Designing and Implementing a Data Science Solution on Azure
Last Update November 24, 2024
Total Questions : 441
Our Microsoft Azure DP-100 exam questions and answers cover all the topics of the latest Designing and Implementing a Data Science Solution on Azure exam, See the topics listed below. We also provide Microsoft DP-100 exam dumps with accurate exam content to help you prepare for the exam quickly and easily. Additionally, we offer a range of Microsoft DP-100 resources to help you understand the topics covered in the exam, such as Microsoft Azure video tutorials, DP-100 study guides, and DP-100 practice exams. With these resources, you can develop a better understanding of the topics covered in the exam and be better prepared for success.
| Exam Name | Designing and Implementing a Data Science Solution on Azure |
| Exam Code | DP-100 |
| Actual Exam Duration | The duration of the Microsoft DP-100 exam is 120 minutes (2 hours). |
| Expected no. of Questions in Actual Exam | 60 |
| What exam is all about | Microsoft DP-100 is an exam that tests the knowledge and skills of candidates in designing and implementing data science solutions using Microsoft Azure technologies. The exam covers various topics such as data exploration, data preparation, modeling, and deployment. Candidates are expected to have a good understanding of Azure Machine Learning, Azure Databricks, and other Azure services related to data science. The exam is intended for data scientists, data engineers, and other professionals who work with data and want to demonstrate their expertise in designing and implementing data science solutions on the Azure platform. |
| Passing Score required | The passing score required in the Microsoft DP-100 exam is 700 out of 1000. This means that you need to answer at least 70% of the questions correctly to pass the exam. The actual passing score may vary depending on the difficulty level of the exam and the number of questions included in it. It is important to note that the passing score is subject to change without prior notice, so it is best to check the official Microsoft website for the latest information. |
| Competency Level required | Based on the official Microsoft DP-100 Exam page, the exam is designed for data scientists and machine learning engineers who have experience with data science and machine learning on Azure. The exam measures the candidate's ability to design and implement machine learning models on Azure, including data preparation, feature engineering, model training, and deployment. Therefore, the competency level required for the exam is intermediate to advanced, and candidates should have practical experience with Azure machine learning services and tools. |
| Questions Format | The Microsoft DP-100 exam consists of multiple-choice questions, drag and drop questions, and scenario-based questions. The exam may also include simulations and case studies. |
| Delivery of Exam | The Microsoft DP-100 exam is a computer-based exam that is delivered through the Pearson VUE testing centers. It is a proctored exam, which means that a proctor will monitor the exam taker throughout the duration of the exam to ensure that the exam is taken fairly and without any cheating. The exam consists of multiple-choice questions and is timed, with a total of 180 minutes (3 hours) allotted for completion. |
| Language offered | English, Japanese, Chinese (Simplified), Korean, German, Chinese (Traditional), French, Spanish, Portuguese (Brazil), Russian, Arabic (Saudi Arabia), Italian, Indonesian (Indonesia) |
| Cost of exam | $165 USD |
| Target Audience | The Microsoft DP-100 exam is designed for data scientists and machine learning engineers who are interested in designing and implementing machine learning models on Microsoft Azure. The target audience for this exam includes: 1. Data scientists who want to learn how to use Azure Machine Learning to build and deploy machine learning models. 2. Machine learning engineers who want to learn how to use Azure Machine Learning to build and deploy machine learning models. 3. Developers who want to learn how to use Azure Machine Learning to build and deploy machine learning models. 4. IT professionals who want to learn how to use Azure Machine Learning to build and deploy machine learning models. 5. Business analysts who want to learn how to use Azure Machine Learning to build and deploy machine learning models. 6. Anyone who wants to learn how to use Azure Machine Learning to build and deploy machine learning models. |
| Average Salary in Market | The average salary for a Microsoft Certified: Azure Data Scientist Associate is around $120,000 per year in the United States. However, the salary may vary depending on the location, industry, and experience level. |
| Testing Provider | You can visit the official Microsoft website to register for the exam and find authorized testing centers near you. |
| Recommended Experience | I can provide you with the recommended experience for the Microsoft DP-100 exam. According to Microsoft, the recommended experience for the DP-100 exam includes: 1. Basic knowledge of programming languages such as Python or R 2. Understanding of data storage and data processing concepts 3. Familiarity with Azure Machine Learning and Azure Databricks 4. Knowledge of machine learning algorithms and techniques 5. Experience with data visualization tools such as Power BI or Tableau 6. Understanding of statistical concepts and methods It is also recommended to have hands-on experience with Azure Machine Learning and Azure Databricks, as well as experience in building and deploying machine learning models. |
| Prerequisite | According to Microsoft's official website, there are no specific prerequisites for the DP-100 exam. However, it is recommended that candidates have a basic understanding of data science and machine learning concepts, as well as experience with programming languages such as Python and SQL. Additionally, candidates should have experience working with Azure Machine Learning and Azure Databricks. |
| Retirement (If Applicable) | Microsoft usually announces the retirement date of an exam at least six months in advance. It is recommended to check the Microsoft website or contact their support team for the latest information on the retirement date of the DP-100 exam. |
| Certification Track (RoadMap): | The Microsoft DP-100 exam is a certification exam that focuses on designing and implementing machine learning solutions. The certification track or roadmap for this exam includes the following steps: 1. Understanding the basics of machine learning: Before taking the DP-100 exam, it is important to have a good understanding of the basics of machine learning, including the different types of machine learning algorithms, data preprocessing techniques, and model evaluation methods. 2. Learning Azure Machine Learning: The DP-100 exam focuses on designing and implementing machine learning solutions using Azure Machine Learning. Therefore, it is important to have a good understanding of Azure Machine Learning, including its features, capabilities, and limitations. 3. Designing machine learning models: The DP-100 exam tests your ability to design machine learning models that meet specific business requirements. This includes selecting appropriate algorithms, preprocessing data, and tuning hyperparameters. 4. Implementing machine learning models: The DP-100 exam also tests your ability to implement machine learning models using Azure Machine Learning. This includes deploying models, creating pipelines, and monitoring model performance. 5. Evaluating machine learning models: The DP-100 exam tests your ability to evaluate machine learning models and make recommendations for improving their performance. This includes analyzing model output, identifying sources of error, and selecting appropriate evaluation metrics. 6. Maintaining machine learning models: The DP-100 exam also tests your ability to maintain machine learning models over time. This includes monitoring model performance, retraining models, and updating models as new data becomes available. Overall, the DP-100 certification track or roadmap is designed to help you develop the skills and knowledge needed to design and implement effective machine learning solutions using Azure Machine Learning. |
| Official Information | https://www.microsoft.com/en-us/learning/exam-dp-100.aspx |
| See Expected Questions | Microsoft DP-100 Expected Questions in Actual Exam |
| Take Self-Assessment | Use Microsoft DP-100 Practice Test to Assess your preparation - Save Time and Reduce Chances of Failure |
| Section | Weight | Objectives |
|---|---|---|
| Create an Azure Machine Learning workspace | 30-35% | - create an Azure Machine Learning workspace - configure workspace settings - manage a workspace by using Azure Machine Learning studio |
| Manage data objects in an Azure Machine Learning workspace | 30-35% | - register and maintain datastores - create and manage datasets |
| Manage experiment compute contexts | 30-35% | - create a compute instance - determine appropriate compute specifications for a training workload - create compute targets for experiments and training |
| Create models by using Azure Machine Learning Designer | 25-30% | - create a training pipeline by using Azure Machine Learning designer - ingest data in a designer pipeline - use designer modules to define a pipeline data flow - use custom code modules in designer |
| Run training scripts in an Azure Machine Learning workspace | 25-30% | - create and run an experiment by using the Azure Machine Learning SDK - configure run settings for a script - consume data from a dataset in an experiment by using the Azure Machine Learning SDK |
| Generate metrics from an experiment run | 25-30% | - log metrics from an experiment run - retrieve and view experiment outputs - use logs to troubleshoot experiment run errors |
| Automate the model training process | 25-30% | - create a pipeline by using the SDK - pass data between steps in a pipeline - run a pipeline - monitor pipeline runs |
| Use Automated ML to create optimal models | 20-25% | - use the Automated ML interface in Azure Machine Learning studio - use Automated ML from the Azure Machine Learning SDK - select pre-processing options - determine algorithms to be searched - define a primary metric - get data for an Automated ML run - retrieve the best model |
| Use Hyperdrive to tune hyperparameters | 20-25% | - select a sampling method - define the search space - define the primary metric - define early termination options - find the model that has optimal hyperparameter values |
| Use model explainers to interpret models | 20-25% | - select a model interpreter - generate feature importance data |
| Manage models | 20-25% | - register a trained model - monitor model usage - monitor data drift |
| Create production compute targets | 20-25% | - consider security for deployed services - evaluate compute options for deployment |
| Deploy a model as a service | 20-25% | - configure deployment settings - consume a deployed service - troubleshoot deployment container issues |
| Create a pipeline for batch inferencing | 20-25% | - publish a batch inferencing pipeline - run a batch inferencing pipeline and obtain outputs |
| Publish a designer pipeline as a web service | 20-25% | - create a target compute resource - configure an Inference pipeline - consume a deployed endpoint |
TESTED 24 Nov 2024
