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Microsoft DP-100 (Designing and Implementing a Data Science Solution on Azure) Certification Exam is a popular certification exam designed to test the skills and knowledge of data professionals in designing and implementing data science solutions on the Microsoft Azure platform. DP-100 Exam is ideal for data scientists, data analysts, and other professionals who want to demonstrate their expertise in working with data on the Azure platform.

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The DP-100 Exam covers a range of topics, including data exploration and transformation, machine learning modeling, and deployment and management of machine learning models on Azure. DP-100 exam also focuses on the candidate's ability to work with Azure services such as Azure Machine Learning Studio, Azure Databricks, and Azure Cognitive Services. Successful candidates will have a deep understanding of the Azure platform and its capabilities, as well as the ability to design and implement data science solutions that meet business requirements.

Microsoft Designing and Implementing a Data Science Solution on Azure Sample Questions (Q126-Q131):

NEW QUESTION # 126
You run an experiment that uses an AutoMLConfig class to define an automated machine learning task with a maximum of ten model training iterations. The task will attempt to find the best performing model based on a metric named accuracy.
You submit the experiment with the following code:
You need to create Python code that returns the best model that is generated by the automated machine learning task. Which code segment should you use?

Answer: B

Explanation:
The get_output method returns the best run and the fitted model.
Reference:
https://notebooks.azure.com/azureml/projects/azureml-getting-started/html/how-to-use-azureml/automated- machine-learning/classification/auto-ml-classification.ipynb

NEW QUESTION # 127
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:

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.
automlconfig

NEW QUESTION # 128
You plan to explore demographic data for home ownership in various cities. The data is in a CSV file with the following format:
age,city,income,home_owner
21,Chicago,50000,0
35,Seattle,120000,1
23,Seattle,65000,0
45,Seattle,130000,1
18,Chicago,48000,0
You need to run an experiment in your Azure Machine Learning workspace to explore the data and log the results. The experiment must log the following information:
the number of observations in the dataset
a box plot of income by home_owner
a dictionary containing the city names and the average income for each city You need to use the appropriate logging methods of the experiment's run object to log the required information.
How should you complete the code? To answer, drag the appropriate code segments to the correct locations. Each code segment may be used once, more than once, or not at all. You may need to drag the split bar between panes or scroll to view content.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:

Explanation:
Box 1: log
The number of observations in the dataset.
run.log(name, value, description='')
Scalar values: Log a numerical or string value to the run with the given name. Logging a metric to a run causes that metric to be stored in the run record in the experiment. You can log the same metric multiple times within a run, the result being considered a vector of that metric.
Example: run.log("accuracy", 0.95)
Box 2: log_image
A box plot of income by home_owner.
log_image Log an image to the run record. Use log_image to log a .PNG image file or a matplotlib plot to the run. These images will be visible and comparable in the run record.
Example: run.log_image("ROC", plot=plt)
Box 3: log_table
A dictionary containing the city names and the average income for each city.
log_table: Log a dictionary object to the run with the given name.

NEW QUESTION # 129
Hotspot Question
You register the following versions of a model.

You use the Azure ML Python SDK to run a training experiment. You use a variable named run to reference the experiment run.
After the run has been submitted and completed, you run the following code:

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

Answer:

Explanation:

Explanation:
https://docs.microsoft.com/en-us/azure/machine-learning/how-to-deploy-and-where

NEW QUESTION # 130
You use an Azure Machine Learning workspace.
You have a trained model that must be deployed as a web service. Users must authenticate by using Azure Active Directory.
What should you do?

A. Deploy the model to Azure Kubernetes Service (AKS). During deployment, set the token_auth_enabledparameter of the target configuration object to true
B. Deploy the model to Azure Container Instances. During deployment, set the auth_enabled parameter of the target configuration object to true
C. Deploy the model to Azure Kubernetes Service (AKS). During deployment, set the auth.enabledparameter of the target configuration object to true
D. Deploy the model to Azure Container Instances. During deployment, set the token_auth_enabledparameter of the target configuration object to true

Answer: A

Explanation:
To control token authentication, use the token_auth_enabled parameter when you create or update a deployment Token authentication is disabled by default when you deploy to Azure Kubernetes Service.
Note: The model deployments created by Azure Machine Learning can be configured to use one of two authentication methods:
key-based: A static key is used to authenticate to the web service.
token-based: A temporary token must be obtained from the Azure Machine Learning workspace (using Azure Active Directory) and used to authenticate to the web service.
Incorrect Answers:
C: Token authentication isn't supported when you deploy to Azure Container Instances.
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/how-to-authenticate-web-service

NEW QUESTION # 131
......

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