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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Describe features of Natural Language Processing (NLP) workloads on Azure | 15-20% | - Identify common NLP tasks - Identify Azure AI services for NLP - Describe Azure capabilities for NLP |
| Topic 2: Describe fundamental principles of machine learning on Azure | 30-35% | - Describe features of no-code automated ML - Identify common machine learning tasks - Describe Azure Machine Learning capabilities - Describe core machine learning concepts |
| Topic 3: Describe AI workloads and considerations | 15-20% | - Identify guiding principles for responsible AI - Identify features of common AI workloads |
| Topic 4: Describe features of Generative AI workloads on Azure | 15-20% | - Describe generative AI concepts - Describe Azure OpenAI Service capabilities - Identify responsible AI considerations for generative AI |
| Topic 5: Describe features of computer vision workloads on Azure | 15-20% | - Identify common computer vision tasks - Describe Azure capabilities for computer vision - Identify Azure AI services for computer vision |
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NEW QUESTION # 94
Match the tasks to the appropriate machine learning models.
To answer, drag the appropriate model from the column on the left to its scenario on the right. Each model may be used once, more than once, or not at all.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
NEW QUESTION # 95
Match the types of AI workloads to the appropriate scenarios.
To answer, drag the appropriate workload type from the column on the left to its scenario on the right. Each workload type may be used once, more than once, or not at all.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation
Box 3: Natural language processing
Natural language processing (NLP) is used for tasks such as sentiment analysis, topic detection, language detection, key phrase extraction, and document categorization.
Reference:
https://docs.microsoft.com/en-us/azure/architecture/data-guide/technology-choices/natural-language-processing
NEW QUESTION # 96
Match the types of AI workloads to the appropriate scenarios.
To answer, drag the appropriate workload type from the column on the left to its scenario on the right. Each workload type may be used once, more than once, or not at all.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Reference:
https://docs.microsoft.com/en-us/learn/paths/get-started-with-artificial-intelligence-on-azure/
NEW QUESTION # 97
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:
NEW QUESTION # 98
You have a dataset that contains the columns shown in the following table.
You have a machine learning model that predicts the value of ColumnE based on the other numeric columns.
Which type of model is this?
Answer: A
Explanation:
The dataset described contains numeric columns (ColumnA through ColumnE). The model's task is to predict the value of ColumnE based on the other numeric columns (A-D). This is a classic regression problem.
According to the Microsoft Azure AI Fundamentals (AI-900) study guide and Microsoft Learn module
"Identify common types of machine learning," a regression model is used when the target variable (the value to predict) is continuous and numeric, such as price, temperature, or-in this case-a numerical value in ColumnE.
Regression models analyze relationships between independent variables (inputs: Columns A-D) and a dependent variable (output: ColumnE) to predict a continuous outcome. Common regression algorithms include linear regression, decision tree regression, and neural network regression.
Option analysis:
* A. Regression: # Correct. Used for predicting numerical, continuous values.
* B. Analysis: # Incorrect. "Analysis" is a general term, not a machine learning model type.
* C. Clustering: # Incorrect. Clustering is unsupervised learning, grouping similar data points, not predicting values.
Therefore, the type of machine learning model used to predict ColumnE (a numeric value) from other numeric columns is Regression, which fits perfectly within Azure's supervised learning models.
NEW QUESTION # 99
......
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