Answer: B,D 🗳️
You manage an Azure Machine Learning workspace. You develop a regression model training pipeline by using Notebooks. You need to determine the appropriate evaluation metric for the experiment.
Which two metrics should you choose? Each correct answer presents a complete solution. Choose two.
NOTE: Each correct selection is worth one point.
- A. correlation
- B. residuals
- C. precision
- D. recall
Correct Answer: A,B 🗳️
You ate designing a training job in an Azure Machine Learning workspace by using Automated ML During training, the compute resource must scale up to handle larger datasets. You need to select the compute resource that has a multi-node cluster that automatically scales Which Azure Machine Learning compute target should you use?
- A. Endpoints
- B. Serverless compute
- C. Compute instance
- D. Kubernetes cluster
Correct Answer: B 🗳️
You manage an Azure Al Foundry project.
You plan to develop a RAG solution from a set of PDF files. To achieve this, you plan to create a vector index from the data. You need to select the location of the data you plan to index.
Which two data sources can you use? Each correct answer presents a complete solution. Choose two. NOTE:
Each correct selection is worth one point.
- A. Azure Data Lake Storage Gen2
- B. OneLake in Microsoft Fabric
- C. Azure Blob Storage
- D. Data in Azure Al Foundry
Correct Answer: A,C 🗳️
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