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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Features of computer vision workloads on Azure | 15-20% | - Computer vision solutions
|
| Topic 2: Features of natural language processing (NLP) workloads on Azure | 30-35% | - Text analytics and language understanding
|
| Topic 3: Fundamentals of machine learning on Azure | 25-30% | - Core machine learning concepts
|
| Topic 4: Describe AI workloads and considerations | 20-25% | - Fundamentals of artificial intelligence concepts
|
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NEW QUESTION # 134
Select the answer that correctly completes the sentence.
Answer:
Explanation:
NEW QUESTION # 135
You plan to deploy an Azure Machine Learning model as a service that will be used by client applications.
Which three processes should you perform in sequence before you deploy the model? To answer, move the appropriate processes from the list of processes to the answer area and arrange them in the correct order.
Answer:
Explanation:
1 - data preparation
2 - model training
3 - model evaluation
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/concept-ml-pipelines
NEW QUESTION # 136
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?
Answer: D
Explanation:
https://docs.microsoft.com/en-us/azure/cognitive-services/text-analytics/overview You can provide the Text Analytics service with unstructured text and it will return a list of entities in the text that it recognizes. You can provide the Text Analytics service with unstructured text and it will return a list of entities in the text that it recognizes. The service can also provide links to more information about that entity on the web. An entity is essentially an item of a particular type or a category; and in some cases, subtype, such as those as shown in the following table.
https://docs.microsoft.com/en-us/learn/modules/analyze-text-with-text-analytics-service/2-get-started-azure
NEW QUESTION # 137
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:
This question examines your understanding of Natural Language Processing (NLP) as described in the Microsoft Azure AI Fundamentals (AI-900) official study guide and Microsoft Learn module "Explore natural language processing." NLP is a branch of artificial intelligence that enables computers to analyze, understand, and generate human language - both written and spoken. Typical NLP tasks include text analytics, language understanding, sentiment analysis, key phrase extraction, and profanity detection.
* Monitoring online service reviews for profanities # YesThis is a classic example of NLP. Detecting profane or inappropriate words in customer reviews requires analyzing text content. Azure Cognitive Services offers Content Moderator and Text Analytics APIs that can detect and filter profanity, sentiment, and offensive language automatically. Microsoft Learn states: "Natural language processing is used to process and analyze text to detect sentiment, key phrases, and inappropriate content." Hence, this task is correctly classified as NLP.
* Identifying brand logos in an image # NoThis task belongs to Computer Vision, not NLP. The Computer Vision API and Custom Vision service in Azure are designed to detect and classify visual elements like logos, objects, or scenes. Since it involves images, not text, it is unrelated to natural language processing.
* Monitoring public news sites for negative mentions of a product # YesThis is another valid example of NLP. The process involves analyzing the sentiment of text from online articles to determine whether mentions of a product are positive, neutral, or negative. Azure Text Analytics provides prebuilt sentiment analysis and entity recognition capabilities that help automate such monitoring.
NEW QUESTION # 138
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 # 139
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