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| Certification Vendor: | Microsoft |
|---|---|
| Exam Name: | Designing and Implementing a Data Science Solution on Azure |
| Exam Number: | DP-100 |
| Certificate Validity Period: | 1 year (renewable through Microsoft certification renewal assessment) |
| Exam Format: | Multiple response, Multiple choice, Hands-on lab tasks, Case studies, Drag and drop |
| Passing Score: | 700/1000 |
| Real Exam Qty: | 40-60 |
| Exam Duration: | 100-120 |
| Related Certifications: | Microsoft Certified: Azure Data Scientist Associate |
| Available Languages: | German, English, Portuguese (Brazil), Spanish, Simplified Chinese, Korean, French, Russian, Japanese |
| Exam Price: | USD 165 (varies by region) |
| Recommended Training: | Azure Machine Learning Documentation Microsoft Learn - DP-100 Learning Path |
| Exam Registration: | Pearson VUE Registration Official Microsoft Certification Page |
| Sample Questions: | Microsoft DP-100 Sample Questions |
| Exam Way: | Online proctored or in-person at authorized testing centers (Pearson VUE) |
| Pre Condition: | No mandatory prerequisites. Recommended familiarity with Python, machine learning concepts, and Azure fundamentals. |
| Official Syllabus URL: | https://learn.microsoft.com/en-us/credentials/certifications/azure-data-scientist/ |
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Microsoft인증 DP-100 시험은 최근 제일 인기있는 인증시험입니다. IT업계에 종사하시는 분들은 자격증취득으로 자신의 가치를 업그레이드할수 있습니다. Microsoft인증 DP-100 시험은 유용한 IT자격증을 취득할수 있는 시험중의 한과목입니다. ITDumpsKR에서 제공해드리는Microsoft인증 DP-100 덤프는 여러분들이 한방에 시험에서 통과하도록 도와드립니다. 덤프를 공부하는 과정은 IT지식을 더 많이 배워가는 과정입니다. 시험대비뿐만아니라 많은 지식을 배워드릴수 있는 덤프를ITDumpsKR에서 제공해드립니다. ITDumpsKR덤프는 선택하시면 성공을 선택한것입니다.
마이크로소프트 DP-100 자격증 시험은 Azure 서비스를 사용하여 데이터 과학 솔루션을 설계하고 구현하는 지식과 기술을 테스트하기 위해 설계되었습니다. 이 자격증은 데이터 과학자, 데이터 엔지니어 및 다른 이해 관계자들과 함께 기계 학습(ML) 및 인공 지능(AI) 솔루션을 개발하고 배포하는 개인을 대상으로 합니다.
질문 # 130
You are building an experiment using the Azure Machine Learning designer.
You split a dataset into training and testing sets. You select the Two-Class Boosted Decision Tree as the algorithm.
You need to determine the Area Under the Curve (AUC) of the model.
Which three modules should you use in sequence? To answer, move the appropriate modules from the list of modules to the answer area and arrange them in the correct order.
정답：
설명：
Explanation:
Step 1: Train Model
Two-Class Boosted Decision Tree
First, set up the boosted decision tree model.
1. Find the Two-Class Boosted Decision Tree module in the module palette and drag it onto the canvas.
2. Find the Train Model module, drag it onto the canvas, and then connect the output of the Two-Class Boosted Decision Tree module to the left input port of the Train Model module.
The Two-Class Boosted Decision Tree module initializes the generic model, and Train Model uses training data to train the model.
3. Connect the left output of the left Execute R Script module to the right input port of the Train Model module (in this tutorial you used the data coming from the left side of the Split Data module for training).
This portion of the experiment now looks something like this:
Step 2: Score Model
Score and evaluate the models
You use the testing data that was separated out by the Split Data module to score our trained models. You can then compare the results of the two models to see which generated better results.
Add the Score Model modules
1. Find the Score Model module and drag it onto the canvas.
2. Connect the Train Model module that's connected to the Two-Class Boosted Decision Tree module to the left input port of the Score Model module.
3. Connect the right Execute R Script module (our testing data) to the right input port of the Score Model module.
Step 3: Evaluate Model
To evaluate the two scoring results and compare them, you use an Evaluate Model module.
1. Find the Evaluate Model module and drag it onto the canvas.
2. Connect the output port of the Score Model module associated with the boosted decision tree model to the left input port of the Evaluate Model module.
3. Connect the other Score Model module to the right input port.
질문 # 131
You manage an Azure Al Foundry project.
You plan to evaluate a fine-tuned large language model by doing the following:
* Identifying discrepancies between runs of the same model to pinpoint the areas where adjustments may be needed.
* Verifying the Al-generated responses align with and are validated by the provided context.
You need to identify an evaluation metric and a comparison feature to assess the performance of the model.
Which assessment techniques should you use? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
정답：
설명：
Explanation:
질문 # 132
You are developing a linear regression model in Azure Machine Learning Studio. You run an experiment to compare different algorithms.
The following image displays the results dataset output:
Use the drop-down menus to select the answer choice that answers each question based on the information presented in the image.
NOTE: Each correct selection is worth one point.
정답：
설명：
Explanation
Box 1: Boosted Decision Tree Regression
Mean absolute error (MAE) measures how close the predictions are to the actual outcomes; thus, a lower score is better.
Box 2:
Online Gradient Descent: If you want the algorithm to find the best parameters for you, set Create trainer mode option to Parameter Range. You can then specify multiple values for the algorithm to try.
References:
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/evaluate-model
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/linear-regression
질문 # 133
You are using C-Support Vector classification to do a multi-class classification with an unbalanced training dataset. The C-Support Vector classification using Python code shown below:
You need to evaluate the C-Support Vector classification code.
Which evaluation statement should you use? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
정답：
설명：
Reference:
https://scikit-learn.org/stable/modules/generated/sklearn.svm.SVC.html
질문 # 134
You are developing a hands-on workshop to introduce Docker for Windows to attendees.
You need to ensure that workshop attendees can install Docker on their devices.
Which two prerequisite components should attendees install on the devices? Each correct answer presents part of the solution.
NOTE: Each correct selection is worth one point.
정답：B,C
설명：
C: Make sure your Windows system supports Hardware Virtualization Technology and that virtualization is enabled.
Ensure that hardware virtualization support is turned on in the BIOS settings. For example:
E: To run Docker, your machine must have a 64-bit operating system running Windows 7 or higher.
References:
https://docs.docker.com/toolbox/toolbox_install_windows/
https://blogs.technet.microsoft.com/canitpro/2015/09/08/step-by-step-enabling-hyper-v-for-use-on-windows-
10/
질문 # 135
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