참고: PassTIP에서 Google Drive로 공유하는 무료 2026 Microsoft DP-100 시험 문제집이 있습니다: https://drive.google.com/open?id=1YzmljeAtkmbCFTa62xSCkXN3myMG1cld
Microsoft인증DP-100시험은 현재 치열한 IT경쟁 속에서 열기는 더욱더 뜨겁습니다. 응시자들도 더욱더 많습니다. 하지만 난이도난 전혀 낮아지지 않고 이지도 어려운 시험입니다. 어쨌든 개인적인 지식 장악도 나 정보기술 등을 테스트하는 시험입니다. 보통은Microsoft인증DP-100시험을 넘기 위해서는 많은 시간과 신경이 필요합니다.
| Section | Objectives |
|---|---|
| Explore and analyze data | - Ingest and prepare data for modeling - Perform exploratory data analysis |
| Design and prepare a machine learning solution | - Plan and configure Azure Machine Learning workspace - Select appropriate Azure services for machine learning workloads - Manage compute and data assets |
| Train machine learning models | - Train models using Azure Machine Learning - Tune hyperparameters and evaluate models |
| Optimize and manage models | - Improve model performance - Track experiments and manage model lifecycle |
| Deploy and consume models | - Deploy models to endpoints - Monitor deployed models and endpoints |
Microsoft인증 DP-100시험을 준비하기 위해 잠도 설쳐가면서 많이 힘들죠? PassTIP덤프가 고객님의 곁을 지켜드립니다. PassTIP에서 제공해드리는Microsoft인증 DP-100덤프는 실제Microsoft인증 DP-100시험문제를 연구하여 만든 공부자료이기에 최고의 품질을 자랑합니다. PassTIP덤프를 열심히 공부하여 멋진 IT전문가의 꿈을 이루세요.
질문 # 160
You create a project in the Azure Al Foundry portal.
You must provide a meaningful comparison of benchmark metrics between the text-embedding-ada-002 and text-embedding-3-large models.
You need to select the metncs to use for the X and Y axes in the Metncs to compare pane.
Which metncs should you select? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
정답：
설명：
Explanation:
질문 # 161
You have an Azure Machine Learning workspace named Workspace 1 Workspace! has a registered Mlflow model named model 1 with PyFunc flavor You plan to deploy model1 to an online endpoint named endpointl without egress connectivity by using Azure Machine learning Python SDK vl You have the following code:
You need to add a parameter to the ManagedOnllneDeployment object to ensure the model deploys successfully Solution: Add the with_package parameter.
Does the solution meet the goal?
정답：A
질문 # 162
You manage an Azure Machine Learning workspace.
You train a model interactively with a Jupyter Notebook in the workspace During training, a dataset is created with accuiacy and loss metrics for each epoch.
You need to configure model tracking with MLflow to log the dataset created during the training.
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：
Explanation:
질문 # 163
You create an Azure Machine Learning dataset containing automobile price data The dataset includes 10,000 rows and 10 columns You use Azure Machine Learning Designer to transform the dataset by using an Execute Python Script component and custom code.
The code must combine three columns to create a new column.
You need to configure the code function.
Which configurations should you use? lo answer, select the appropriate options in the answer area NOTE: Each correct selection is worth one point.
정답：
설명：
질문 # 164
You publish a batch inferencing pipeline that will be used by a business application.
The application developers need to know which information should be submitted to and returned by the REST interface for the published pipeline.
You need to identify the information required in the REST request and returned as a response from the published pipeline.
Which values should you use in the REST request and to expect in the response? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
정답：
설명：
Explanation
Box 1: JSON containing an OAuth bearer token
Specify your authentication header in the request.
To run the pipeline from the REST endpoint, you need an OAuth2 Bearer-type authentication header.
Box 2: JSON containing the experiment name
Add a JSON payload object that has the experiment name.
Example:
rest_endpoint = published_pipeline.endpoint
response = requests.post(rest_endpoint,
headers=auth_header,
json={"ExperimentName": "batch_scoring",
"ParameterAssignments": {"process_count_per_node": 6}})
run_id = response.json()["Id"]
Box 3: JSON containing the run ID
Make the request to trigger the run. Include code to access the Id key from the response dictionary to get the value of the run ID.
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/tutorial-pipeline-batch-scoring-classification
질문 # 165
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PassTIP는 여러분의 꿈을 이루어줄 뿐만 아니라 일년무료 업뎃서비스도 따릅니다. PassTIP에서 제공하는 덤프로 여러분은 1000%시험을 패스하실수 있고Microsoft DP-100자격증을 취득하실 수 있습니다.지금 바로 사이트에서Microsoft DP-100덤프데모 즉 덤프의 일부 문제와 답을 다운 받으셔서 체험하실 수 있습니다.
DP-100시험유형: https://www.passtip.net/DP-100-pass-exam.html
참고: PassTIP에서 Google Drive로 공유하는 무료 2026 Microsoft DP-100 시험 문제집이 있습니다: https://drive.google.com/open?id=1YzmljeAtkmbCFTa62xSCkXN3myMG1cld