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| Section | Weight | Objectives |
|---|---|---|
| Features of natural language processing (NLP) workloads on Azure | 30-35% | - Text analytics and language understanding
|
| Features of computer vision workloads on Azure | 15-20% | - Computer vision solutions
|
| Describe AI workloads and considerations | 20-25% | - Fundamentals of artificial intelligence concepts
|
| Fundamentals of machine learning on Azure | 25-30% | - Core machine learning concepts
|
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NEW QUESTION # 167
For each of the following statements, select Yes if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Reference:
https://docs.microsoft.com/en-us/azure/cloud-adoption-framework/innovate/best-practices/trusted-ai
NEW QUESTION # 168
You need to use Azure Machine Learning designer to build a model that will predict automobile prices.
Which type of modules should you use to complete the model? To answer, drag the appropriate modules to the correct locations. Each module may be used once, more than once, or not at all. You may need to drag the split bar between panes or scroll to view content.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
Box 1: Select Columns in Dataset
For Columns to be cleaned, choose the columns that contain the missing values you want to change. You can choose multiple columns, but you must use the same replacement method in all selected columns.
Example:
The task is to build a machine learning model in Azure Machine Learning designer to predict automobile prices, which is a regression problem since the output (price) is a continuous numeric value. The pipeline must follow the logical data preparation, training, and evaluation flow as outlined in the Microsoft Azure AI Fundamentals (AI-900) study guide and Microsoft Learn module "Create a machine learning model with Azure Machine Learning designer." Here's the correct sequence and reasoning:
* Select Columns in Dataset:The first step after loading the raw automobile dataset is to choose the relevant columns that will be used as features (inputs) and the label (output). This module ensures that only necessary fields (for example, horsepower, engine size, mileage, etc.) are used to train the model while excluding irrelevant columns like vehicle ID or serial number.
* Split Data:Next, the cleaned and filtered dataset must be split into two subsets: training data and testing data (often 70/30 or 80/20). This allows the model to be trained on one portion and evaluated on the other to measure predictive accuracy.
* Linear Regression:Since automobile price prediction is a numeric prediction task, the appropriate learning algorithm is Linear Regression. This supervised algorithm learns relationships between numeric features and the target (price).
Finally, the workflow connects the training data and Linear Regression module to the Train Model module, which outputs a trained regression model. The trained model is then linked to the Score Model module to compare predicted vs. actual prices.
This pipeline fully aligns with Microsoft's recommended process for regression in Azure ML Designer.
NEW QUESTION # 169
For each of the following statements, select Yes if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Reference:
https://azure.microsoft.com/es-es/blog/machine-assisted-text-classification-on-content-moderator-public-preview/
https://docs.microsoft.com/en-us/azure/architecture/data-guide/technology-choices/natural-language-processing
NEW QUESTION # 170
For each of the following statements, select Yes if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Reference:
https://azure.microsoft.com/es-es/blog/machine-assisted-text-classification-on-content-moderator-public-preview/
https://docs.microsoft.com/en-us/azure/architecture/data-guide/technology-choices/natural-language-processing
NEW QUESTION # 171
You are developing a model to predict events by using classification.
You have a confusion matrix for the model scored on test data as shown in the following exhibit.
Use the drop-down menus to select the answer choice that completes each statement based on the information presented in the graphic.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
Box 1: 11
TP = True Positive.
The class labels in the training set can take on only two possible values, which we usually refer to as positive or negative. The positive and negative instances that a classifier predicts correctly are called true positives (TP) and true negatives (TN), respectively. Similarly, the incorrectly classified instances are called false positives (FP) and false negatives (FN).
Box 2: 1,033
FN = False Negative
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/studio/evaluate-model-performance Finding TP is easy. It basically means the value where Predicted and True value is 1 and that is 11 in this case.
False Negative means where true value was 1 but predicted value was 0 and that is 1033 in this case The confusion matrix shows cases where both the predicted and actual values were 1 (known as true positives) at the top left, and cases where both the predicted and the actual values were 0 (true negatives) at the bottom right. The other cells show cases where the predicted and actual values differ (false positives and false negatives).
https://docs.microsoft.com/en-us/learn/modules/create-classification-model-azure-machine-learning-designer
/evaluate-model
NEW QUESTION # 172
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