MS Azure DP-100 Mastery: 6 Practice Exam Essentials 2024

Be prepared for the MS Azure Exam DP-100: Designing and Implementing a Data Science Solution on Azure

The ONLY mock test you’ll need to study for, pass, and earn MS Azure DP-100

The DP 100 Microsoft Azure Data Scientist Certification is aimed towards those who apply their knowledge of data science and machine learning to implement and run machine learning workloads on Azure, using Azure Machine Learning Service. This implies planning and creating a suitable working environment for data science workloads on Azure, running data experiments, and training predictive ML models.

What makes this course worth your time ?

6 Practice Tests
360 questions in total
Topics explanation follows quizzes and practice exams so you can test your understanding immediately.

Q&A

Is it possible to take the practice test more than once? Certainly, you are allowed to attempt each practice test multiple times. Upon completion, your final outcome will be displayed, and with every attempt, the sequence of questions and answers will be randomized.
What score is required? The target achievement threshold for each practice test is to achieve at least 75% correct answers.
Do the questions have explanations? Yes, all questions come with explanations for each answer.
Are the questions updated regularly? Indeed, the questions are routinely updated to ensure the best learning experience.

Additional Note: It is strongly recommended that you take these exams multiple times until you consistently score 90% or higher on each test. Take the challenge without hesitation and start your journey today. Good luck!

Exam Topics

The following domains are the torch-bearers of the DP 100 exam.

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

Creating an Azure Machine Learning Workspace- It includes creating an Azure Machine Learning Workspace in Azure ML studio.
Manage Data in Azure ML Workspace- It includes selecting Azure storage services and creating and managing datasets.
Manage to Compute for Experiments- It involves determining the appropriate compute specifications for a training workload, creating compute targets for experiments and training, etc.
Implement security and access control: determine access requirements and map requirements to built-in roles, manage credentials by using Azure Key Vault, etc.
Set up an Azure ML development environment- It involves creating compute instances, and accessing Machine Learning workspaces from other development environments.
Set up an Azure Databricks workspace: create an Azure Databricks workspace, create an Azure Databricks cluster, and create and run notebooks in Azure Databricks

2) Explore data and train models (35-40%)

Creating ML models using Azure ML designer- It includes creating a training pipeline using a designer.
Run training scripts in an Azure ML workspace- It includes creating and running an experiment using Azure ML SDK, consuming data from a dataset, configuring run settings for a script, and more.
Generate Metrics, retrieve experiment outputs & troubleshoot experiment run- It covers log metrics generated from an experiment run and view experiment outputs.
Use Automated Machine Learning to create optimal models- It covers using the Automated ML interface in Azure Machine Learning Studio, defines a primary metric, and retrieves the best model.
Tune hyperparameters with Azure Machine Learning- selecting a sampling method, defining the search space, etc.

3) Prepare a model for deployment (20-25%)

Select Compute for Model Deployment- It includes considering security for deployed services and evaluating compute options for the deployment.
Create an Azure Machine Learning pipeline for batch inferencing – It involves configuring a ParallelRunStep, configuring compute for a batch inferencing pipeline, publishing a batch inferencing pipeline, etc.
Apply ML Ops practices- It includes triggering an Azure Machine Learning pipeline from Azure DevOps, automating model retraining based on new data additions or data changes, and more.

4) Deploy and retrain a model (10-15%)

Use model explainers to interpret models- It involves selecting a model interpreter and generating feature importance data.
Describe fairness considerations for models- It includes evaluating model fairness based on prediction disparity and mitigating model unfairness.
Describe privacy considerations for data- It includes describing principles of differential privacy and specifying acceptable levels of noise in data and the effects on privacy.

Refund Guarantee

This course comes with a full 30 day money-back guarantee.

You either end up with getting those high paying jobs and make an awesome career for yourself, or you try the course and simply get all your money back if you don’t like it…

You literally can’t lose.

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