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DP-100인증덤프문제 - DP-100인기자격증덤프공부문제

Itcertkr DP-100 최신 PDF 버전 시험 문제집을 무료로 Google Drive에서 다운로드하세요: https://drive.google.com/open?id=1t1GrKhJSNZUNBUMM_j2w2k5VbBvXIu4l

다년간 IT업계에 종사하신 전문가들이 자신의 노하우와 경험으로 제작한 Microsoft DP-100덤프는 DP-100 실제 기출문제를 기반으로 한 자료로서 DP-100시험문제의 모든 범위와 유형을 포함하고 있어 높을 적중율을 자랑하고 있습니다.덤프구매후 불합격 받으시면 구매일로부터 60일내 주문은 덤프비용을 환불해드립니다.IT 자격증 취득은 Itcertkr덤프가 정답입니다.

Microsoft DP-100 인증을 받으면 고용주와 클라이언트에게 Azure에서 데이터 과학 솔루션을 설계하고 구현하는 데 필요한 기술과 지식을 보유하고 있다는 것을 입증할 수 있습니다. 이는 또한 교육을 지속적으로 받고 최신 기술과 최상의 실천 방법을 따르는 것에 헌신하고 있다는 것을 보여줍니다.

Microsoft DP-100 인증 시험은 데이터 과학 및 기계 학습과 관련된 광범위한 주제를 다룹니다. 응시자는 Azure Machine Learning, Azure Stream Analytics 및 Azure Data Factory를 포함한 Azure Data Services를 깊이 이해해야합니다. 이 시험은 또한 통계 분석, 데이터 시각화 및 데이터 탐색 기술에 대한 후보자의 지식을 테스트합니다.

>> DP-100인증덤프문제 <<

DP-100인기자격증 덤프공부문제 - DP-100완벽한 공부문제

Itcertkr는Microsoft DP-100시험을 패스할 수 있는 아주 좋은 사이트입니다. Itcertkr은 아주 알맞게 최고의Microsoft DP-100시험문제와 답 내용을 만들어 냅니다. 덤프는 기존의 시험문제와 답과 시험문제분석 등입니다. Itcertkr에서 제공하는Microsoft DP-100시험자료의 문제와 답은 실제시험의 문제와 답과 아주 비슷합니다.

DP-100 자격증은 마이크로소프트 아즈루를 사용하여 데이터 솔루션을 설계하고 구현하는 데이터 전문가들이 스킬을 개발하고자 할 때 필수적인 자격증입니다. 이 자격증은 데이터 탐색, 데이터 준비, 데이터 모델링, 기계 학습 및 데이터 시각화와 같은 데이터 과학의 다양한 측면에서 후보자의 능력을 증명합니다. 이 자격증은 전 세계에서 인정되며 후보자가 더 나은 취업 기회와 경력 성장을 이루는 데 도움이 될 수 있습니다.

최신 Microsoft Azure DP-100 무료샘플문제 (Q42-Q47):

질문 # 42
You are a lead data scientist for a project that tracks the health and migration of birds. You create a multi-image classification deep learning model that uses a set of labeled bird photos collected by experts. You plan to use the model to develop a cross-platform mobile app that predicts the species of bird captured by app users.
You must test and deploy the trained model as a web service. The deployed model must meet the following requirements:
An authenticated connection must not be required for testing.
The deployed model must perform with low latency during inferencing.
The REST endpoints must be scalable and should have a capacity to handle large number of requests when multiple end users are using the mobile application.
You need to verify that the web service returns predictions in the expected JSON format when a valid REST request is submitted.
Which compute resources 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://docs.microsoft.com/en-us/azure/machine-learning/data-science-virtual-machine/dsvm-common-identity
https://docs.microsoft.com/en-us/azure/architecture/reference-architectures/ai/training-deep-learning

질문 # 43
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. Yes
B. No

정답：A

설명：
The two steps are present: process_step and train_step
Data_input correctly references the data in the data store.
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.
PipelineData objects are also used when constructing Pipelines to describe step dependencies. To specify that a step requires the output of another step as input, use a PipelineData object in the constructor of both steps.
For 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

질문 # 44
You plan to use Hyperdrive to optimize the hyperparameters selected when training a model. You create the following code to define options for the hyperparameter experiment

For each of the following statements, select Yes if the statement is true. Otherwise, select No. NOTE: Each correct selection is worth one point.

정답：

설명：

Reference:
https://docs.microsoft.com/en-us/python/api/azureml-train-core/azureml.train.hyperdrive.hyperdriveconfig
https://docs.microsoft.com/en-us/azure/machine-learning/how-to-tune-hyperparameters

질문 # 45
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：

질문 # 46
Your Azure Machine Learning workspace has a dataset named real_estate_data. A sample of the data in the dataset follows.

You want to use automated machine learning to find the best regression model for predicting the price column.
You need to configure an automated machine learning experiment using the Azure Machine Learning SDK.
How should you complete the code? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

정답：

설명：

Explanation:

Box 1: training_data
The training data to be used within the experiment. It should contain both training features and a label column (optionally a sample weights column). If training_data is specified, then the label_column_name parameter must also be specified.
Box 2: validation_data
Provide validation data: In this case, you can either start with a single data file and split it into training and validation sets or you can provide a separate data file for the validation set. Either way, the validation_data parameter in your AutoMLConfig object assigns which data to use as your validation set.
Example, the following code example explicitly defines which portion of the provided data in dataset to use for training and validation.
dataset = Dataset.Tabular.from_delimited_files(data)
training_data, validation_data = dataset.random_split(percentage=0.8, seed=1) automl_config = AutoMLConfig(compute_target = aml_remote_compute, task = 'classification', primary_metric = 'AUC_weighted', training_data = training_data, validation_data = validation_data, label_column_name = 'Class' ) Box 3: label_column_name label_column_name:
The name of the label column. If the input data is from a pandas.DataFrame which doesn't have column names, column indices can be used instead, expressed as integers.
This parameter is applicable to training_data and validation_data parameters.
Reference:
https://docs.microsoft.com/en-us/python/api/azureml-train-automl-client/azureml.train.automl.automlconfig.auto

질문 # 47
......

DP-100인기자격증 덤프공부문제: https://www.itcertkr.com/DP-100_exam.html

Itcertkr DP-100 최신 PDF 버전 시험 문제집을 무료로 Google Drive에서 다운로드하세요: https://drive.google.com/open?id=1t1GrKhJSNZUNBUMM_j2w2k5VbBvXIu4l

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