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| Certification Vendor: | Microsoft |
|---|---|
| Exam Name: | Designing and Implementing a Data Science Solution on Azure |
| Exam Number: | DP-100 |
| Passing Score: | 700/1000 |
| Exam Format: | Drag and drop, Multiple response, Hands-on lab tasks, Case studies, Multiple choice |
| Exam Duration: | 100-120 |
| Related Certifications: | Microsoft Certified: Azure Data Scientist Associate |
| Available Languages: | German, French, Japanese, Spanish, Simplified Chinese, English, Korean, Russian, Portuguese (Brazil) |
| Real Exam Qty: | 40-60 |
| Certificate Validity Period: | 1 year (renewable through Microsoft certification renewal assessment) |
| Exam Price: | USD 165 (varies by region) |
| Recommended Training: | Microsoft Learn - DP-100 Learning Path Azure Machine Learning Documentation |
| 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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Microsoft DP-100 Certification Exam is ideal for data scientists, data analysts, and other professionals who work with data and want to validate their skills and knowledge in data science. It is also suitable for professionals who are looking to transition into the data science field and want to demonstrate their proficiency in this area. Designing and Implementing a Data Science Solution on Azure certification exam can help you stand out in the job market and increase your earning potential.
NEW QUESTION # 369
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.
Answer:
Explanation:
NEW QUESTION # 370
You train and register a model by using the Azure Machine Learning SDK on a local workstation. Python 3.6 and Visual Studio Code are installed on the workstation.
When you try to deploy the model into production as an Azure Kubernetes Service (AKS)-based web service, you experience an error in the scoring script that causes deployment to fail.
You need to debug the service on the local workstation before deploying the service to production.
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.
Answer:
Explanation:
Explanation:
Step 1: Install Docker on the workstation
Prerequisites include having a working Docker installation on your local system.
Build or download the dockerfile to the compute node.
Step 2: Create an AksWebservice deployment configuration and deploy the model to it To deploy a model to Azure Kubernetes Service, create a deployment configuration that describes the compute resources needed.
# If deploying to a cluster configured for dev/test, ensure that it was created with enough
# cores and memory to handle this deployment configuration. Note that memory is also used by
# things such as dependencies and AML components.
deployment_config = AksWebservice.deploy_configuration(cpu_cores = 1, memory_gb = 1) service = Model.deploy(ws, "myservice", [model], inference_config, deployment_config, aks_target) service.wait_for_deployment(show_output = True) print(service.state) print(service.get_logs()) Step 3: Create a LocalWebservice deployment configuration for the service and deploy the model to it To deploy locally, modify your code to use LocalWebservice.deploy_configuration() to create a deployment configuration. Then use Model.deploy() to deploy the service.
Step 4: Debug and modify the scoring script as necessary. Use the reload() method of the service after each modification.
During local testing, you may need to update the score.py file to add logging or attempt to resolve any problems that you've discovered. To reload changes to the score.py file, use reload(). For example, the following code reloads the script for the service, and then sends data to it.
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/how-to-deploy-azure-kubernetes-service
https://docs.microsoft.com/en-us/azure/machine-learning/how-to-troubleshoot-deployment-local
NEW QUESTION # 371
You need to identify the methods for dividing the data according, to the testing requirements.
Which properties should you select? To answer, select the appropriate option-, m the answer area. NOTE:
Each correct selection is worth one point.
Answer:
Explanation:
NEW QUESTION # 372
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.
Answer:
Explanation:
Explanation:
NEW QUESTION # 373
You have a dataset created for multiclass classification tasks that contains a normalized numerical feature set with 10,000 data points and 150 features.
You use 75 percent of the data points for training and 25 percent for testing. You are using the scikit-learn machine learning library in Python. You use X to denote the feature set and Y to denote class labels.
You create the following Python data frames:
You need to apply the Principal Component Analysis (PCA) method to reduce the dimensionality of the feature set to 10 features in both training and testing sets.
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.
Answer:
Explanation:
Reference:
https://scikit-learn.org/stable/modules/generated/sklearn.decomposition.PCA.html
NEW QUESTION # 374
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