DP-100: Designing and Implementing a Data Science Solution

Prepare with Confidence: Gain an edge with not one, not two, but three meticulously crafted, high-quality practice exams. Our DP-100 Designing and Implementing a Data Science Solution on Azure practice exams are expertly designed to closely match the official certification test, ensuring you're fully prepared for the real deal.

Designed with utmost care, the questions in this practice test are either directly sourced from Azure Documentation or represent real-world data engineering scenarios. This ensures that you are well-prepared for the challenges you may encounter during the exam.

Each question is accompanied by detailed explanations and links to the corresponding Microsoft documentation, where the concept or scenario was framed. By taking this practice test, you will not only gain a deep understanding of the subject matter but also get accustomed to the format of the actual DP-100 exam.

Our practice test is regularly updated to reflect any changes in the testing areas by Microsoft. You can rest assured that you are practicing with the most relevant and up-to-date content, giving you a competitive edge in the certification process.

The objectives covered in this course are:

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

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 (NLP)

• 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

Candidates for this exam should have subject matter expertise integrating, transforming, and consolidating data from various structured and unstructured data systems into a structure that is suitable for building Machine Learning solutions, alongside with the knowledge of data processing languages such as SQL, Python, or Scala, and they need to understand parallel processing and data architecture patterns.