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The DP-100 Exam is a hands-on practical exam that requires the candidate to demonstrate their ability to design and implement data science solutions on Azure. DP-100 exam includes various tasks, such as creating machine learning models, building data pipelines, and deploying models to production environments. The tasks are designed to simulate real-world scenarios that data scientists and engineers face in their jobs.
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Microsoft DP-100: Designing and Implementing a Data Science Solution on Azure is a certification exam that is designed to evaluate a candidate's knowledge and skills in designing and implementing data science solutions on Microsoft Azure. DP-100 Exam is intended for data professionals, data scientists, and developers who want to demonstrate their expertise in implementing big data solutions on the Azure platform.
NEW QUESTION # 519
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?
Answer: A
Explanation:
train_step is missing.
Reference:
https://docs.microsoft.com/en-us/python/api/azureml-pipeline-core/azureml.pipeline.core.pipelinedata?
view=azure-ml-py
NEW QUESTION # 520
You need to implement early stopping criteria as suited in the model training requirements.
Which three code segments should you use to develop the solution? To answer, move the appropriate code segments from the list of code segments to the answer area and arrange them in the correct order.
NOTE: More than one order of answer choices is correct. You will receive credit for any of the correct orders you select.
Answer:
Explanation:
1 - from azureml . train.hyperdrive
2 - import TruncationSelectionPolicy
3 - early_termination_policy =....
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-tune-hyperparameters
NEW QUESTION # 521
You plan to implement a two-step pipeline by using the Azure Machine Learning SDK for Python.
The pipeline will pass temporary data from the first step to the second step.
You need to identify the class and the corresponding method that should be used in the second step to access temporary data generated by the first step in the pipeline.
Which class and method should you identify? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point
Answer:
Explanation:
Explanation:
NEW QUESTION # 522
You manage an Azure Machine Learning workspace and a GitHub repository. The GitHub repository contains a CSV file located at httpsy/raw.githubusercontent.com/account1/repo1/main/doc1/data1.csv. The CSV file includes embedded newlines.
You plan to consume the content of the CSV file in the workspace. The solution must minimize the possibility of misaligned field values when reading the file content.
You need to create a data asset that references the CSV file.
Which data asset configuration values should you use? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
NEW QUESTION # 523
You are creating a machine learning model in Python. The provided dataset contains several numerical columns and one text column. The text column represents a product's category. The product category will always be one of the following:
* Bikes
* Cars
* Vans
* Boats
You are building a regression model using the scikit-learn Python package.
You need to transform the text data to be compatible with the scikit-learn Python package.
How should you complete the code segment? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
Box 1: pandas as df
Pandas takes data (like a CSV or TSV file, or a SQL database) and creates a Python object with rows and columns called data frame that looks very similar to table in a statistical software (think Excel or SPSS for example.
Box 2: transpose[ProductCategoryMapping]
Reshape the data from the pandas Series to columns.
Reference:
https://datascienceplus.com/linear-regression-in-python/
NEW QUESTION # 524
......
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