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.