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| Section | Weight | Objectives |
|---|---|---|
| Features of natural language processing (NLP) workloads on Azure | 30-35% | - Text analytics and language understanding
|
| Fundamentals of machine learning on Azure | 25-30% | - Core machine learning concepts
|
| Describe AI workloads and considerations | 20-25% | - Fundamentals of artificial intelligence concepts
|
| Features of computer vision workloads on Azure | 15-20% | - Computer vision solutions
|
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NEW QUESTION # 195
To complete the sentence, select the appropriate option in the answer area.
Answer:
Explanation:
Explanation:
The correct answer is "adding and connecting modules on a visual canvas." According to the Microsoft Azure AI Fundamentals (AI-900) Official Study Guide and the Microsoft Learn module "Explore automated machine learning in Azure Machine Learning," the Azure Machine Learning designer is a drag-and-drop, no-code environment that allows users to create, train, and deploy machine learning models visually. It is specifically designed for users who prefer an intuitive graphical interface rather than writing extensive code.
Microsoft Learn defines Azure Machine Learning designer as a tool that allows you to "build, test, and deploy machine learning models by dragging and connecting pre-built modules on a visual canvas." These modules can represent data inputs, transformations, training algorithms, and evaluation processes. By linking them together, users can create an end-to-end machine learning pipeline.
The designer simplifies the machine learning workflow by allowing data scientists, analysts, and even non- developers to:
* Import and prepare datasets visually.
* Choose and connect algorithm modules (e.g., classification, regression, clustering).
* Train and evaluate models interactively.
* Publish inference pipelines as web services for prediction.
Let's analyze the other options:
* Automatically performing common data preparation tasks - This describes Automated ML (AutoML), not the Designer.
* Automatically selecting an algorithm to build the most accurate model - Also a characteristic of AutoML, where the system tests multiple algorithms automatically.
* Using a code-first notebook experience - This describes the Azure Machine Learning notebooks environment, which uses Python and SDKs, not the Designer interface.
Therefore, based on the official AI-900 learning objectives and Microsoft Learn documentation, the Azure Machine Learning designer allows you to create models by adding and connecting modules on a visual canvas, providing a no-code, interactive experience ideal for users building custom machine learning workflows visually.
NEW QUESTION # 196
To complete the sentence, select the appropriate option in the answer area.
Answer:
Explanation:
Explanation:
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/how-to-label-data
NEW QUESTION # 197
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/dotnet/machine-learning/resources/tasks
NEW QUESTION # 198
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 # 199
You need to scan the news for articles about your customers and alert employees when there is a negative article. Positive articles must be added to a press book.
Which natural language processing tasks should you use to complete the process? To answer, drag the appropriate tasks to the correct locations. Each task 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:
Diagram Description automatically generated
Box 1: Entity recognition
the Named Entity Recognition module in Machine Learning Studio (classic), to identify the names of things, such as people, companies, or locations in a column of text.
Named entity recognition is an important area of research in machine learning and natural language processing (NLP), because it can be used to answer many real-world questions, such as:
Which companies were mentioned in a news article?
Does a tweet contain the name of a person? Does the tweet also provide his current location?
Were specified products mentioned in complaints or reviews?
Box 2: Sentiment Analysis
The Text Analytics API's Sentiment Analysis feature provides two ways for detecting positive and negative sentiment. If you send a Sentiment Analysis request, the API will return sentiment labels (such as "negative",
"neutral" and "positive") and confidence scores at the sentence and document-level.
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/named-entity-recognition
https://docs.microsoft.com/en-us/azure/cognitive-services/text-analytics/how-tos/text-analytics-how-to-sentimen
NEW QUESTION # 200
......
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