Practice Exams MS Azure DP-100 Design & Implement DS Sol

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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. Candidates for this exam should have subject matter expertise in applying data science and machine learning to implement and run machine learning workloads on Azure. 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; and managing, deploying, and monitoring scalable machine learning solutions. A candidate for this exam should have knowledge and experience in data science by using Azure Machine Learning and MLflow. Skills measured on Microsoft Azure DP-100 Exam 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%)This exam measures your ability to accomplish the following technical tasks: design and prepare a machine learning solution; explore data and train models; prepare a model for deployment; and deploy and retrain a model. Functional groups 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 Azure Databricks and Azure Synapse Analytics Monitor compute utilization Explore data and train models (35-40%)Explore data by using data assets and data stores Load and transform data Analyze data by using Azure Data Explorer Use differential privacy Create models by using the Azure Machine Learning designer Create a training pipeline Consume data assets from the designer Use designer components to define a pipeline data flow 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 Consume data in a notebook Track model training by using MLflow Evaluate a model Train a model by using Python SDKUse 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 real-time deployment Configure compute for a batch deployment Deploy a model to a real-time endpoint Deploy a model to a batch endpoint Test a real-time deployed service Invoke the batch endpoint to start a batch scoring job Apply machine learning operations (MLOps) practices Trigger an Azure Machine Learning pipeline, including from Azure Dev Ops or Git Hub Automate model retraining based on new data additions or data changes Define event-based retraining triggers The exam is available in the following languages: English, Japanese, Chinese (Simplified), Korean, German, Chinese (Traditional), French, Spanish, Portuguese (Brazil), Russian, Arabic (Saudi Arabia), Italian, Indonesian (Indonesia)