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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Describe AI workloads and considerations | 15-20% | - Identify features of common AI workloads - Identify guiding principles for responsible AI |
| Topic 2: Describe fundamental principles of machine learning on Azure | 30-35% | - Describe Azure Machine Learning capabilities - Describe core machine learning concepts - Identify common machine learning tasks - Describe features of no-code automated ML |
| Topic 3: Describe features of Generative AI workloads on Azure | 15-20% | - Describe generative AI concepts - Identify responsible AI considerations for generative AI - Describe Azure OpenAI Service capabilities |
| Topic 4: Describe features of computer vision workloads on Azure | 15-20% | - Describe Azure capabilities for computer vision - Identify Azure AI services for computer vision - Identify common computer vision tasks |
| Topic 5: Describe features of Natural Language Processing (NLP) workloads on Azure | 15-20% | - Describe Azure capabilities for NLP - Identify common NLP tasks - Identify Azure AI services for NLP |
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NEW QUESTION # 165
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 # 166
To complete the sentence, select the appropriate option in the answer area.
Answer:
Explanation:
Explanation
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/team-data-science-process/create-features
NEW QUESTION # 167
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:
According to the Microsoft Azure AI Fundamentals (AI-900) official study guide and Microsoft Learn module "Identify features of Computer Vision workloads on Azure", the Custom Vision service is a part of Azure Cognitive Services that allows users to build, train, and deploy custom image classification and object detection models. It is primarily designed for still-image analysis, not video processing.
* "The Custom Vision service can be used to detect objects in an image." - Yes.This is correct. The Custom Vision service supports two major model types: classification (categorizing entire images) and object detection (identifying and locating multiple objects within a single image). In object detection mode, the model outputs both the object's category and its position in the image using bounding boxes.
This capability is emphasized in the AI-900 curriculum as an example of applying computer vision to real-world scenarios, such as identifying products on shelves or detecting equipment parts in manufacturing.
* "The Custom Vision service requires that you provide your own data to train the model." - Yes.This statement is also true. Unlike prebuilt computer vision models, Custom Vision is a trainable model that requires users to upload their own labeled images to create a domain-specific AI model. The model's accuracy depends on the quality and quantity of this user-provided data. The AI-900 study materials explain that Custom Vision is used when prebuilt models do not meet specific needs, enabling businesses to train models tailored to unique image sets.
* "The Custom Vision service can be used to analyze video files." - No.This is incorrect. Custom Vision is limited to image-based analysis. To analyze video content (detecting objects or motion in moving frames), Azure provides Video Indexer, which is a separate service designed for extracting insights from video files, including speech, objects, faces, and emotions.
NEW QUESTION # 168
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
Reference:
https://docs.microsoft.com/en-gb/azure/cognitive-services/qnamaker/concepts/data-sources-and-content
https://docs.microsoft.com/en-us/azure/cognitive-services/luis/choose-natural-language-processing-service
NEW QUESTION # 169
Match the machine learning tasks to the appropriate scenarios.
To answer, drag the appropriate task from the column on the left to its scenario on the right. Each task 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/azure/machine-learning/studio/evaluate-model-performance
https://docs.microsoft.com/en-us/azure/machine-learning/concept-automated-ml
NEW QUESTION # 170
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