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The DP-100 Certification Exam is intended for data scientists, data architects, and data engineers who want to validate their expertise in designing and implementing data science solutions on Azure. It is also suitable for professionals who are responsible for implementing Azure-based machine learning and AI solutions, including data analysts, developers, and IT administrators.
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Microsoft DP-100 (Designing and Implementing a Data Science Solution on Azure) Certification Exam is designed for professionals who want to validate their expertise in designing and implementing data science solutions using Microsoft Azure technologies. Designing and Implementing a Data Science Solution on Azure certification exam is aimed at data scientists, data engineers, and other professionals who work with data and want to demonstrate their skills in this field. DP-100 Exam Tests the candidate's knowledge of Azure data services, machine learning, and other related technologies.
NEW QUESTION # 218
You are using Azure Machine Learning to monitor a trained and deployed model. You implement Event Grid to respond to Azure Machine Learning events.
Model performance has degraded due to model input data changes.
You need to trigger a remediation ML pipeline based on an Azure Machine Learning event.
Which event should you use?
Answer: D
Explanation:
https://learn.microsoft.com/en-
us/dotnet/api/azure.messaging.eventgrid.systemevents.machinelearningservicesdatasetdriftdetec tedeventdata?view=azure-dotnet
NEW QUESTION # 219
You collect data from a nearby weather station. You have a pandas dataframe named weather_df that includes the following data:
The data is collected every 12 hours: noon and midnight.
You plan to use automated machine learning to create a time-series model that predicts temperature over the next seven days. For the initial round of training, you want to train a maximum of 50 different models.
You must use the Azure Machine Learning SDK to run an automated machine learning experiment to train these models.
You need to configure the automated machine learning run.
How should you complete the AutoMLConfig definition? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
Box 1: forcasting
Task: The type of task to run. Values can be 'classification', 'regression', or 'forecasting' depending on the type of automated ML problem to solve.
Box 2: temperature
The training data to be used within the experiment. It should contain both training features and a label column (optionally a sample weights column).
Box 3: observation_time
time_column_name: The name of the time column. This parameter is required when forecasting to specify the datetime column in the input data used for building the time series and inferring its frequency. This setting is being deprecated. Please use forecasting_parameters instead.
Box 4: 7
"predicts temperature over the next seven days"
max_horizon: The desired maximum forecast horizon in units of time-series frequency. The default value is 1.
Units are based on the time interval of your training data, e.g., monthly, weekly that the forecaster should predict out. When task type is forecasting, this parameter is required.
Box 5: 50
"For the initial round of training, you want to train a maximum of 50 different models." Iterations: The total number of different algorithm and parameter combinations to test during an automated ML experiment.
Reference:
https://docs.microsoft.com/en-us/python/api/azureml-train-automl-client/azureml.train.automl.automlconfig.auto
NEW QUESTION # 220
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.
Answer:
Explanation:
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
NEW QUESTION # 221
You need to define a modeling strategy for ad response.
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:
1 - Implement a K-Means Clustering model.
2 - Use the cluster as a feature in a Decision Jungle model.
3 - Use the raw score as a feature....
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/multiclass-decision-jungle
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/score-matchbox-recommender
NEW QUESTION # 222
You create an Azure Machine Learning workspace. You use the Azure Machine Learning SDK for Python.
You must create a dataset from remote paths. The dataset must be reusable within the workspace.
You need to create the dataset.
How should you complete the following code segment? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
NEW QUESTION # 223
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