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New Guide DP-100 Files | High Pass-Rate Certification DP-100 Exam Infor: Designing and Implementing a Data Science Solution on Azure

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We can say that the Microsoft DP-100 exam practice questions are real, valid, and updated Designing and Implementing a Data Science Solution on Azure (DP-100) exam questions that will provide you with everything that you need to learn to prepare and pass the DP-100 exam. The Microsoft DP-100 Exam Questions will not only assist you in Designing and Implementing a Data Science Solution on Azure (DP-100) exam preparation but also give you sight knowledge about the Designing and Implementing a Data Science Solution on Azure (DP-100) exam topics that will help you in your professional career.

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.

Microsoft DP-100 exam is a certification exam that validates the skills of individuals in designing and implementing data science solutions on Azure. It is a particularly valuable certification for professionals in the field of data science and machine learning who are interested in utilizing the Azure platform to develop scalable and efficient data solutions. Designing and Implementing a Data Science Solution on Azure certification exam is designed to test the candidate's ability to design and implement data science solutions using Azure technologies, including Azure Machine Learning, Azure Databricks, and Azure Cognitive Services.

Exam Details & Skills Measured

Microsoft DP-100 is an associate-level certification exam. It can be taken as a proctored test if this option is available in your country. If it’s not, you can sit for it as an online exam at one of the Pearson VUE testing centers across the world. You should check the official webpage for more information about scheduling this exam.

When you are ready for this test, you have to pay $165 as a registration fee, which applies to a single delivery of the exam. You can take it in English, Korean, Japanese, or Simplified Chinese. Microsoft DP-100 contains 40-60 questions that cover different types, such as drag and drop, multiple choice, active screen, build list, short answer, best answer, and case studies, among others. If you want to pass this test on the first try, you should get 720 points on a scale of 100-1000.

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Microsoft Designing and Implementing a Data Science Solution on Azure Sample Questions (Q402-Q407):

NEW QUESTION # 402
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?

A. Yes
B. No

Answer: A

Explanation:
Explanation
The two steps are present: process_step and train_step
Note:
Data used in pipeline can be produced by one step and consumed in another step by providing a PipelineData object as an output of one step and an input of one or more subsequent steps.
PipelineData objects are also used when constructing Pipelines to describe step dependencies. To specify that a step requires the output of another step as input, use a PipelineData object in the constructor of both steps.
For example, the pipeline train step depends on the process_step_output output of the pipeline process step:
from azureml.pipeline.core import Pipeline, PipelineData
from azureml.pipeline.steps import PythonScriptStep
datastore = ws.get_default_datastore()
process_step_output = PipelineData("processed_data", datastore=datastore) process_step = PythonScriptStep(script_name="process.py", arguments=["--data_for_train", process_step_output], outputs=[process_step_output], compute_target=aml_compute, source_directory=process_directory) train_step = PythonScriptStep(script_name="train.py", arguments=["--data_for_train", process_step_output], inputs=[process_step_output], compute_target=aml_compute, source_directory=train_directory) pipeline = Pipeline(workspace=ws, steps=[process_step, train_step]) Reference:
https://docs.microsoft.com/en-us/python/api/azureml-pipeline-core/azureml.pipeline.core.pipelinedata?view=azu

NEW QUESTION # 403
You plan to run a script as an experiment using a Script Run Configuration. The script uses modules from the scipy library as well as several Python packages that are not typically installed in a default conda environment.
You plan to run the experiment on your local workstation for small datasets and scale out the experiment by running it on more powerful remote compute clusters for larger datasets.
You need to ensure that the experiment runs successfully on local and remote compute with the least administrative effort.
What should you do?

A. Create and register an Environment that includes the required packages. Use this Environment for all experiment runs.
B. Create a virtual machine (VM) with the required Python configuration and attach the VM as a compute target. Use this compute target for all experiment runs.
C. Always run the experiment with an Estimator by using the default packages.
D. Do not specify an environment in the run configuration for the experiment. Run the experiment by using the default environment.
E. Create a config. yaml file defining the conda packages that are required and save the file in the experiment folder.

Answer: A

Explanation:
If you have an existing Conda environment on your local computer, then you can use the service to create an environment object. By using this strategy, you can reuse your local interactive environment on remote runs.
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/how-to-use-environments

NEW QUESTION # 404
You have the following Azure subscriptions and Azure Machine Learning service workspaces:

You need to obtain a reference to the ml-project workspace.
Solution: Run the following Python code:

Does the solution meet the goal?

A. Yes
B. No

Answer: B

NEW QUESTION # 405
You are tuning a hyperparameter for an algorithm. The following table shows a data set with different hyperparameter, training error, and validation errors.

Use the drop-down menus to select the answer choice that answers each question based on the information presented in the graphic.

Answer:

Explanation:

Explanation

Box 1: 4
Choose the one which has lower training and validation error and also the closest match.
Minimize variance (difference between validation error and train error).
Box 2: 5
Minimize variance (difference between validation error and train error).
Reference:
https://medium.com/comet-ml/organizing-machine-learning-projects-project-management-guidelines-2d2b85651

NEW QUESTION # 406
You create a training pipeline by using the Azure Machine Learning designer. You need to load data into a machine learning pipeline by using the Import Data component. Which two data sources could you use? Each correct answer presents a complete solution.
NOTE: Each correct selection is worth one point

A. Registered dataset
B. URL via HTTP
C. Azure SQL Database
D. Azure Blob storage container through a registered datastore
E. Azure Data Lake Storage Gen2

Answer: B,C

NEW QUESTION # 407
......

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