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DP-100試題，最新DP-100題庫

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最新DP-100題庫 - DP-100最新考題

DP-100 認證是互聯網界具有極大聲望的網路技能認證，在全球，通過IBM認證考試的工程師，平均年薪在10萬元以上。通過 HP 認證考試的工程師，平均年薪在30萬元以上。獲得 Microsoft 的 DP-100 認證的工程師，平均年薪也不低於20萬人民幣。據說，這還只是基本工資，不包括獎金，紅利和其他非工資性補貼。難怪美國副總統戈爾曾把 Microsoft DP-100 認證恰當而幽默地稱為“獲得高技術，高薪水的頭等艙船票”。

最新的 Microsoft Azure DP-100 免費考試真題 (Q495-Q500):

問題 #495
You create a training pipeline by using the Azure Machine Learning designer.
You need to load data into a machine learning pipeline by using the Import Data component.
Which two data sources could you use? Each correct answer presents a complete solution.
NOTE: Each correct selection is worth one point.

A. Azure Data Lake Storage Gen2
B. Azure SQL Database
C. URL via HTTP
D. Registered dataset
E. Azure Blob storage container through a registered datastore

答案：C,E

解題說明：
https://learn.microsoft.com/en-us/azure/machine-learning/component-reference/import-data

問題 #496
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.

答案：

解題說明：

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

問題 #497
You need to implement a scaling strategy for the local penalty detection data.
Which normalization type should you use?

A. Streaming
B. Weight
C. Batch
D. Cosine

答案：C

解題說明：
Post batch normalization statistics (PBN) is the Microsoft Cognitive Toolkit (CNTK) version of how to evaluate the population mean and variance of Batch Normalization which could be used in inference Original Paper.
In CNTK, custom networks are defined using the BrainScriptNetworkBuilder and described in the CNTK network description language "BrainScript." Scenario:
Local penalty detection models must be written by using BrainScript.
Reference:
https://docs.microsoft.com/en-us/cognitive-toolkit/post-batch-normalization-statistics

問題 #498
You need to configure the Feature Based Feature Selection module based on the experiment requirements and datasets.
How should you configure the module properties? To answer, select the appropriate options in the dialog box in the answer area.
NOTE: Each correct selection is worth one point.

答案：

解題說明：

Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/filter-based-feature-selection

問題 #499
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 a model to forecast weather conditions based on historical data.
You need to create a pipeline that runs a processing script to load data from a datastore and pass the processed data to a machine learning model training script.
Solution: Run the following code:

Does the solution meet the goal?

A. No
B. Yes

答案：A

解題說明：
Note: Data used in pipeline can be produced by one step and consumed in another step by providing a PipelineData object as an output of one step and an input of one or more subsequent steps.
Compare with this example, the pipeline train step depends on the process_step_output output of the pipeline process step:
from azureml.pipeline.core import Pipeline, PipelineData
from azureml.pipeline.steps import PythonScriptStep
datastore = ws.get_default_datastore()
process_step_output = PipelineData("processed_data", datastore=datastore) process_step = PythonScriptStep(script_name="process.py", arguments=["--data_for_train", process_step_output], outputs=[process_step_output], compute_target=aml_compute, source_directory=process_directory) train_step = PythonScriptStep(script_name="train.py", arguments=["--data_for_train", process_step_output], inputs=[process_step_output], compute_target=aml_compute, source_directory=train_directory) pipeline = Pipeline(workspace=ws, steps=[process_step, train_step]) Reference:
https://docs.microsoft.com/en-us/python/api/azureml-pipeline-core/azureml.pipeline.core.pipelinedata?view=azure-ml-py

問題 #500
......

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