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Microsoft Certified: Azure Data Scientist Associate Certification Practice Test Questions, Microsoft Certified: Azure Data Scientist Associate Exam Practice Test Questions
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The Microsoft Certified: Azure Data Scientist Associate certification is designed to validate the knowledge and skills of the candidates in machine learning and data science. It certifies their ability to run and implement machine learning workloads on Azure, specifically in the use of the Azure Machine Learning Service. To obtain this certificate, the individuals must pass a single exam, which is Microsoft DP-100.
The target audience for this Microsoft certification is the Azure Data Scientists who can apply their knowledge of machine learning and data science. Due to the fact that this is an associate-level certificate, it does not have any compulsory prerequisites. However, it is recommended that the students understand the topics of the qualifying test before attempting it. Whether you have any technical experience or not, it will boost your performance a lot and help you master the exam content to get the required skills.
To obtain the Microsoft Certified: Azure Data Scientist Associate certification, the applicants must pass one qualifying exam, which is known as Microsoft DP-100: Designing & Implementing a Data Science Solution on Azure. The test is created to measure the skills of the students in accomplishing some technical tasks. These include setting up an Azure Machine Learning workspace, optimizing and managing models, deploying and consuming models, as well as running experiments and training models.
The Microsoft DP-100 exam covers a variety of question formats. These include multiple choice, short answer, drag and drop, build list, and scenario-based. Although Microsoft does not reveal the details of this certification test, the individuals can expect about 40-60 questions to deal with. They will be given 120 minutes to complete the exam. It is available in English, Simplified Chinese, Korean, and Japanese. To register for this test, you must pay the fee of $165. It is important to mention that the price varies from one country to another. Therefore, the examinees should check the official webpage to get an accurate fee for the region where they are taking the test.
The Microsoft DP-100 exam is designed to evaluate one’s skills and knowledge of specific technical tasks. The potential candidates must develop competence in these areas to be able to achieve success in the certification test. Therefore, they need to learn the following:
Setting up an Azure Machine Learning Workspace: 30-35%
- Create an Azure Machine Learning workspace: this subsection requires that the candidates have competence in creating an Azure Machine Learning workspace; configuring workspace settings; managing a workspace through the use of an Azure Machine Learning studio;
- Manage data objects in the Azure ML workspaces: this area covers one’s skills in registering and maintaining datastores, as well as creating and managing datasets;
- Manage Experiment Compute Contexts: the test takers should have the expertise in creating the compute instance; determining the correct compute specifications required for a training workload; creating the compute targets for training and experiments.
Running Experiments and Training Models: 25-30%
- Create models with the use of Azure Machine Learning Designer: the potential candidates for the Microsoft DP-100 exam should have the capacity to create training pipelines with the use of Azure Machine Learning Designer; ingesting data within a designer pipeline; using designer modules for defining the pipeline data flow; using custom code modules in this Designer;
- Run training scripts with an Azure Machine Learning workspace: this domain requires your competence in creating and running an experiment with the use of Azure Machine Learning SDK; configuring running settings for scripts; consuming data from datasets within an experiment with the use of Azure Machine Learning SDK;
- Generate the metrics from the experiment run: the applicants should be able to log metrics from the experiment run; recover and observe experiment outputs; use the logs for the troubleshooting experiment run errors;
- Automate a model training process: this subject area entails the individuals’ skills in creating pipelines with the use of SDK; passing data between steps within a pipeline; running a pipeline; monitoring pipeline runs.
Optimizing and Managing Models: 20-25%
- Utilize Automated Machine Learning to develop optimal models: this objective covers the skills in utilizing the Automated Machine Learning interface within an Azure Machine Learning studio; utilizing Automated Machine Learning from Azure Machine Learning SDK; choosing pre-processing alternatives; determining the algorithms to be investigated; describing primary metrics; accessing data from an Automated Machine Learning run; recovering the best model;
- Utilize Hyperdrive to tune hyperparameters: this area covers your ability to choose a sampling method; describe the search space; describe the primary metric; describe early termination alternatives; locate the model with the optimal hyperparameter values;
- Utilize model explainers for interpreting models: this module involves choosing a model interpreter and generating the feature importance data;
- Manage the model: you should be able to demonstrate competence in registering trained models; monitoring model usage; monitoring data drift.
Deploying and Consuming Models: 20-25%
- Create the production compute targets: this component will evaluate the ability of the candidates to consider the security required for deployed services and measure the compute alternatives for deployment;
- Deploy the model as a service: the learners should have the capacity to configure deployment settings; consume the deployed service; troubleshoot deployment container concerns;
- Create the pipeline for batch inferencing: this section requires that the test takers demonstrate competence in publishing the batch inferencing pipeline as well as running the batch inferencing pipeline and obtaining outputs;
- Publish designer pipelines as a web service: the examinees need to be able to develop the target compute resource; configure the inference pipeline; consume the deployed endpoint.
The Microsoft Certified: Azure Data Scientist Associate certificate comes with a lot of advantages and career opportunities with rewarding salaries are some of them. With this certification, the candidates can look forward to taking up the job roles as an Azure Administrator, an Azure Developer, an Azure Solution Architect, an Azure Consultant, an Azure DevOps Engineer, or an Azure Data Scientist, among others. The salary outlook for these professionals is between $98,000 and $130,000 per annum.
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