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The DP-100 Exam covers a wide range of topics, including data exploration and preparation, modeling, deployment, and monitoring. You will need to demonstrate your proficiency in using Azure services like Azure Machine Learning, Azure Databricks, Azure Stream Analytics, and Azure Cognitive Services to build end-to-end ML solutions.

The DP-100 exam covers a range of topics, including creating and managing data sets, building and training predictive models, deploying models to production environments, and monitoring and optimizing the performance of models. Candidates will be required to demonstrate their ability to work with Azure Machine Learning, Azure Databricks, and other Azure data services, and to use tools such as Python and R to design and implement data solutions.

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Undoubtedly, passing the Microsoft DP-100 certification exam is one big achievement. Regardless of how tough the DP-100 exam is, it serves an important purpose of improving your skills and knowledge of a specific field. Once you become certified by Microsoft DP-100, a whole new career scope will open up to you.

Microsoft DP-100 Certification Exam covers a wide range of topics related to data science, including data exploration and visualization, data preparation, modeling, and deployment. It also covers various Azure tools and services for data science, such as Azure Machine Learning, Azure Databricks, Azure Stream Analytics, and more. DP-100 Exam is designed to test your ability to design and implement end-to-end data science solutions on the Azure platform.

Microsoft Designing and Implementing a Data Science Solution on Azure Sample Questions (Q290-Q295):

NEW QUESTION # 290
You create a multi-class image classification deep learning experiment by using the PyTorch framework. You plan to run the experiment on an Azure Compute cluster that has nodes with GPU's.
You need to define an Azure Machine Learning service pipeline to perform the monthly retraining of the image classification model. The pipeline must run with minimal cost and minimize the time required to train the model.
Which three pipeline steps should you run 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:

Explanation:
Step 1: Configure a DataTransferStep() to fetch new image data...
Step 2: Configure a PythonScriptStep() to run image_resize.y on the cpu-compute compute target.
Step 3: Configure the EstimatorStep() to run training script on the gpu_compute computer target.
The PyTorch estimator provides a simple way of launching a PyTorch training job on a compute target.
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/how-to-train-pytorch

NEW QUESTION # 291
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 endpoint1 without egress connectivity by using Azure Machine learning Python SDK vl You have the following code:

You need to add a parameter to the ManagedOnlineDeployment object to ensure the model deploys successfully Solution: Add the environment parameter.
Does the solution meet the goal?

A. Yes
B. No

Answer: B

NEW QUESTION # 292
Your Azure Machine Learning workspace has a dataset named . 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.

Answer:

Explanation:

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

NEW QUESTION # 293
You use an Azure Machine Learning workspace.
You must monitor cost at the endpoint and deployment level.
You have a trained model that must be deployed as an online endpoint. Users must authenticate by using Microsoft Entra ID.
What should you do?

A. Deploy the model lo Azure Kubernetes Service (AKS). During deployment, set the token_auth_mode parameter of the target configuration object to true.
B. Deploy the model to a managed online endpoint. During deployment, set the token_auth_mode parameter of the target configuration object to true.
C. Deploy the model to Azure Kubernetes Service (AKS). During deployment, set the auth.mode parameter to configure the authentication type.
D. Deploy the model to a managed online endpoint. During deployment, set the auth_mode parameter to configure the authentication type.

Answer: D

NEW QUESTION # 294
You define a datastore named ml-data for an Azure Storage blob container. In the container, you have a folder named train that contains a file named data.csv. You plan to use the file to train a model by using the Azure Machine Learning SDK.
You plan to train the model by using the Azure Machine Learning SDK to run an experiment on local compute.
You define a DataReference object by running the following code:

You need to load the training data.
Which code segment should you use?

Answer: D

Explanation:
Example:
data_folder = args.data_folder
# Load Train and Test data
train_data = pd.read_csv(os.path.join(data_folder, 'data.csv'))
Reference:
https://www.element61.be/en/resource/azure-machine-learning-services-complete-toolbox-ai

NEW QUESTION # 295
......

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