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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Describe AI workloads and considerations | 20-25% | - Fundamentals of artificial intelligence concepts
|
| Topic 2: Features of natural language processing (NLP) workloads on Azure | 30-35% | - Text analytics and language understanding
|
| Topic 3: Features of computer vision workloads on Azure | 15-20% | - Computer vision solutions
|
| Topic 4: Fundamentals of machine learning on Azure | 25-30% | - Core machine learning concepts
|
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NEW QUESTION # 244
To complete the sentence, select the appropriate option in the answer area.
Answer:
Explanation:
Explanation
Reliability & Safety
https://en.wikipedia.org/wiki/Tay_(bot)
"To build trust, it's critical that AI systems operate reliably, safely, and consistently under normal circumstances and in unexpected conditions. These systems should be able to operate as they were originally designed, respond safely to unanticipated conditions, and resist harmful manipulation. It's also important to be able to verify that these systems are behaving as intended under actual operating conditions. How they behave and the variety of conditions they can handle reliably and safely largely reflects the range of situations and circumstances that developers anticipate during design and testing. We believe that rigorous testing is essential during system development and deployment to ensure AI systems can respond safely in unanticipated situations and edge cases, don't have unexpected performance failures, and don't evolve in ways that are inconsistent with original expectations"
NEW QUESTION # 245
Match the Azure OpenAI large language model (LLM) process to the appropriate task.
To answer, drag the appropriate process from the column on the left to its task on the right. Each process may be used once, more than once, or not at all.
NOTE: Each correct match is worth one point.
Answer:
Explanation:
Explanation:
According to the Microsoft Azure AI Fundamentals (AI-900) study material and Azure OpenAI Service documentation, large language models (LLMs) such as GPT are capable of performing multiple natural language processing (NLP) tasks depending on the intent of the prompt. These tasks generally fall into categories like classification, generation, summarization, and translation, each with a distinct purpose and output type.
* Classifying - This process involves analyzing text and assigning it to a predefined category or label based on its content. The scenario "Detect the genre of a work of fiction" clearly fits this category. The model must evaluate the text and determine whether it belongs to genres like mystery, romance, or science fiction. This is a classic text classification problem, as the output is a discrete category derived from textual features.
* Summarizing - This process means condensing lengthy text into a shorter version that preserves the key information. In the scenario "Create a list of bullet points based on text input," the model extracts essential information and reformats it as concise bullet points, which is an abstraction form of summarization. Summarization models help users quickly understand the main ideas from long documents, meeting efficiency and readability goals.
* Generating - This refers to the LLM's ability to produce new, creative content based on input instructions. The task "Create advertising slogans from a product description" represents generation because it requires the model to construct original text that didn't previously exist. Generation tasks showcase the creativity and contextual fluency of models like GPT in marketing and content creation.
Thus, these mappings align directly with the Azure OpenAI LLM capabilities taught in AI-900, linking each NLP process with its most suitable real-world task.
NEW QUESTION # 246
You are building a tool that will process images from retail stores and identify the products of competitors.
The solution will use a custom model.
Which Azure Cognitive Services service should you use?
Answer: B
Explanation:
to 300 words in Explanation:
The Custom Vision service under Azure Cognitive Services is specifically designed for image classification and object detection tasks that require a custom-trained model. According to the AI-900 official study materials, Custom Vision enables developers to "build, deploy, and improve image classifiers that recognize specific objects in images based on custom data." In this question, the goal is to build a system that processes images from retail stores and identifies products of competitors. Since these are unique products that may not be part of Microsoft's pre-trained models, a custom model must be created. The Custom Vision service allows you to upload your own labeled images (e.
g., product pictures), train a model to recognize those products, and then deploy it as an API for image recognition tasks.
Other options explained:
* B. Form Recognizer is used to extract text, key-value pairs, and tables from structured or semi- structured documents like invoices or receipts. It is not suitable for object identification.
* C. Face service detects and analyzes human faces, providing attributes like age, emotion, and facial landmarks, but cannot recognize general objects like products.
* D. Computer Vision is a general-purpose image analysis service used for tagging, OCR, and scene recognition, but it uses pre-trained models. It doesn't allow for custom product identification.
Thus, based on Microsoft's guidance, the best fit for recognizing competitor products from images using a custom-trained model is A. Custom Vision.
NEW QUESTION # 247
You need to make the press releases of your company available in a range of languages.
Which service should you use?
Answer: B
Explanation:
Translator is a cloud-based machine translation service you can use to translate text in near real-time through a simple REST API call. The service uses modern neural machine translation technology and offers statistical machine translation technology. Custom Translator is an extension of Translator, which allows you to build neural translation systems.
Reference:
https://docs.microsoft.com/en-us/azure/cognitive-services/translator/
NEW QUESTION # 248
You plan to apply Text Analytics API features to a technical support ticketing system.
Match the Text Analytics API features to the appropriate natural language processing scenarios.
To answer, drag the appropriate feature from the column on the left to its scenario on the right. Each feature 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/architecture/data-guide/technology-choices/natural-language-processing
https://azure.microsoft.com/en-us/services/cognitive-services/text-analytics
NEW QUESTION # 249
......
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