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| Section | Weight | Objectives |
|---|---|---|
| Features of generative AI workloads on Azure | 20–25% | - Describe generative AI concepts - Describe responsible AI practices for generative AI - Describe capabilities of Azure OpenAI Service - Describe use cases for generative AI |
| Features of computer vision workloads on Azure | 15–20% | - Identify types of computer vision solutions - Describe capabilities of Azure Face - Describe capabilities of Azure Custom Vision - Describe capabilities of Azure Computer Vision - Describe capabilities of Azure Form Recognizer |
| Fundamental principles of machine learning on Azure | 15–20% | - Describe machine learning pipelines - Describe automated machine learning - Describe capabilities of Azure Machine Learning - Describe core concepts of machine learning |
| Artificial Intelligence workloads and considerations | 15–20% | - Describe responsible AI principles - Describe considerations for developing AI solutions - Identify types of AI workloads |
| Features of Natural Language Processing (NLP) workloads on Azure | 15–20% | - Describe capabilities of Azure Language - Identify types of NLP solutions - Describe capabilities of Azure Translator - Describe capabilities of Azure Speech |
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NEW QUESTION # 13
You plan to apply Text Analytics API features to a technical support ticketing system.
Match the Text Analytics API features to the appropriate natural language processing scenarios.
To answer, drag the appropriate feature from the column on the left to its scenario on the right. Each feature may be used once, more than once, or not at all.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
Box1: Sentiment analysis
Sentiment Analysis is the process of determining whether a piece of writing is positive, negative or neutral.
Box 2: Broad entity extraction
Broad entity extraction: Identify important concepts in text, including key Key phrase extraction/ Broad entity extraction: Identify important concepts in text, including key phrases and named entities such as people, places, and organizations.
Box 3: Entity Recognition
Named Entity Recognition: Identify and categorize entities in your text as people, places, organizations, date
/time, quantities, percentages, currencies, and more. Well-known entities are also recognized and linked to more information on the web.
Reference:
https://docs.microsoft.com/en-us/azure/architecture/data-guide/technology-choices/natural-language- processing
https://azure.microsoft.com/en-us/services/cognitive-services/text-analytics
NEW QUESTION # 14
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
Box 1: No
Box 2: Yes
Box 3: Yes
Anomaly detection encompasses many important tasks in machine learning:
Identifying transactions that are potentially fraudulent.
Learning patterns that indicate that a network intrusion has occurred.
Finding abnormal clusters of patients.
Checking values entered into a system.
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/anomaly-detection
NEW QUESTION # 15
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://machinelearningmastery.com/difference-test-validation-datasets/
NEW QUESTION # 16
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:
These answers align with the Microsoft Azure AI Fundamentals (AI-900) Official Study Guide and the Microsoft Learn module "Explore conversational AI in Microsoft Azure."
1. A webchat bot can interact with users visiting a website # Yes
This statement is true. The Azure Bot Service allows developers to create intelligent chatbots that can be integrated into a webchat interface. This enables visitors to interact with the bot directly from a website, asking questions and receiving automated responses. This is a typical use case of conversational AI, where natural language processing (NLP) is used to interpret and respond to user input conversationally.
2. Automatically generating captions for pre-recorded videos is an example of conversational AI # No This statement is false. Automatically generating captions from video content is an example of speech-to-text (speech recognition) technology, not conversational AI. While it uses AI to convert spoken words into text, it lacks the two-way interactive communication characteristic of conversational AI. This task is typically handled by the Azure AI Speech service, which transcribes spoken content.
3. A smart device in the home that responds to questions such as "What will the weather be like today?" is an example of conversational AI # Yes This statement is true. Smart home assistants that engage in dialogue with users are powered by conversational AI. These devices use speech recognition to understand spoken input, natural language understanding (NLU) to determine intent, and speech synthesis (text-to-speech) to respond audibly. This represents the full conversational AI loop, where machines communicate naturally with humans.
NEW QUESTION # 17
Select the answer that correctly completes the sentence.
Answer:
Explanation:
NEW QUESTION # 18
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