Exam DP-100 Actual Tests | DP-100 Prep Guide

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The DP-100 Exam covers a wide range of topics, including data exploration, data preparation, modeling, and deployment. You will need to understand key Azure technologies such as Azure Machine Learning, Azure Databricks, and Azure Cosmos DB, as well as programming languages like Python and R. You will also need to be familiar with data storage and processing, data visualization, and machine learning techniques.

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Microsoft DP-100 is a certification exam that focuses on designing and implementing data science solutions on Azure. DP-100 exam validates the candidate's knowledge and skills in using Azure technologies to build and deploy machine learning models, data pipelines, and data analysis solutions. The DP-100 certification is designed for data scientists, data engineers, and other professionals who work with data and want to demonstrate their expertise in Azure-based data science solutions.

To prepare for the Microsoft DP-100 Certification Exam, you should have a solid understanding of data science concepts and techniques, as well as experience working with Azure tools and services. Microsoft offers various training courses and resources to help you prepare for the exam, including online courses, practice tests, and study guides. You can also find numerous third-party resources, such as books and tutorials, to help you prepare for the exam.

Microsoft Designing and Implementing a Data Science Solution on Azure Sample Questions (Q64-Q69):

NEW QUESTION # 64
You manage an Azure Machine Learning workspace named workspace1 with a compute instance named compute1. You connect to compute! by using a terminal window from wofkspace1. You create a file named
"requirements.txt" containing Python dependencies to include Jupyler.
You need to add a new Jupyter kernel to compute1.
Which four commands should you use? To answer, move the appropriate actions from the list of actions to the answer area and arrange them in the correct order.

Answer:

Explanation:

Explanation


NEW QUESTION # 65
You need to resolve the local machine learning pipeline performance issue. What should you do?

Answer: A


NEW QUESTION # 66
You manage are Azure Machine Learning workspace by using the Python SDK v2.
You must create an automated machine learning job to generate a classification model by using data files stored in Parquet format. You must configure an auto scaling compute target and a data asset for the job.
You need to configure the resources for the job.
Which resource configuration 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 # 67
You are developing a machine learning solution by using the Azure Machine Learning designer.
You need to create a web service that applications can use to submit data feature values and retrieve a predicted label.
Which three actions should you perform in sequence? To answer, move the appropriate actions from the list of actions to the answer area and arrange them in the correct order.

Answer:

Explanation:

1 - Create and run a training pipeling
2 - Deploy a service to an inference cluster.
3 - Create and run a real-time inference pipeline.


NEW QUESTION # 68
You are implementing hyperparameter tuning for a model training from a notebook. The notebook is in an Azure Machine Learning workspace. You add code that imports all relevant Python libraries.
You must configure Bayesian sampling over the search space for the num_hidden_layers and batch_size hyperparameters.
You need to complete the following Python code to configure Bayesian sampling.
Which code segments should you use? To answer, select the appropriate options in the answer area NOTE: Each correct selection is worth one point.

Answer:

Explanation:


NEW QUESTION # 69
......

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