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Microsoft DP-100 exam is an important certification for data scientists working with Azure. It covers a range of topics related to data science and requires candidates to demonstrate their ability to design and implement end-to-end machine learning solutions on Azure. With the right preparation and training, candidates can pass the DP-100 Exam and earn a valuable certification that can help them advance their careers.

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Microsoft Designing and Implementing a Data Science Solution on Azure Sample Questions (Q36-Q41):

NEW QUESTION # 36
You are conducting feature engineering to prepuce data for further analysis.
The data includes seasonal patterns on inventory requirements.
You need to select the appropriate method to conduct feature engineering on the data.
Which method should you use?

A. Finite Impulse Response (FIR) Filter module.
B. Exponential Smoothing (ETS) function.
C. Time Series Anomaly Detection module
D. One Class Support Vector Machine module

Answer: A

NEW QUESTION # 37
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:

NEW QUESTION # 38
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 train and register a machine learning model.
You plan to deploy the model as a real-time web service. Applications must use key-based authentication to use the model.
You need to deploy the web service.
Solution:
Create an AciWebservice instance.
Set the value of the ssl_enabled property to True.
Deploy the model to the service.
Does the solution meet the goal?

A. No
B. Yes

Answer: A

Explanation:
Instead use only auth_enabled = TRUE
Note: Key-based authentication.
Web services deployed on AKS have key-based auth enabled by default. ACI-deployed services have key-based auth disabled by default, but you can enable it by setting auth_enabled = TRUE when creating the ACI web service. The following is an example of creating an ACI deployment configuration with key-based auth enabled.
deployment_config <- aci_webservice_deployment_config(cpu_cores = 1,
memory_gb = 1,
auth_enabled = TRUE)
Reference:
https://azure.github.io/azureml-sdk-for-r/articles/deploying-models.html

NEW QUESTION # 39
You use Azure Machine Learning Designer lo load the following datasets into an experiment:
Dataset1:

Dataset2:

You need to create a dataset that has the same columns and header row as the input datasets and contains all rows from both input datasets.
Solution: Use the Add Rows component.
Does the solution meet the goal?

A. No
B. Yes

Answer: A

NEW QUESTION # 40
You manage an Azure Machine Learning workspace. The development environment is configured with a Serverless Spark compute in Azure Machine Learning Notebooks.
You perform interactive data wrangling to clean up the Titanic dataset and store it as a new dataset (Line numbers are used for reference only.)

For each of the following statements, select Yes if the statement is true Otherwise, select No NOTE: Bach correct selection is worth one point.

Answer:

Explanation:

Explanation:

NEW QUESTION # 41
......

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