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| Certification Vendor: | Microsoft |
|---|---|
| Exam Name: | Designing and Implementing a Data Science Solution on Azure |
| Exam Number: | DP-100 |
| Exam Format: | Multiple Choice, Drag and Drop, Interactive Tasks, Case Study, Lab |
| Exam Duration: | 100 minutes |
| Exam Price: | $165 USD |
| Passing Score: | 700/1000 |
| Related Certifications: | Microsoft Certified: Azure Data Scientist Associate |
| Certificate Validity Period: | 1 year |
| Available Languages: | German, Chinese (Simplified), Japanese, Korean, Spanish, Portuguese, English, French |
| Real Exam Qty: | 40-60 |
| Sample Questions: | Microsoft DP-100 Sample Questions |
| Exam Way: | Online proctored exam or test center delivery through Pearson VUE. |
| Pre Condition: | Candidates should have experience with Azure services, Python programming, and machine learning frameworks such as Scikit-Learn, PyTorch, or TensorFlow. |
| Official Syllabus URL: | https://learn.microsoft.com/en-us/credentials/certifications/exams/dp-100/ |
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마이크로소프트 DP-100 (Azure에서 데이터 과학 솔루션 설계 및 구현) 시험은 Azure 기술을 사용하여 데이터 과학 솔루션을 설계하고 구현하는 데 필요한 기술과 지식을 평가하는 자격증 시험입니다. 이 시험은 데이터 과학 분야에서 전문성을 증명하고 경력 기회를 향상시키고자 하는 전문가들에게 이상적입니다.
질문 # 404
You have a deployment of an Azure OpenAI Service base model.
You plan to fine-tune the model.
You need to prepare a file that contains training data.
Which file format should you use?
정답：A
질문 # 405
You need to define an evaluation strategy for the crowd sentiment models.
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.
정답：
설명：
Explanation:
Step 1: Define a cross-entropy function activation
When using a neural network to perform classification and prediction, it is usually better to use cross-entropy error than classification error, and somewhat better to use cross-entropy error than mean squared error to evaluate the quality of the neural network.
Step 2: Add cost functions for each target state.
Step 3: Evaluated the distance error metric.
References:
https://www.analyticsvidhya.com/blog/2018/04/fundamentals-deep-learning-regularization-techniques/
질문 # 406
You create a Python script named train.py and save it in a folder named scripts. The script uses the scikit-learn framework to train a machine learning model.
You must run the script as an Azure Machine Learning experiment on your local workstation.
You need to write Python code to initiate an experiment that runs the train.py script.
How should you complete the code segment? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
정답：
설명：
Reference:
https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.core.scriptrunconfig
질문 # 407
You are creating a machine learning model that can predict the species of a penguin from its measurements.
You have a file that contains measurements for free species of penguin in comma delimited format.
The model must be optimized for area under the received operating characteristic curve performance metric averaged for each class.
You need to use the Automated Machine Learning user interface in Azure Machine Learning studio to run an experiment and find the best performing model.
Which five 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 collect order.
정답：
설명：
Explanation:
질문 # 408
You are building an intelligent solution using machine learning models.
The environment must support the following requirements:
Data scientists must build notebooks in a cloud environment
Data scientists must use automatic feature engineering and model building in machine learning pipelines.
Notebooks must be deployed to retrain using Spark instances with dynamic worker allocation.
Notebooks must be exportable to be version controlled locally.
You need to create the environment.
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.
정답：
설명：
Explanation:
Step 1: Create an Azure HDInsight cluster to include the Apache Spark Mlib library Step 2: Install Microsot Machine Learning for Apache Spark You install AzureML on your Azure HDInsight cluster.
Microsoft Machine Learning for Apache Spark (MMLSpark) provides a number of deep learning and data science tools for Apache Spark, including seamless integration of Spark Machine Learning pipelines with Microsoft Cognitive Toolkit (CNTK) and OpenCV, enabling you to quickly create powerful, highly-scalable predictive and analytical models for large image and text datasets.
Step 3: Create and execute the Zeppelin notebooks on the cluster
Step 4: When the cluster is ready, export Zeppelin notebooks to a local environment.
Notebooks must be exportable to be version controlled locally.
References:
https://docs.microsoft.com/en-us/azure/hdinsight/spark/apache-spark-zeppelin-notebook
https://azuremlbuild.blob.core.windows.net/pysparkapi/intro.html
질문 # 409
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