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| Section | Weight | Objectives |
|---|---|---|
| Describe features of Generative AI workloads on Azure | 15-20% | - Describe Azure OpenAI Service capabilities - Describe generative AI concepts - Identify responsible AI considerations for generative AI |
| Describe AI workloads and considerations | 15-20% | - Identify guiding principles for responsible AI - Identify features of common AI workloads |
| Describe features of Natural Language Processing (NLP) workloads on Azure | 15-20% | - Identify common NLP tasks - Describe Azure capabilities for NLP - Identify Azure AI services for NLP |
| 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 |
| Describe fundamental principles of machine learning on Azure | 30-35% | - Describe features of no-code automated ML - Describe Azure Machine Learning capabilities - Describe core machine learning concepts - Identify common machine learning tasks |
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NEW QUESTION # 298
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 # 299
Match the services to the appropriate descriptions.
To answer, drag the appropriate service from the column on the left to its description on the right. Each service may be used once, more than once, or not at all.
NOTE: Each correct match is worth one point
Answer:
Explanation:
NEW QUESTION # 300
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/azure/cognitive-services/Custom-Vision-Service/overview
NEW QUESTION # 301
Which machine learning technique can be used for anomaly detection?
Answer: A
Explanation:
According to the Microsoft Azure AI Fundamentals (AI-900) Official Study Guide and the Microsoft Learn module "Explore fundamental principles of machine learning," anomaly detection is a specialized machine learning technique used to identify data points, patterns, or events that deviate significantly from normal behavior.
Anomaly detection is widely used for monitoring time-series data and detecting unexpected or rare occurrences that may indicate problems, opportunities, or fraud. For example:
* Detecting fraudulent transactions in banking systems.
* Identifying equipment malfunctions in industrial IoT applications.
* Monitoring network intrusions in cybersecurity.
* Detecting unexpected spikes or drops in web traffic or sales.
In Azure, this workload is supported by the Azure AI Anomaly Detector service, which uses statistical and machine learning algorithms to learn from historical data and establish a baseline of normal behavior. When the system detects data points that fall outside expected patterns, it flags them as anomalies.
Let's evaluate the incorrect options:
* A. A machine learning technique that understands written and spoken language # This describes Natural Language Processing (NLP), not anomaly detection.
* B. A machine learning technique that classifies objects based on user-supplied images # This refers to image classification, typically using computer vision.
* D. A machine learning technique that classifies images based on their contents # Also describes computer vision, not anomaly detection.
Therefore, the correct answer is C, since anomaly detection specifically refers to analyzing data over time and identifying unusual or abnormal patterns that differ from the expected trend.
NEW QUESTION # 302
Select the answer that correctly completes the sentence.
Answer:
Explanation:
NEW QUESTION # 303
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