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The Microsoft AI-900: Microsoft Azure AI Fundamentals exam is aimed at those professionals who want to demonstrate their knowledge of basic Machine Learning (ML) and Artificial Intelligence (AI) workloads. The test evaluates their understanding of how to implement both ML and AI on Azure. The candidates who pass this exam are eligible for the Microsoft Certified: Azure AI Fundamentals certification.
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Microsoft Azure AI Fundamentals Sample Questions (Q79-Q84):
NEW QUESTION # 79
Match the types of natural languages processing workloads to the appropriate scenarios.
To answer, drag the appropriate workload type from the column on the left to its scenario on the right. Each workload type may be used once, more than once, or not at all.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Reference:
https://azure.microsoft.com/en-us/services/cognitive-services/text-analytics
NEW QUESTION # 80
To complete the sentence, select the appropriate option in the answer area.
Answer:
Explanation:
Explanation
Text Description automatically generated
Reference:
https://docs.microsoft.com/en-us/azure/cognitive-services/custom-vision-service/getting-started-build-a-classifie
NEW QUESTION # 81
Which service should you use to extract text, key/value pairs, and table data automatically from scanned documents?
- A. Form Recognizer
- B. Text Analytics
- C. Custom Vision
- D. Ink Recognizer
Answer: A
Explanation:
Accelerate your business processes by automating information extraction. Form Recognizer applies advanced machine learning to accurately extract text, key/value pairs, and tables from documents. With just a few samples, Form Recognizer tailors its understanding to your documents, both on-premises and in the cloud. Turn forms into usable data at a fraction of the time and cost, so you can focus more time acting on the information rather than compiling it.
Reference:
https://azure.microsoft.com/en-us/services/cognitive-services/form-recognizer/
NEW QUESTION # 82
You use natural language processing to process text from a Microsoft news story.
You receive the output shown in the following exhibit.
Which type of natural languages processing was performed?
- A. translation
- B. sentiment analysis
- C. key phrase extraction
- D. entity recognition
Answer: D
Explanation:
Section: Describe features of Natural Language Processing (NLP) workloads on Azure Explanation:
Named Entity Recognition (NER) is the ability to identify different entities in text and categorize them into pre- defined classes or types such as: person, location, event, product, and organization.
In this question, the square brackets indicate the entities such as DateTime, PersonType, Skill.
Reference:
https://docs.microsoft.com/en-in/azure/cognitive-services/text-analytics/how-tos/text-analytics-how-to-entity- linking?tabs=version-3-preview
NEW QUESTION # 83
You build a machine learning model by using the automated machine learning user interface (UI).
You need to ensure that the model meets the Microsoft transparency principle for responsible AI.
What should you do?
- A. Set Max concurrent iterations to 0.
- B. Set Validation type to Auto.
- C. Enable Explain best model.
- D. Set Primary metric to accuracy.
Answer: C
Explanation:
Section: Describe Artificial Intelligence workloads and considerations
Explanation:
Model Explain Ability.
Most businesses run on trust and being able to open the ML "black box" helps build transparency and trust.
In heavily regulated industries like healthcare and banking, it is critical to comply with regulations and best practices. One key aspect of this is understanding the relationship between input variables (features) and model output. Knowing both the magnitude and direction of the impact each feature (feature importance) has on the predicted value helps better understand and explain the model. With model explain ability, we enable you to understand feature importance as part of automated ML runs.
Reference:
https://azure.microsoft.com/en-us/blog/new-automated-machine-learning-capabilities-in-azure-machine- learning-service/
NEW QUESTION # 84
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