Microsoft DP-100: Skills Measured
Microsoft provides you with the elaborate outline of the skills that you need to acquire before attempting the test. The specific topics of the exam along with the main subtopics are enumerated below:
- Perform Feature Engineering (15%)
Answering the questions that are drawn from this domain, the test takers should be able to perform the tasks such as performing feature selection as well as performing feature extraction.
- Define and Prepare the Development Environment (15%)
To answer the questions within this objective, the applicants should have the professional ability to accomplish such technical tasks as selecting a development environment; quantifying the business problem; setting up a development environment; etc.
- Prepare Data for Modeling (25%)
This subject area revolves around cleansing and transforming data; performing EDA (Exploratory Data Analysis); transforming data into usable datasets.
- Develop Models (40%)
The skills measured within this topic include selecting an algorithmic approach; evaluating model performance; training the model; identifying data imbalances; splitting datasets, and so on.
We provide you with comprehensive service
Updating once you bought Designing and Implementing a Data Science Solution on Azure - DP-100 vce dumps from our website; you can enjoy the right of free updating your dumps one-year. If there are latest Designing and Implementing a Data Science Solution on Azure pdf vce released, we will send to your email promptly.
Full refund if you lose exam with our Microsoft Designing and Implementing a Data Science Solution on Azure valid vce, we promise you to full refund. As long as you send the scan of score report to us within 7 days after exam transcripts come out, we will full refund your money.
Invoice When you need the invoice, please email us the name of your company. We will make custom invoice according to your demand.
24/7 customer assisting there are 24/7 customer assisting to support you if you have any questions about our products. Please feel free to contact us.
After purchase, Instant Download DP-100 valid dumps: Upon successful payment, Our systems will automatically send the product you have purchased to your mailbox by email. (If not received within 12 hours, please contact us. Note: don't forget to check your spam.)
Why choose our website
First, choosing our DP-100 Designing and Implementing a Data Science Solution on Azure vce dumps means you can closer to success. We have rich experienced in the real questions of Designing and Implementing a Data Science Solution on Azure. Our Designing and Implementing a Data Science Solution on Azure vce files are affordable, latest and best quality with detailed answers and explanations, which can overcome the difficulty of Designing and Implementing a Data Science Solution on Azure. You will save lots of time and money with our Designing and Implementing a Data Science Solution on Azure valid vce.
Second, the latest Designing and Implementing a Data Science Solution on Azure vce dumps are created by our IT experts and certified trainers who are dedicated to DP-100 Designing and Implementing a Data Science Solution on Azure valid dumps for a long time. All questions of our Designing and Implementing a Data Science Solution on Azure pdf vce are written based on the real questions. Besides, we always check the updating of Designing and Implementing a Data Science Solution on Azure vce files to make sure exam preparation smoothly.
Third, as one of the hot exam of our website, Designing and Implementing a Data Science Solution on Azure has a high pass rate which reach to 89%. According to our customer's feedback, our Designing and Implementing a Data Science Solution on Azure valid vce covers mostly the same topics as included in the real exam. So if you practice our Designing and Implementing a Data Science Solution on Azure valid dumps seriously and review Designing and Implementing a Data Science Solution on Azure vce files, you can pass exam absolutely.
Career Opportunities & Salary Outlook
The demand for the certified professionals is on the increase. As an Azure Data Scientist, you can explore many employment opportunities. So, if you want to boost your career potential, pursuing the Microsoft Certified: Azure Data Scientist Associate certification is the right step. To get this certificate, you have to pass the qualifying exam, which is Microsoft DP-100. With this associate-level certification, you can get the positions, such as a Data Scientist, a Senior Data Scientist, a Data Science Manager, and a Data Science Director. The average salary for these job roles is $135,000 per annum. With advanced experience, the specialists can earn up to $170,000 per year.
For who want to work in Microsoft, passing DP-100 Designing and Implementing a Data Science Solution on Azure is the first step to closer your dream. As one of most reliable and authoritative exam, Designing and Implementing a Data Science Solution on Azure is a long and task for most IT workers. It is very difficult for office workers who have no enough time to practice Designing and Implementing a Data Science Solution on Azure vce files to pass exam at first attempt. So you need a right training material to help you. As an experienced dumps leader, our website provides you most reliable Designing and Implementing a Data Science Solution on Azure vce dumps and study guide. We offer customer with most comprehensive Designing and Implementing a Data Science Solution on Azure pdf vce and the guarantee of high pass rate. The key of our success is to constantly provide the best quality Designing and Implementing a Data Science Solution on Azure valid dumps with the best customer service.
2. Train Models & Run Experiments (25-30%):
- Metrics generation from experiment runs: The candidates must be able to use logs for troubleshooting errors in experiment runs, log metrics from experiment run, and view and retrieve experiment outputs.
- Model training process automation: The individuals need the relevant skills in running pipelines, passing data within steps in pipelines, monitoring pipeline runs, and creating pipelines with the use of SDK.
- Training scripts run within Azure ML workspaces: The students should have the expertise in creating and running experiments utilizing Azure ML SDK as well configuring run settings for the scripts. This subject area also requires their skills in data consumption from datasets for an experiment using Azure ML SDK.
- Models creation with Azure ML Designer: This domain covers the examinees’ skills in using custom code modules within the design and using designer modules for the definition of pipeline data flows. It also requires one’s competence in ingesting data within designer pipelines and creating training pipelines utilizing ML Designer.
Microsoft DP-100 Exam Syllabus Topics:
| Topic | Details |
|---|---|
Manage Azure resources for machine learning (25-30%) | |
| Create an Azure Machine Learning workspace | - create an Azure Machine Learning workspace - configure workspace settings - manage a workspace by using Azure Machine Learning studio |
| Manage data in an Azure Machine Learning workspace | - select Azure storage resources - register and maintain datastores - create and manage datasets |
| Manage compute for experiments in Azure Machine Learning | - determine the appropriate compute specifications for a training workload - create compute targets for experiments and training - configure Attached Compute resources including Azure Databricks - monitor compute utilization |
| Implement security and access control in Azure Machine Learning | - determine access requirements and map requirements to built-in roles - create custom roles - manage role membership - manage credentials by using Azure Key Vault |
| Set up an Azure Machine Learning development environment | - create compute instances - share compute instances - access Azure Machine Learning workspaces from other development environments |
| Set up an Azure Databricks workspace | - create an Azure Databricks workspace - create an Azure Databricks cluster - create and run notebooks in Azure Databricks - link and Azure Databricks workspace to an Azure Machine Learning workspace |
Run Experiments and Train Models (20-25%) | |
| Create models by using the Azure Machine Learning Designer | - 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 model training scripts | - 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 - run a training script on Azure Databricks compute - run code to train a model in an Azure Databricks notebook |
| Generate metrics from an experiment run | - log metrics from an experiment run - retrieve and view experiment outputs - use logs to troubleshoot experiment run errors - use MLflow to track experiments - track experiments running in Azure Databricks |
| Use Automated Machine Learning to create optimal models | - use the Automated ML interface in Azure Machine Learning studio - use Automated ML from the Azure Machine Learning SDK - select pre-processing options - select the algorithms to be searched - define a primary metric - get data for an Automated ML run - retrieve the best model |
| Tune hyperparameters with Azure Machine Learning | - select a sampling method - define the search space - define the primary metric - define early termination options - find the model that has optimal hyperparameter values |
Deploy and operationalize machine learning solutions (35-40%) | |
| Select compute for model deployment | - consider security for deployed services - evaluate compute options for deployment |
| Deploy a model as a service | - configure deployment settings - deploy a registered model - deploy a model trained in Azure Databricks to an Azure Machine Learning endpoint - consume a deployed service - troubleshoot deployment container issues |
| Manage models in Azure Machine Learning | - register a trained model - monitor model usage - monitor data drift |
| Create an Azure Machine Learning pipeline for batch inferencing | - configure a ParallelRunStep - configure compute for a batch inferencing pipeline - publish a batch inferencing pipeline - run a batch inferencing pipeline and obtain outputs - obtain outputs from a ParallelRunStep |
| Publish an Azure Machine Learning designer pipeline as a web service | - create a target compute resource - configure an Inference pipeline - consume a deployed endpoint |
| Implement pipelines by using the Azure Machine Learning SDK | - create a pipeline - pass data between steps in a pipeline - run a pipeline - monitor pipeline runs |
| Apply ML Ops practices | - trigger an Azure Machine Learning pipeline from Azure DevOps - automate model retraining based on new data additions or data changes - refactor notebooks into scripts - implement source control for scripts |
Implement Responsible ML (5-10%) | |
| Use model explainers to interpret models | - select a model interpreter - generate feature importance data |
| Describe fairness considerations for models | - evaluate model fairness based on prediction disparity - mitigate model unfairness |
| Describe privacy considerations for data | - describe principles of differential privacy - specify acceptable levels of noise in data and the effects on privacy |
Reference: https://www.microsoft.com/en-us/learning/exam-dp-100.aspx
Hardy 
Eden 
King 
Tracy 
Antonio 
Jacob 
Marico 
Atalanta 
Catherine 
Josephine 
Doreen 
Norton 
Sally 
Gwendolyn 
Egbert 
Carter 
