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Exam Code: DP-100
Exam Name: Designing and Implementing a Data Science Solution on Azure
Exam Q&As: 408 Q&As
Last update: June 22, 2024

Product Description

The Designing and Implementing a Data Science Solution on Azure certification is highly valued in the IT industry and obtaining it can open a variety of career opportunities. One of the best ways to prepare for the Microsoft DP-100 exam is by practicing with high-quality practice questions and understand the answers. QuestionsTube offers DP-100 exam questions with precise answers online for helping you make preparation for DP-100 exam.

Microsoft Certification DP-100 Exam Information

The Designing and Implementing a Data Science Solution on Azure DP-100 exam is the part of the requirements for Microsoft Certified: Azure Data Scientist Associate certification. You for this certification should have subject matter expertise in applying data science and machine learning to implement and run machine learning workloads on Azure. The Microsoft DP-100 exam is available in multiple languages, including English, Japanese, Chinese (Simplified), Korean, German, Chinese (Traditional), French, Spanish, Portuguese (Brazil), Russian, Arabic (Saudi Arabia), Italian, Indonesian (Indonesia). Make sure that you can pass the DP-100 exam and earn the Microsoft Certified: Azure Data Scientist Associate certification to promote your positions.

To make sure that you can prepare for the DP-100 exam well, you need to read all exam objectives first:

  • Determine the appropriate compute specifications for a training workload
  • Describe model deployment requirements
  • Select which development approach to use to build or train a model
  • Create an Azure Machine Learning workspace
  • Manage a workspace by using developer tools for workspace interaction
  • Set up Git integration for source control
  • Select Azure Storage resources
  • Register and maintain datastores
  • Create and manage data assets
  • Create compute targets for experiments and training
  • Select an environment for a machine learning use case
  • Configure attached compute resources, including Apache Spark pools
  • Monitor compute utilization
  • Access and wrangle data during interactive development
  • Wrangle interactive data with Apache Spark
  • Create a training pipeline
  • Consume data assets from the designer
  • Use custom code components in designer
  • Evaluate the model, including responsible AI guidelines
  • Use automated machine learning for tabular data
  • Use automated machine learning for computer vision
  • Use automated machine learning for natural language processing (NLP)
  • Select and understand training options, including preprocessing and algorithms
  • Evaluate an automated machine learning run, including responsible AI guidelines
  • Develop code by using a compute instance
  • Track model training by using MLflow
  • Evaluate a model
  • Train a model by using Python SDKv2
  • Use the terminal to configure a compute instance
  • Select a sampling method
  • Define the search space
  • Define the primary metric
  • Define early termination options
  • Configure job run settings for a script
  • Configure compute for a job run
  • Consume data from a data asset in a job
  • Run a script as a job by using Azure Machine Learning
  • Use MLflow to log metrics from a job run
  • Use logs to troubleshoot job run errors
  • Configure an environment for a job run
  • Define parameters for a job
  • Create a pipeline
  • Pass data between steps in a pipeline
  • Run and schedule a pipeline
  • Monitor pipeline runs
  • Create custom components
  • Use component-based pipelines
  • Describe MLflow model output
  • Identify an appropriate framework to package a model
  • Assess a model by using responsible AI guidelines
  • Configure settings for online deployment
  • Configure compute for a batch deployment
  • Deploy a model to an online endpoint
  • Deploy a model to a batch endpoint
  • Test an online deployed service
  • Invoke the batch endpoint to start a batch scoring job
  • Trigger an Azure Machine Learning job, including from Azure DevOps or GitHub
  • Automate model retraining based on new data additions or data changes
  • Define event-based retraining triggers

DP-100 Practice Questions with Precise Answers

DP-100 practice questions of QuestionsTube are designed to help you prepare for the Designing and Implementing a Data Science Solution on Azure exam by providing a thorough understanding of the exam topics. Each question is accompanied by a precise answer and a detailed explanation that help you understand the concept behind the question. This ensures that you not only memorize the correct answer but also understand the reasoning behind it.

Multiple Formats and Free Updates

QuestionsTube offers Microsoft DP-100 practice questions in two formats: PDF and Exam Engine. The PDF format allows you to study the questions on any device, while the Exam Engine format simulates the real exam experience. The two formats can be downloaded directly without waiting. Additionally, we offer free updates for different periods, including 3-month, 6-month, and 1-year intervals. This ensures that you have access to the most up-to-date content, and you can prepare for the DP-100 exam with confidence.

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In conclusion, the Microsoft DP-100 study materials of QuestionsTube are an excellent resource for anyone preparing for the Designing and Implementing a Data Science Solution on Azure exam. By using the DP-100 exam questions, you can prepare for the exam with confidence and increase your chances of passing the DP-100 exam on the first try.

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