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| Section | Weight | Objectives |
|---|---|---|
| Fundamentals of machine learning on Azure | 25-30% | - Core machine learning concepts
|
| Features of natural language processing (NLP) workloads on Azure | 30-35% | - Text analytics and language understanding
|
| 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 # 134
To complete the sentence, select the appropriate option in the answer area.
Answer:
Explanation:
Explanation:
According to the Microsoft Azure AI Fundamentals (AI-900) official study guide and the Microsoft Learn module "Describe core concepts of machine learning on Azure", labeling is the process of assigning correct output values (labels) to training data before model training. In supervised learning, every input in the dataset must be paired with its corresponding output so the algorithm can learn the relationship between the two.
In this scenario, the task is to assign classes to images before training a classification model-for example, marking images as "cat," "dog," or "bird." This process defines the target variable (label) that the model will later predict. During training, the classification model uses these labeled examples to learn patterns and distinguish between categories.
Microsoft's official materials clearly define labeling as:
"The process of tagging data with the correct answer so that the model can learn to make predictions." Labeling is a crucial early step in the machine learning lifecycle, especially for image classification and natural language processing (NLP) tasks. Without accurate labels, the model cannot learn correctly and its predictions will be unreliable.
Let's briefly clarify why the other options are incorrect:
* Evaluation refers to testing the model after training to measure accuracy or performance using metrics like precision, recall, or F1 score.
* Feature engineering involves creating or selecting the most relevant input features from raw data but does not involve tagging output labels.
* Hyperparameter tuning adjusts parameters (like learning rate or depth of a tree) to optimize model performance after labeling and training have begun.
Thus, assigning classes to images prior to model training is definitively a Labeling task.
NEW QUESTION # 135
Match the Azure OpenAI large language model (LLM) process to the appropriate task.
To answer, drag the appropriate process from the column on the left to its task on the right. Each process may be used once, more than once, or not at all.
NOTE: Each correct match is worth one point.
Answer:
Explanation:
NEW QUESTION # 136
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 # 137
Match the principles of responsible AI to the appropriate descriptions.
To answer, drag the appropriate principle from the column on the left to its description on the right. Each principle may be used once, more than once, or not at all.
NOTE: Each correct match is worth one point.
Answer:
Explanation:
NEW QUESTION # 138
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:
Explanation:
This question is derived from the Microsoft Azure AI Fundamentals (AI-900) learning module, particularly under "Describe features of conversational AI workloads on Azure." It tests understanding of chatbot capabilities and design principles within the context of Azure Bot Service and Conversational AI.
* Chatbots can support voice input - YesAccording to the AI-900 official materials, conversational AI systems such as chatbots can interact with users through text or voice. Using speech recognition services like Azure Cognitive Services Speech-to-Text, bots can interpret spoken input, and with Text- to-Speech, they can respond verbally. This enables voice-based chatbots used in virtual assistants, call centers, and customer support. Hence, voice input is fully supported by conversational AI solutions in Azure.
* A separate chatbot is required for each communication channel - NoThe Azure Bot Service is designed to provide multi-channel communication from a single bot instance. A single chatbot can communicate across several channels such as Microsoft Teams, Web Chat, Slack, Facebook Messenger, and email without needing separate bots for each platform. This centralized design allows developers to create, deploy, and manage one bot while configuring multiple channel connections through the Azure portal.
Therefore, the statement is false.
* Chatbots manage conversation flows by using a combination of natural language and constrained option responses - YesIn Microsoft's AI-900 training, chatbots are described as using Natural Language Processing (NLP) to understand free-form user input while also guiding interactions with predefined options such as buttons or quick replies. This hybrid approach ensures both flexibility and control, improving user experience and accuracy. Bots can interpret natural language via services like Language Understanding (LUIS) and also present structured options to guide conversations efficiently.
NEW QUESTION # 139
......
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