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To prepare for the Microsoft DP-100 Certification Exam, you should have a solid understanding of data science concepts and techniques, as well as experience working with Azure tools and services. Microsoft offers various training courses and resources to help you prepare for the exam, including online courses, practice tests, and study guides. You can also find numerous third-party resources, such as books and tutorials, to help you prepare for the exam.

Microsoft DP-100 Certification Exam covers a wide range of topics related to data science and machine learning. It requires candidates to have a deep understanding of Azure data services, including Azure Machine Learning, Azure Stream Analytics, and Azure Data Factory. DP-100 exam also tests candidates' knowledge of statistical analysis, data visualization, and data exploration techniques.

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Microsoft Designing and Implementing a Data Science Solution on Azure Sample Questions (Q438-Q443):

NEW QUESTION # 438
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 an Azure Machine Learning service datastore in a workspace. The datastore contains the following files:
* /data/2018/Q1 .csv
* /data/2018/Q2.csv
* /data/2018/Q3.csv
* /data/2018/Q4.csv
* /data/2019/Q1.csv
All files store data in the following format:
id,f1,f2,l
1,1,2,0
2,1,1,1
3.2.1.0
You run the following code:

You need to create a dataset named training_data and load the data from all files into a single data frame by using the following code:

Solution: Run the following code:

Does the solution meet the goal?

Answer: A

Explanation:
Use two file paths.
Use Dataset.Tabular_from_delimeted as the data isn't cleansed.
Note:
A TabularDataset represents data in a tabular format by parsing the provided file or list of files. This provides you with the ability to materialize the data into a pandas or Spark DataFrame so you can work with familiar data preparation and training libraries without having to leave your notebook. You can create a TabularDataset object from .csv, .tsv, .parquet, .jsonl files, and from SQL query results.
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/how-to-create-register-datasets


NEW QUESTION # 439
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 are creating a new experiment in Azure Machine Learning Studio. One class has a much smaller number of observations than the other classes in the training set. You need to select an appropriate data sampling strategy to compensate for the class imbalance.
Solution: You use the Scale and Reduce sampling mode.
Does the solution meet the goal?

Answer: A

Explanation:
Instead use the Synthetic Minority Oversampling Technique (SMOTE) sampling mode.
Note: SMOTE is used to increase the number of underepresented cases in a dataset used for machine learning. SMOTE is a better way of increasing the number of rare cases than simply duplicating existing cases.
References:
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/smote


NEW QUESTION # 440
You create an Azure Machine Learning workspace named workspaces. You create a Python SDK v2 notebook to perform custom model training in workspaces.
You need to run the notebook from Azure Machine Learning Studio in workspaces.
What should you provision first?

Answer: D

Explanation:
An Azure Machine Learning compute instance is a cloud-based development environment that provides a Jupyter notebook server pre-configured with the Azure Machine Learning SDK and other necessary dependencies. It allows you to interactively create and run Jupyter notebooks in your workspace.


NEW QUESTION # 441
You have an Azure Machine Learning workspace.
You plan to run a job to tram a model as an MLflow model output.
You need to specify the output mode of the MLflow model.
Which three modes can you specify? Each correct answer presents a complete solution.
NOTE: Each correct selection is worth one point.

Answer: A,B,E


NEW QUESTION # 442
You create an Azure Machine learning workspace. The workspace contains a folder named src. The folder contains a Python script named script 1 .py.
You use the Azure Machine Learning Python SDK v2 to create a control script. You must use the control script to run script l.py as part of a training job.
You need to complete the section of script that defines the job parameters.
How should you complete the script? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:

Explanation:


NEW QUESTION # 443
......

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