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Microsoft DP-100 Certification Exam is designed to test the skills and knowledge required to design and implement a data science solution on Azure. DP-100 exam is aimed at professionals who are involved in building and implementing data science solutions, including data scientists, data engineers, and machine learning engineers. Passing the exam demonstrates that the candidate has the skills and knowledge required to design and implement a data science solution on Azure.

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The Microsoft DP-100 exam is designed to test the candidate's understanding of key concepts such as data exploration and preparation, modeling, deployment, and maintenance of machine learning models. It requires a deep understanding of Azure data services, including Azure Machine Learning, Azure Databricks, and Azure HDInsight. Furthermore, candidates are expected to have a good understanding of programming languages such as Python and R.

Obtaining the DP-100 certification demonstrates the candidate's proficiency in designing and implementing data science solutions on Azure. Designing and Implementing a Data Science Solution on Azure certification provides a competitive edge in the job market and opens up various career opportunities for the candidate. The DP-100 Certification is also a prerequisite for other advanced Azure-based data science certifications, such as the DP-200 (Implementing an Azure Data Solution) and DP-201 (Designing an Azure Data Solution) certifications.

Microsoft Designing and Implementing a Data Science Solution on Azure Sample Questions (Q517-Q522):

NEW QUESTION # 517
You create a training pipeline using the Azure Machine Learning designer. You upload a CSV file that contains the data from which you want to train your model.
You need to use the designer to create a pipeline that includes steps to perform the following tasks:
Select the training features using the pandas filter method.
Train a model based on the naive_bayes.GaussianNB algorithm.
Return only the Scored Labels column by using the query SELECT [Scored Labels] FROM t1; Which modules should you use? To answer, drag the appropriate modules to the appropriate locations. Each module name 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:

NEW QUESTION # 518
You are using The Azure Machine Learning designer to transform a dataset containing the census data of all nations.
You must use the Split Data component to separate the dataset into two datasets. The first dataset must contain the census data of the United States. The second dataset must include the census data of the remaining nations.
You need to configure the component to create the datasets.
Which 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 # 519
You are developing a linear regression model in Azure Machine Learning Studio. You run an experiment to compare different algorithms.
The following image displays the results dataset output:

Use the drop-down menus to select the answer choice that answers each question based on the information presented in the image.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:

Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/evaluate-model
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/linear-regression

NEW QUESTION # 520
You manage an Azure Machine Learning workspace named workspace 1 with a compute instance named computet.
You must remove a kernel named kernel 1 from computet1. You connect to compute 1 by using noa terminal window from workspace 1.
You need to enter a command in the terminal window to remove kernel 1.
Which command should you use? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection it worth one point.

Answer:

Explanation:

NEW QUESTION # 521
You have an Azure blob container that contains a set of TSV files. The Azure blob container is registered as a datastore for an Azure Machine Learning service workspace. Each TSV file uses the same data schema.
You plan to aggregate data for all of the TSV files together and then register the aggregated data as a dataset in an Azure Machine Learning workspace by using the Azure Machine Learning SDK for Python.
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

Box 1: No
FileDataset references single or multiple files in datastores or from public URLs. The TSV files need to be parsed.
Box 2: Yes
to_path() gets a list of file paths for each file stream defined by the dataset.
Box 3: Yes
TabularDataset.to_pandas_dataframe loads all records from the dataset into a pandas DataFrame.
TabularDataset represents data in a tabular format created by parsing the provided file or list of files.
Note: TSV is a file extension for a tab-delimited file used with spreadsheet software. TSV stands for Tab Separated Values. TSV files are used for raw data and can be imported into and exported from spreadsheet software. TSV files are essentially text files, and the raw data can be viewed by text editors, though they are often used when moving raw data between spreadsheets.
Reference:
https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.data.tabulardataset

NEW QUESTION # 522
......

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