VALID MICROSOFT DP-100 EXAM GUIDE & VALID DP-100 TEST SYLLABUS

Valid Microsoft DP-100 Exam Guide & Valid DP-100 Test Syllabus

Valid Microsoft DP-100 Exam Guide & Valid DP-100 Test Syllabus

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Tags: Valid DP-100 Exam Guide, Valid DP-100 Test Syllabus, Latest DP-100 Cram Materials, Pass DP-100 Guaranteed, DP-100 Minimum Pass Score

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The DP-100 exam is an excellent opportunity for data scientists and engineers to validate their skills and gain a competitive advantage in the job market. Designing and Implementing a Data Science Solution on Azure certification provides an added advantage for organizations to identify skilled data scientists and engineers who can design and implement data science solutions on Azure.

Earning the DP-100 certification can help you advance your career in data science and machine learning. It demonstrates to employers that you have the knowledge and skills to design and implement data-driven solutions using Azure services. Additionally, Microsoft offers a variety of other Azure certifications that can complement the DP-100 and help you build a well-rounded skillset in cloud computing.

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.

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Customizable Designing and Implementing a Data Science Solution on Azure (DP-100) practice exams allow you to adjust the time and Microsoft DP-100 questions numbers according to your practice needs. Scenarios of our DP-100 Practice Tests are similar to the actual DP-100 exam. You feel like sitting in the real DP-100 exam while taking these DP-100 practice exams.

Microsoft Designing and Implementing a Data Science Solution on Azure Sample Questions (Q328-Q333):

NEW QUESTION # 328
You are hired as a data scientist at a winery. The previous data scientist used Azure Machine Learning.
You need to review the models and explain how each model makes decisions.
Which explainer modules should you use? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:

Explanation

Meta explainers automatically select a suitable direct explainer and generate the best explanation info based on the given model and data sets. The meta explainers leverage all the libraries (SHAP, LIME, Mimic, etc.) that we have integrated or developed. The following are the meta explainers available in the SDK:
Tabular Explainer: Used with tabular datasets.
Text Explainer: Used with text datasets.
Image Explainer: Used with image datasets.
Box 1: Tabular
Box 2: Text
Box 3: Image
Reference:
https://medium.com/microsoftazure/automated-and-interpretable-machine-learning-d07975741298


NEW QUESTION # 329
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 using Azure Machine Learning to run an experiment that trains a classification model.
You want to use Hyperdrive to find parameters that optimize the AUC metric for the model. You configure a HyperDriveConfig for the experiment by running the following code:

You plan to use this configuration to run a script that trains a random forest model and then tests it with validation data. The label values for the validation data are stored in a variable named y_test variable, and the predicted probabilities from the model are stored in a variable named y_predicted.
You need to add logging to the script to allow Hyperdrive to optimize hyperparameters for the AUC metric.
Solution: Run the following code:

Does the solution meet the goal?

  • A. No
  • B. Yes

Answer: A

Explanation:
Use a solution with logging.info(message) instead.
Note: Python printing/logging example:
logging.info(message)
Destination: Driver logs, Azure Machine Learning designer
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/how-to-debug-pipelines


NEW QUESTION # 330
You are preparing to build a deep learning convolutional neural network model for image classification. You create a script to train the model using CUDA devices.
You must submit an experiment that runs this script in the Azure Machine Learning workspace.
The following compute resources are available:
* a Microsoft Surface device on which Microsoft Office has been installed. Corporate IT policies prevent the installation of additional software
* a Compute Instance named ds-workstation in the workspace with 2 CPUs and 8 GB of memory
* an Azure Machine Learning compute target named cpu-cluster with eight CPU-based nodes
* an Azure Machine Learning compute target named gpu-cluster with four CPU and GPU-based nodes You need to specify the compute resources to be used for running the code to submit the experiment, and for running the script in order to minimize model training time.
Which resources should the data scientist use? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:


NEW QUESTION # 331
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 # 332
You manage an Azure Machine Learning workspace. You configure an automated machine learning regression training job by using the Azure Machine Learning Python SDK v2. You configure the regression job by using the following script:

For each of the following statements, select Yes if the statement is true. Otherwise, select No.

Answer:

Explanation:


NEW QUESTION # 333
......

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