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Microsoft DP-100 Practice Test Questions, Microsoft DP-100 Exam Dumps

Passing the IT Certification Exams can be Tough, but with the right exam prep materials, that can be solved. ExamLabs providers 100% Real and updated Microsoft Data Science DP-100 exam dumps, practice test questions and answers which can make you equipped with the right knowledge required to pass the exams. Our Microsoft DP-100 exam dumps, practice test questions and answers, are reviewed constantly by IT Experts to Ensure their Validity and help you pass without putting in hundreds and hours of studying.

The Microsoft DP-100: Designing and Implementing a Data Science Solution on Azure exam evaluates the skills of the specialists in managing Azure resources for machine learning (ML). It also measures their competence in carrying out the experiments and training models as well as deploying and operationalizing machine learning solutions. This test leads to getting the Microsoft Certified: Azure Data Scientist Associate certification. This is an associate-level certificate that has the Data Scientists as an intended audience. These professionals have the expertise in applying machine learning and data science to implement and run ML workloads on Microsoft Azure. They are responsible for creating and planning the appropriate working settings for data science workloads on Microsoft Azure. They also have the skills in training predictive models and running data experiments. In addition, these individuals should have proficiency in optimizing, deploying, and managing the ML models for production. The exam doesn’t have any official prerequisites, but it is recommended that the applicants possess working experience and some level of knowledge of data science and usage of Azure Databricks and Azure ML.

Exam Details

The individuals can register for and schedule the Microsoft DP-100 test through the Pearson VUE platform. The exam is available in English, Japanese, Korean, and Simplified Chinese. The regular fee is $165. The applicants do not have to fulfill any requirements to register for the test, but it is important to possess an understanding of its topics to increase the chance of achieving the passing score of 700 points on a scale of 1000.

The professionals can consider using the official training options to prepare for the DP-100 test. Microsoft proposes two instructor-led courses. Both of them include the details of the exam domains and equip the candidates with the knowledge that is required for success in the certification test. They focus on important points of the exam and can help the students improve their skills in operating machine learning solutions at the Cloud scale utilizing Azure Machine Learning, managing data ingestion and preparation, training, and deploying models. They also cover using Azure Databricks for exploring, preparing, and modeling data.

Exam Topics

It is important to get familiar with the subject areas of the DP-100 certification test as early as possible. The applicants can download the exam blueprint and explore it to start their preparation. The highlights of the domains that are included in the test are enumerated as follows:

Managing Azure Resources for Machine Learning: The first module checks the skills of the test takers in the following:

  • Creating Azure Machine Learning workspaces: It requires the competence in configuring settings for the workspace and utilizing the Azure ML studio for managing workspaces.
  • Managing compute for experiments in Azure Machine Learning: It covers determining the relevant compute specifications for training workloads and monitoring compute utilization, among others.
  • Implementing security and access control in Azure Machine Learning: The examinees should be able to determine the access prerequisites and map prerequisites for the built-in roles and manage role membership, among others.
  • Setting up an Azure Machine Learning development environment: It requires the experience in creating and sharing compute instances as well as accessing Azure Machine Learning workspaces from other development environments.
  • Setting up Azure Databricks workspaces: It focuses on creating Azure Databricks cluster and workspace as well as creating & running notebooks within Azure Databricks.

Running Experiments and Train Models: This section assesses the competence of the professionals in:

  • Creating models by utilizing the Azure Machine Learning designer: It includes the skills in utilizing the Azure ML designer to create training pipelines and ingesting data within the designer pipeline. It also covers using a custom code module within the designer and utilizing designer modules for defining the pipeline data flow.
  • Running model training scripts: It focuses on creating & running the experiment with Azure ML SDK and running training scripts on Azure Databricks compute.
  • Generating metrics from an experiment run: It requires the proficiency in logging metrics from experiment runs, using ML flow for tracking experiments, retrieving & viewing outputs from experiments.
  • Utilizing Automated Machine Learning to create optimal models: The applicants need to possess the knowledge of utilizing an automated machine learning interface within the Azure ML studio. It is also important to be familiar with defining primary metrics and retrieving the best modules.
  • Tuning hyperparameters with Azure Machine Learning: It covers defining primary metrics and search space as well as choosing sampling methods, among others.

Deploying and Operationalizing Machine Learning Solutions: The next part measures the expertise of the candidates in the following:

  • Selecting compute for model deployment: It covers measuring compute alternatives for deployment.
  • Deploying a model as a service: It focuses on consuming deployed services and deploying registered models, among others.
  • Managing models in Azure Machine Learning: It includes monitoring data drift and model usage as well as registering trained models.
  • Creating an Azure Machine Learning pipeline for batch inferencing: It requires the skills in setting ParallelRunStep as well as configuring compute for batch inference pipelines.
  • Implementing pipelines by utilizing Azure Machine Learning SDK: It covers creating, running, and monitoring pipelines.
  • Applying machine learning operations practices: The students need to have the competence in implementing source control for a script and triggering Azure ML pipelines from Azure DevOps.

Implementing Responsible Machine Learning: The last subject area measures the following skills:

  • Using the model explainers to interpret models: It requires the skills in selecting a model interpreter and generating feature importance data.
  • Describing fairness considerations for models: It covers the proficiency in mitigating model unfairness and measuring model fairness according to prediction disparity.
  • Describing privacy considerations for data: It includes explaining differential privacy principles and specifying acceptable noise levels within data.

Career Opportunities

The test takers who obtain the Microsoft Certified: Azure Data Scientist Associate certificate can explore different career paths in the domain of data science. Some of the job roles that are open to these individuals include a Machine Learning Engineer, a Data Scientist, a Research Scientist, and a Data Science Manager, among others. The average remuneration for these positions is $107,000 per annum.

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