DP-100 Designing and Implementing Data Science Solution 2023

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Are you aiming to achieve the prestigious Microsoft Certified: Azure Data Scientist Associate certification? Look no further! Our comprehensive practice test is specially curated to test your knowledge and prepare you with 100% confidence to ace the DP-100 examination. 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 SDKv2Use 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 Dev Ops or Git Hub 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. All the best for your exam