The DP-100 exam format assesses the candidate’s ability to design and implement data science solutions on the Microsoft Azure platform. It covers a wide range of topics, including data exploration and preparation, modeling, feature engineering, and machine learning implementation. Additionally, the exam assesses the candidate’s ability to monitor and optimize models, deploy models, and manage data science infrastructure on Azure.
Successfully completing the DP-100 exam indicates that a candidate possesses a thorough grasp of data science methods and tools on Azure. They can design, build, and implement effective data science solutions aligned with business requirements. This certification targets individuals such as data scientists, data analysts, and machine learning engineers aiming to showcase their expertise in creating data science solutions with Azure.
In this blog post, we will explore the DP-100 exam format in detail, including the exam structure, content, and skills measured. We will also provide some useful tips and resources to help you prepare for the exam and pass it with confidence. Whether you are just starting your data science journey or seeking to validate your skills and expertise, this blog post will provide you with valuable insights into the DP-100 exam and help you achieve your certification goals.
Microsoft Exam DP-100: Overview
Before you begin your tour it is important to understand the intricacies of the exam and what it has to offer. The Microsoft DP-100: Designing and Implementing a Data Science Solution on Azure is a professional exam that entails planning, creating a suitable environment for the data science workloads on azure. Also, it involves the study of running data experiments, training predictive models, managing and optimizing the models, and deploying machine learning models into the production. The Microsoft Exam DP-100 has been developed to measure your ability to accomplish the technical tasks that mainly include setting up an Azure Machine Learning workspace, running experiments and train models, optimizing and managing the models, and also deploying and consuming the models. With the ultimate motive to help you develop the required skills and evaluate you to establish your domain expertise.
Microsoft DP-100 Exam Format
When you are crystal clear with the format of the exam it becomes quite easy to plan for the same and perform better –
- The Microsoft DP-100 exam will consist of 40 to 60 questions, and you’ll have 120 minutes to tackle them all, so speed is crucial.
- There’s no penalty for wrong answers, meaning you can attempt every question without worry.
- The exam fee is $165 USD.
- Questions will be in multiple-choice and multiple-response formats. In multiple-choice, pick the one correct response out of four. For multiple-response, choose the two or more correct answers out of five or more options. Use the elimination technique to select the best match for each question.
Negative Marking – If you happen to mark an incorrect answer, don’t worry—there’s no penalty for wrong answers. This means you can take a guess without the fear of losing points. If your guess is correct, you’ll boost your score. If not, no harm done.
Unscored Content – While giving your exam, you might come across certain unscored questions. Attempting or not attempting such questions will not affect your scores in any manner. These unscored questions are placed in the question paper only to gather statistical information.
Scheduling the Exam
The sole objective of the exam is to measure the ability you have to understand what is cloud concepts, core azure services, security, privacy, compliance, and trust. The exam can be scheduled in the following mentioned ways:
- For non-students interested in technology – Schedule with Pearson VUE
- For students or instructors – Schedule with certiport
Exam policy
The policy of the exam includes that you can cancel the exam within 24 hours prior to the appointment of your schedule. Your entire exam fee will get forfeited if you do not appear on the day of the exam without canceling or rescheduling it. With the help of the certification dash board, you can easily reschedule or cancel the examination.
Certification validity
The certificate for this course never expires. Previously, the certificate for this course used to last for about 3 years or required recertification after 2 years. Now we have overcome that problem and now it can last for a life long.
Course Outline
The course outline of the Microsoft DP-100 exam basically includes four domains and the percentages against them is the weightage for the same.
Design and prepare a machine learning solution (20–25%)
Design a machine learning solution
- Determine the appropriate compute specifications for a training workload (Microsoft Documentation: compute targets in Azure Machine Learning)
- Describe model deployment requirements (Microsoft Documentation: Deploy machine learning models to Azure)
- Select which development approach to use to build or train a model (Microsoft Documentation: Train models with Azure Machine Learning)
Manage an Azure Machine Learning workspace
- Create an Azure Machine Learning workspace (Microsoft Documentation: Create workspace resources you need to get started with Azure Machine Learning)
- Manage a workspace by using developer tools for workspace interaction (Microsoft Documentation: Manage Azure Machine Learning workspaces in the portal or with the Python SDK (v2))
- Set up Git integration for source control (Microsoft Documentation: Source control in Azure Data Factory)
- Create and manage registries
Manage data in an Azure Machine Learning workspace
- Select Azure Storage resources (Microsoft Documentation: Introduction to Azure Storage)
- Register and maintain datastores (Microsoft Documentation: Create datastores)
- Create and manage data assets (Microsoft Documentation: Create data assets)
Manage compute for experiments in Azure Machine Learning
- Create compute targets for experiments and training (Microsoft Documentation: Configure and submit training jobs)
- Select an environment for a machine learning use case (Microsoft Documentation: What are Azure Machine Learning environments?)
- Configure attached compute resources, including Apache Spark pools (Microsoft Documentation: Apache Spark pool configurations in Azure Synapse Analytics)
- Monitor compute utilization
Explore data and train models (35–40%)
Explore data by using data assets and data stores
- Access and wrangle data during interactive development (Microsoft Documentation: What is data wrangling?)
- Wrangle interactive data with Apache Spark (Microsoft Documentation: Interactive Data Wrangling with Apache Spark in Azure Machine Learning)
Create models by using the Azure Machine Learning designer
- Create a training pipeline (Microsoft Documentation: Create a build pipeline with Azure Pipelines)
- Consume data assets from the designer (Microsoft Documentation: Create data assets)
- Use custom code components in designer (Microsoft Documentation: Add code components to a custom page for your model-driven app)
- Evaluate the model, including responsible AI guidelines (Microsoft Documentation: What is Responsible AI?)
Use automated machine learning to explore optimal models
- Use automated machine learning for tabular data (Microsoft Documentation: What is automated machine learning (AutoML)?)
- Use automated machine learning for computer vision
- Use automated machine learning for natural language processing (Microsoft Documentation: Set up AutoML to train a natural language processing model)
- Select and understand training options, including preprocessing and algorithms
- Evaluate an automated machine learning run, including responsible AI guidelines (Microsoft Documentation: What is Responsible AI?)
Use notebooks for custom model training
- Develop code by using a compute instance (Microsoft Documentation: Create and manage an Azure Machine Learning compute instance)
- Track model training by using MLflow (Microsoft Documentation: Track ML experiments and models with MLflow)
- Evaluate a model (Microsoft Documentation: Evaluate Model component)
- Train a model by using Python SDKv2
- Use the terminal to configure a compute instance (Microsoft Documentation: Access a compute instance terminal in your workspace)
Tune hyperparameters with Azure Machine Learning
- Select a sampling method (Microsoft Documentation: Sampling in Application Insights)
- Define the search space
- Define the primary metric (Microsoft Documentation: Set up AutoML training with the Azure ML Python SDK v2)
- Define early termination options (Microsoft Documentation: Hyperparameter tuning a model (v2))
Prepare a model for deployment (20–25%)
Run model training scripts
- Configure job run settings for a script (Microsoft Documentation: Configure and submit training jobs)
- Configure compute for a job run
- Consume data from a data asset in a job (Microsoft Documentation: Create data assets)
- Run a script as a job by using Azure Machine Learning (Microsoft Documentation: Azure Machine Learning in a day, Configure and submit training jobs)
- Use MLflow to log metrics from a job run (Microsoft Documentation: Log metrics, parameters and files with MLflow)
- Use logs to troubleshoot job run errors (Microsoft Documentation: Review logs to diagnose pipeline issues)
- Configure an environment for a job run (Microsoft Documentation: Create and target an environment)
- Define parameters for a job (Microsoft Documentation: Runtime parameters)
Implement training pipelines
- Create a pipeline (Microsoft Documentation: Create your first pipeline, What is Azure Pipelines?)
- Pass data between steps in a pipeline (Microsoft Documentation: How to use parameters, expressions and functions in Azure Data Factory)
- Run and schedule a pipeline (Microsoft Documentation: Configure schedules for pipelines)
- Monitor pipeline runs (Microsoft Documentation: Visually monitor Azure Data Factory)
- Create custom components (Microsoft Documentation: Create your first component)
- Use component-based pipelines (Microsoft Documentation: Create and run machine learning pipelines using components with the Azure Machine Learning CLI)
Manage models in Azure Machine Learning
- Describe MLflow model output (Microsoft Documentation: Track ML experiments and models with MLflow)
- Identify an appropriate framework to package a model (Microsoft Documentation: Model management, deployment, and monitoring with Azure Machine Learning)
- Assess a model by using responsible AI guidelines (Microsoft Documentation: What is Responsible AI?)
Deploy and retrain a model (10–15%)
Deploy a model
- Configure settings for online deployment (Microsoft Documentation: Configuration options for the Office Deployment Tool)
- Configure compute for a batch deployment (Microsoft Documentation: Deploy applications to compute nodes with Batch application packages)
- Deploy a model to an online endpoint (Microsoft Documentation: Deploy and score a machine learning model by using an online endpoint)
- Deploy a model to a batch endpoint (Microsoft Documentation: Use batch endpoints for batch scoring)
- Test an online deployed service (Microsoft Documentation: Testing the Deployment)
- Invoke the batch endpoint to start a batch scoring job (Microsoft Documentation: Use batch endpoints for batch scoring)
Apply machine learning operations (MLOps) practices
- Trigger an Azure Machine Learning job, including from Azure DevOps or GitHub (Microsoft Documentation: Trigger Azure Machine Learning jobs with GitHub Actions)
- Automate model retraining based on new data additions or data changes
- Define event-based retraining triggers (Microsoft Documentation: Create a trigger that runs a pipeline in response to a storage event)
Exam Result
It will be declared after the exam, immediately. Also, it requires a score of 700 or above to qualify for the exam. Good preparation is the key to success. The resource that you will use also affects the result, so use the proper resources. Therefore, for your convenience, below mentioned are some of these resources.
Learning Resources for Microsoft Exam DP-100
You can get a lot of online resources to prepare for Microsoft DP 100 but choosing the best resource will surely make a lot of difference. Therefore, you must select the best resources to successfully pass the exam. Since we have gathered complete and accurate details of the exam, now is the time to choose the learning path for yourself to prepare better. You should also refer to Microsoft documentation, and Microsoft’s study guide as well. To prepare more efficiently you can also join the Microsoft online community. From there you can get many helpful free online study material and learn by participating in online communities.
Also, nothing can really beat the instructor-led training. The official training recommended by Microsoft for Exam DP-100 is the Designing and Implementing a Data Science Solution on Azure training course. This resource offers detailed insights into each concept, increasing your chances of scoring well. Additionally, taking practice or mock tests can familiarize you with the actual exam environment, enhancing your preparation.
In addition to the above-mentioned learning resources, we also provide Microsoft DP-100 Online Tutorials and Practice Tests to help you in your preparation. These tutorials serve as a comprehensive guide for your career path, offering essential study resources to boost your knowledge. Exploring these tutorials is a valuable step in your journey to become a Microsoft Certified Azure Data Scientist Associate.
Expert’s Corner
The Microsoft DP-100 exam is taking over the market at a very good pace today. So taking this certification will be worth your hard work in the future. This exam will be beneficial for you in building a strong career in Data Science in Microsoft Azure. This course will also help you to accomplish various business objectives. You must also take practice tests before appearing for the exam to get more detailed knowledge, and thus attain confidence. Appear for the exam with full dedication and confidence and you will definitely clear this exam with flying colours.
