DP-100: Microsoft Azure Data Scientist Practice Tests 2026

DP-100: Microsoft Azure Data Scientist Associate Practice Exam is a comprehensive and highly effective tool for individuals looking to prepare for the DP-100 certification exam. This practice exam is designed to help candidates familiarize themselves with the latest syllabus and test their knowledge and skills in the field of data science.

This  Practice Exam covers a wide range of topics, including data exploration, data preparation, modeling, and evaluation. By taking this practice exam, candidates can assess their readiness for the actual DP-100 exam and identify areas where they may need to focus their study efforts.

One of the key features of the DP-100 practice exam is its adherence to the latest syllabus. The exam questions are carefully curated to reflect the most up-to-date content and requirements of the DP-100 certification exam. This ensures that candidates are well-prepared for the actual exam and have a clear understanding of the topics that will be covered.

In addition to aligning with the latest syllabus, the DP-100 practice exam also offers a realistic testing experience. The exam questions are designed to mimic the format and difficulty level of the actual DP-100 exam, giving candidates a true sense of what to expect on test day. This realistic testing experience can help candidates build confidence and reduce test anxiety, leading to better performance on the actual exam.

Another key feature of the DP-100 practice exam is its comprehensive coverage of the exam topics. The exam questions are carefully crafted to cover all the key concepts and skills that candidates need to master in order to pass the DP-100 exam. This comprehensive coverage ensures that candidates are well-prepared for any question that may appear on the exam, giving them the best possible chance of success.

DP-100 practice exam also offers detailed explanations for each question. After completing the exam, candidates can review their answers and see detailed explanations for why each answer is correct or incorrect. This feedback can help candidates identify areas where they may need to improve and guide their study efforts moving forward.

In addition to detailed explanations, the DP-100 practice exam also provides performance tracking and analytics. Candidates can see their overall score, as well as their performance on individual topics and question types. This data can help candidates identify their strengths and weaknesses and focus their study efforts on areas where they need the most improvement.

DP-100: Microsoft Azure Data Scientist Associate Practice Exam is a valuable tool for individuals looking to prepare for the DP-100 certification exam. With its realistic testing experience, comprehensive coverage of exam topics, and detailed explanations and performance tracking, this practice exam can help candidates build confidence and improve their chances of passing the DP-100 exam on their first attempt.

DP-100: Microsoft Azure Data Scientist Associate Exam Summary:

• Exam Name : Microsoft Certified - Azure Data Scientist Associate

• Exam code: DP-100

• Exam voucher cost: $165 USD

• Exam languages: English, Japanese, Korean, and Simplified Chinese

• Exam format: Multiple-choice, multiple-answer

• Number of questions: 40-60 (estimate)

• Length of exam: 120minutes

• Passing grade: Score is from 700-1000.

DP-100: Microsoft Azure Data Scientist Associate Exam Syllabus Topics:

• Design and prepare a machine learning solution (20–25%)

• Explore data and train models (35–40%)

• Prepare a model for deployment (20–25%)

• Deploy and retrain a model (10–15%)

Design and prepare a machine learning solution (20–25%)

Design a machine learning solution

• Determine the appropriate compute specifications for a training workload

• Describe model deployment requirements

• Select which development approach to use to build or train a model

Manage an Azure Machine Learning workspace

• Create an Azure Machine Learning workspace

• Manage a workspace by using developer tools for workspace interaction

• Set up Git integration for source control

• Create and manage registries

Manage data in an Azure Machine Learning workspace

• Select Azure Storage resources

• Register and maintain datastores

• Create and manage data assets

Manage compute for experiments in Azure Machine Learning

• Create compute targets for experiments and training

• Select an environment for a machine learning use case

• Configure attached compute resources, including Apache Spark pools

• 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

• Wrangle interactive data with Apache Spark

Create models by using the Azure Machine Learning designer

• Create a training pipeline

• Consume data assets from the designer

• Use custom code components in designer

• Evaluate the model, including responsible AI guidelines

Use automated machine learning to explore optimal models

• Use automated machine learning for tabular data

• Use automated machine learning for computer vision

• Use automated machine learning for natural language processing

• Select and understand training options, including preprocessing and algorithms

• Evaluate an automated machine learning run, including responsible AI guidelines

Use notebooks for custom model training

• Develop code by using a compute instance

• Track model training by using MLflow

• Evaluate a model

• Train a model by using Python SDKv2

• Use the terminal to configure a compute instance

Tune hyperparameters with Azure Machine Learning

• Select a sampling method

• Define the search space

• Define the primary metric

• Define early termination options

Prepare a model for deployment (20–25%)

Run model training scripts

• Configure job run settings for a script

• Configure compute for a job run

• Consume data from a data asset in a job

• Run a script as a job by using Azure Machine Learning

• Use MLflow to log metrics from a job run

• Use logs to troubleshoot job run errors

• Configure an environment for a job run

• Define parameters for a job

Implement training pipelines

• Create a pipeline

• Pass data between steps in a pipeline

• Run and schedule a pipeline

• Monitor pipeline runs

• Create custom components

• Use component-based pipelines

Manage models in Azure Machine Learning

• Describe MLflow model output

• Identify an appropriate framework to package a model

• Assess a model by using responsible AI guidelines

Deploy and retrain a model (10–15%)

Deploy a model

• Configure settings for online deployment

• Configure compute for a batch deployment

• Deploy a model to an online endpoint

• Deploy a model to a batch endpoint

• Test an online deployed service

• Invoke the batch endpoint to start a batch scoring job

Apply machine learning operations (MLOps) practices

• Trigger an Azure Machine Learning job, including from Azure DevOps or GitHub

• Automate model retraining based on new data additions or data changes

• Define event-based retraining triggers

  
  

Overall, the DP-100: Microsoft Azure Data Scientist Associate Practice Exam is a valuable tool for individuals looking to prepare for the DP-100 certification exam. With its realistic testing experience, comprehensive coverage of exam topics, and detailed explanations and performance tracking, this practice exam can help candidates build confidence and improve their chances of passing the DP-100 exam on their first attempt.