Get instant Coupon on our Telegram ChannelBe prepared for the MS Azure Exam DP-100: Designing and Implementing a Data Science Solution on Azure
Description
In order to set realistic expectations, please note: These questions are NOT official questions that you will find on the official exam. These questions DO cover all the material outlined in the knowledge sections below. Many of the questions are based on fictitious scenarios which have questions posed within them.
The official knowledge requirements for the exam are reviewed routinely to ensure that the content has the latest requirements incorporated in the practice questions. Updates to content are often made without prior notification and are subject to change at any time.
Each question has a detailed explanation and links to reference materials to support the answers which ensures accuracy of the problem solutions.
The questions will be shuffled each time you repeat the tests so you will need to know why an answer is correct, not just that the correct answer was item “B” last time you went through the test.
NOTE: This course should not be your only study material to prepare for the official exam. These practice tests are meant to supplement topic study material.
Should you encounter content which needs attention, please send a message with a screenshot of the content that needs attention and I will be reviewed promptly. Providing the test and question number do not identify questions as the questions rotate each time they are run. The question numbers are different for everyone.
As a candidate for this exam, you should have subject matter expertise in applying data science and machine learning to implement and run machine learning workloads on Azure.
Your responsibilities for this role include:
Designing and creating a suitable working environment for data science workloads.
Exploring data.
Training machine learning models.
Implementing pipelines.
Running jobs to prepare for production.
Managing, deploying, and monitoring scalable machine learning solutions.
As a candidate for this exam, you should have knowledge and experience in data science by using:
Azure Machine Learning
MLflow
Skills at a glance
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
Study resources
It is recommended that you train and get hands-on experience before you take the exam.
| Total Students | 2816 |
|---|---|
| Original Price($) | |
| Sale Price | Free |
| Number of lectures | 0 |
| Number of quizzes | 6 |
| Total Reviews | 225 |
| Global Rating | 4.44 |
| Instructor Name | Wade Henderson |
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