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| Section | Weight | Objectives |
|---|---|---|
| Features of Natural Language Processing (NLP) workloads on Azure | 15–20% | - Describe capabilities of Azure Translator - Identify types of NLP solutions - Describe capabilities of Azure Speech - Describe capabilities of Azure Language |
| Features of generative AI workloads on Azure | 20–25% | - Describe capabilities of Azure OpenAI Service - Describe generative AI concepts - Describe use cases for generative AI - Describe responsible AI practices for generative AI |
| Features of computer vision workloads on Azure | 15–20% | - Describe capabilities of Azure Computer Vision - Describe capabilities of Azure Face - Describe capabilities of Azure Form Recognizer - Identify types of computer vision solutions - Describe capabilities of Azure Custom Vision |
| Artificial Intelligence workloads and considerations | 15–20% | - Describe responsible AI principles - Identify types of AI workloads - Describe considerations for developing AI solutions |
| Fundamental principles of machine learning on Azure | 15–20% | - Describe core concepts of machine learning - Describe automated machine learning - Describe machine learning pipelines - Describe capabilities of Azure Machine Learning |
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NEW QUESTION # 262
Match the Azure Cognitive Services service to the appropriate actions.
To answer, drag the appropriate service from the column on the left to its action on the right. Each service may he used once, more than once, or not at all.
NOTE: Each correct match is worth one point.
Answer:
Explanation:
NEW QUESTION # 263
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:
The Text Analytics API is a cloud-based service that provides advanced natural language processing over raw text, and includes four main functions: sentiment analysis, key phrase extraction, named entity recognition, and language detection.
Box 1: Yes
You can detect which language the input text is written in and report a single language code for every document submitted on the request in a wide range of languages, variants, dialects, and some regional/cultural languages. The language code is paired with a score indicating the strength of the score.
Box 2: No
Box 3: Yes
Named Entity Recognition: Identify and categorize entities in your text as people, places, organizations, date/time, quantities, percentages, currencies, and more. Well-known entities are also recognized and linked to more information on the web.
Reference:
https://docs.microsoft.com/en-us/azure/cognitive-services/text-analytics/overview
NEW QUESTION # 264
To complete the sentence, select the appropriate option in the answer area.
Answer:
Explanation:
Reference:
https://docs.microsoft.com/en-us/azure/cognitive-services/computer-vision/concept-object-detection
NEW QUESTION # 265
Select the answer that correctly completes the sentence.
Answer:
Explanation:
Explanation:
When building a K-means clustering model, all features (variables) used in the model must be numeric in nature. According to the Microsoft Azure AI Fundamentals (AI-900) study materials and standard machine learning theory, K-means clustering is an unsupervised learning algorithm that groups data points into clusters based on their similarity - specifically by minimizing the Euclidean distance between data points and their assigned cluster centroids.
Because the K-means algorithm depends on distance calculations, it requires numeric data types. The Euclidean distance (or similar measures) can only be computed between numerical values. Therefore, all categorical or text data must first be converted into numeric form through feature engineering techniques such as one-hot encoding, label encoding, or embedding vectors, depending on the nature of the data.
Here's how K-means works in summary:
* The algorithm initializes a predefined number of centroids (K).
* Each data point is assigned to the nearest centroid based on numeric distance.
* The centroids are recalculated as the mean of the points in each cluster.
* The process repeats until convergence.
If non-numeric data (e.g., text or Boolean) were provided, the model would not be able to calculate distances accurately, leading to computational errors.
Other options are incorrect:
* Boolean and integer types can represent numeric values but are considered special cases; the algorithm requires general numeric representation (e.g., continuous values).
* Text cannot be processed directly without conversion.
Thus, according to Azure Machine Learning and AI-900 official concepts, all features in a K-means clustering model must be numeric to ensure valid mathematical operations and clustering accuracy.
NEW QUESTION # 266
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: A
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 # 267
......
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