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NEW QUESTION # 94
Extracting relationships between data from large volumes of unstructured data is an example of which type of Al workload?
Answer: D
Explanation:
Extracting relationships and insights from large volumes of unstructured data (such as documents, text files, or images) aligns with the Knowledge Mining workload in Microsoft Azure AI. According to the Microsoft AI Fundamentals (AI-900) study guide and Microsoft Learn module "Describe features of common AI workloads," knowledge mining involves using AI to search, extract, and structure information from vast amounts of unstructured or semi-structured content.
In a typical knowledge mining solution, tools like Azure AI Search and Azure AI Document Intelligence work together to index data, apply cognitive skills (such as OCR, key phrase extraction, and entity recognition), and then enable users to discover relationships and patterns through intelligent search. The process transforms raw content into searchable knowledge.
The key characteristics of knowledge mining include:
* Using AI to extract entities and relationships between data points.
* Applying cognitive skills to text, images, and documents.
* Creating searchable knowledge stores from unstructured data.
Hence, B. Knowledge Mining is correct.
The other options-computer vision, NLP, and anomaly detection-deal with image recognition, language understanding, and data irregularities, respectively, not large-scale information extraction.
NEW QUESTION # 95
To complete the sentence, select the appropriate option in the answer area.
Answer:
Explanation:
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/concept-designer#deploy
NEW QUESTION # 96
You need to predict the income range of a given customer by using the following dataset.
Which two fields should you use as features? Each correct answer presents a complete solution.
NOTE: Each correct selection is worth one point.
Answer: C,E
Explanation:
Section: Describe fundamental principles of machine learning on Azure
Explanation:
First Name, Last Name, Age and Education Level are features. Income range is a label (what you want to predict). First Name and Last Name are irrelevant in that they have no bearing on income. Age and Education level are the features you should use.
NEW QUESTION # 97
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-us/azure/cognitive-services/custom-vision-service/get-started-build-detector
NEW QUESTION # 98
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 # 99
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