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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Features of Natural Language Processing (NLP) workloads on Azure | 15–20% | - Describe capabilities of Azure Translator - Describe capabilities of Azure Speech - Identify types of NLP solutions - Describe capabilities of Azure Language |
| Topic 2: Fundamental principles of machine learning on Azure | 15–20% | - Describe automated machine learning - Describe core concepts of machine learning - Describe machine learning pipelines - Describe capabilities of Azure Machine Learning |
| Topic 3: Features of generative AI workloads on Azure | 20–25% | - Describe responsible AI practices for generative AI - Describe generative AI concepts - Describe capabilities of Azure OpenAI Service - Describe use cases for generative AI |
| Topic 4: Artificial Intelligence workloads and considerations | 15–20% | - Describe responsible AI principles - Describe considerations for developing AI solutions - Identify types of AI workloads |
| Topic 5: Features of computer vision workloads on Azure | 15–20% | - Describe capabilities of Azure Custom Vision - Identify types of computer vision solutions - Describe capabilities of Azure Face - Describe capabilities of Azure Form Recognizer - Describe capabilities of Azure Computer Vision |
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NEW QUESTION # 137
To complete the sentence, select the appropriate option in the answer area.
Answer:
Explanation:
Explanation
With Microsoft's Conversational AI tools developers can build, connect, deploy, and manage intelligent bots that naturally interact with their users on a website, app, Cortana, Microsoft Teams, Skype, Facebook Messenger, Slack, and more.
Reference:
https://azure.microsoft.com/en-in/blog/microsoft-conversational-ai-tools-enable-developers-to-build-connect-and
NEW QUESTION # 138
Select the answer that correctly completes the sentence.
Answer:
Explanation:
NEW QUESTION # 139
You need to identify street names based on street signs in photographs.
Which type of computer vision should you use?
Answer: B
NEW QUESTION # 140
Match the machine learning models to the appropriate deceptions.
To answer, drag the appropriate model from the column on the left to its description on the right Each model may be used once, more than once, or not at all.
NOTE: Each correct match is worth one point.
Answer:
Explanation:
Explanation:
According to the Microsoft Azure AI Fundamentals (AI-900) official study guide and the Microsoft Learn module "Identify features of common machine learning types", the three main machine learning model types differ by their purpose and the kind of data they use - whether supervised (using labeled data) or unsupervised (using unlabeled data).
* Regression # A supervised machine learning model used to predict numeric values.Regression is a type of supervised learning that predicts continuous numerical outcomes. It learns the relationship between input features (independent variables) and a continuous target variable (dependent variable). Examples include predicting house prices, sales revenue, or temperature. The AI-900 curriculum emphasizes regression for "predicting numeric values based on known data," using algorithms such as linear regression or decision tree regression.
* Classification # A supervised machine learning model used to predict categories.Classification is also a supervised learning technique, but it predicts discrete outcomes (classes) instead of continuous values.
It assigns input data to one or more categories based on learned patterns. Typical examples include spam detection (spam vs. not spam), sentiment analysis (positive, neutral, negative), or predicting loan approval (approved/denied). The AI-900 study materials describe classification as "predicting a category or label for new observations."
* Clustering # An unsupervised machine learning model used to group similar entities based on features.
Clustering is an unsupervised learning approach, meaning it works on unlabeled data. It automatically identifies patterns and groups similar data points into clusters based on shared characteristics. Examples include customer segmentation (grouping customers by behavior) and grouping similar documents. The AI-900 learning module explains clustering as "discovering natural groupings in data without predefined labels." Thus, per Microsoft's official AI-900 learning objectives:
* Regression # Predicts numeric/continuous values.
* Classification # Predicts categories/labels.
* Clustering # Groups similar entities (unsupervised).
# Final Verified Configuration:
* Regression # Predict numeric values
* Classification # Predict categories
* Clustering # Group similar entities based on features
NEW QUESTION # 141
To complete the sentence, select the appropriate option in the answer area.
Answer:
Explanation:
Reference:
https://docs.microsoft.com/en-us/azure/architecture/data-guide/technology-choices/natural-language-processing
NEW QUESTION # 142
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