Exam AI-900 topic 1 question 163 discussion

First at all you need to load the dataset, no?

Correct answer is A... A is the first step

Lock and Load 1st step...... A = answer

You need to create a Classification model first. Isn't? Then you can load the dataset for training it.

A. Load the Dataset

Answer is A. Otherwise, how can you split the data without loading?

First create dataset, then configure task https://microsoftlearning.github.io/mslearn-ai-fundamentals/Instructions/Labs/01-machine-learning.html

Splitting the data into training and testing datasets is a crucial step in machine learning model development, as it allows you to train the model on a subset of the data (the training dataset) and then evaluate its performance on a separate dataset (the testing dataset) that it hasn't seen before. Loading the dataset and creating the classification model come after splitting the data, while creating a clustering model is not relevant to this task, as clustering is used for unsupervised learning, while classification is a supervised learning task. Therefore, the correct first step in this scenario is to split the data into training and testing datasets, which will then be used to train and evaluate the Azure Machine Learning classification model. So its C.

A. Load the dataset. The first step in building an Azure Machine Learning classification model to identify defective products is to load the dataset. Loading the dataset is essential as it provides the data that the model will use for training and testing. Once the data is loaded, you can proceed with other steps like data preprocessing, splitting the data, and creating the classification model.

A is the answer. https://learn.microsoft.com/en-us/training/modules/create-classification-model-azure-machine-learning-designer/classification-steps You can think of the steps to train and evaluate a classification machine learning model as: - Prepare data: Identify the features and label in a dataset. Pre-process, or clean and transform, the data as needed. - Train model: Split the data into two groups, a training and a validation set. Train a machine learning model using the training data set. Test the machine learning model for performance using the validation data set. - Evaluate performance: Compare how close the model's predictions are to the known labels. - Deploy a predictive service: After you train a machine learning model, you need to convert the training pipeline into a real-time inference pipeline. Then you can deploy the model as an application on a server or device so that others can use it.

A is the correct answer. The first step in building an Azure Machine Learning classification model to identify defective products is to load the dataset. Loading the dataset is necessary to access and analyze the data, as well as to prepare it for model training. Once the dataset is loaded, you can proceed with data preprocessing, including handling missing values, performing feature engineering, and splitting the data into training and testing datasets (option C). Finally, you can create a classification model (option D) using the prepared dataset. However, the initial step is to load the dataset to work with the data effectively.

A is the right answer

Preparing the data, though its not mentioned clearly in "Prepare data" but we can not perform any analysis until data is loaded

from MS training documentation: https://learn.microsoft.com/en-us/training/modules/create-classification-model-azure-machine-learning-designer/classification-steps "To train a classification model, you need a dataset that includes historical features, characteristics of the entity for which you want to make a prediction, and known label values." You can think of the steps to train and evaluate a classification machine learning model as: 1. Prepare data: Identify the features and label in a dataset. Pre-process, or clean and transform, the data as needed. 2. Train model: Split the data into two groups, a training and a validation set. 3. Evaluate performance: Compare how close the model's predictions are to the known labels. 4. Deploy a predictive service: After you train a machine learning model, you need to convert the training pipeline into a real-time inference pipeline.