Exam DP-100 topic 2 question 3 discussion

Should be: Create Azure Databricks cluster -> Install Azure ML SDK for Python -> Create and exec Jupyter notebook using AutoML -> Export Jupyter to local env. That because you need auto feature engineering provided by autoML

1. Create Azure Databricks cluster 2. Install Azure ML SDK for Python 3. Create and exec Jupyter notebook using AutoML 4. Export Jupyter to local env

1. Create an Azure Databricks cluster 2. Install the Azure Machine Learning SDK for Python on the cluster 3. Create and execute a Jupyter notebook by using automated machine learning (AutoML) on the cluster 4. When the cluster is ready and has processed the notebook, export your Jupyter notebook to a local environment

Create an Azure Databricks cluster to provide a cloud environment for data scientists to build their notebooks. Install the Azure ML SDK for Python on the cluster to enable data scientists to use automatic feature engineering and model building in machine learning pipelines. Create and execute the Zeppelin notebooks on the cluster to build and train machine learning models using Spark instances with dynamic worker allocation. When the cluster is ready, export Zeppelin notebooks to a local environment to enable version control of the notebooks locally. SaM

Here is the most accurate sequence of actions for creating the desired environment: Create an Azure Databricks cluster Install Microsoft Machine Learning for Apache Spark on the cluster Create and execute Jupyter notebooks using AutoML on the cluster When the cluster is ready and has processed the notebook, export your Jupyter notebook to a local environment for version control. This sequence of actions will allow you to take advantage of the Azure Databricks platform for cloud-based data processing, and the Microsoft Machine Learning for Apache Spark library for automating feature engineering and model building in your Jupyter notebooks. Additionally, exporting the notebooks to a local environment will allow you to version control them and collaborate with other team members.

https://industry40.co.in/azure-hdinsight-and-azure-databricks/

Totally agree 1. Create Azure Databricks cluster 2. Install Azure ML SDK for Python 3. Create and exec Jupyter notebook using AutoML 4. Export Jupyter to local env

Reference this link: https://docs.microsoft.com/en-us/azure/machine-learning/concept-automated-ml Answer is as per DASIST noted: 1. Create Azure Databricks cluster 2. Install Azure ML SDK for Python 3. Create and exec Jupyter notebook using AutoML 4. Export Jupyter to local env Another link that supports this rationale is :https://industry40.co.in/azure-hdinsight-and-azure-databricks/. It clearly outlines why for Spark based environments, Databricks is a better option than HDInsight

Should be: Create Azure Databricks cluster -> Install Azure ML SDK for Python -> Create and exec Jupyter notebook using AutoML -> Export Jupyter to local env. That because you need auto feature engineering provided by autoML

I am still confused on the right answer.

agree: Create Azure Databricks cluster -> Create and exec Jupyter notebook using AutoML -> Install Azure ML SDK for Python -> Export Jupyter to local env

Create Azure Databricks cluster -> Create and exec Jupyter notebook using AutoML -> Install Azure ML SDK for Python -> Export Jupyter to local env

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you can use only azure hdinsights because of condition you can use only apache-spark for data bricks, you must use Hadoop.

I believe Data Bricks is capable, but if you choose that as first step, there is no further actions can be chosen, which all around Zeppelin, but Data Bricks doesn't support Zeppelin.

Azure data bricks meets all the requirements. HDInsight does not. Example: automatic feature engineering is included with autoML. HDinsight does not include this feature. HDinsight: https://docs.microsoft.com/en-us/azure/hdinsight/ Azure Databricks: https://docs.microsoft.com/en-us/azure/databricks/applications/machine-learning/automl-hyperparam-tuning/

Why not jupyter note book?