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The AI-900 exam is an entry-level certification that does not require any prerequisites. However, it is recommended that you have some basic knowledge of cloud computing and programming concepts before taking AI-900 exam. AI-900 exam is available in multiple languages and can be taken online or at a testing center.
Microsoft AI-900 Exam is an entry-level certification exam that does not require any prerequisites. However, candidates should have a basic understanding of cloud computing and Microsoft Azure. AI-900 Exam consists of 40-60 multiple-choice questions, and candidates have 60 minutes to complete it. Upon passing the exam, candidates will receive the Microsoft Certified: Azure AI Fundamentals certification, which is a valuable credential in the AI industry.
Nowadays everyone is interested in the field of Microsoft because it is growing rapidly day by day. The Microsoft Azure AI Fundamentals (AI-900) credential is designed to validate the expertise of candidates. But most of the students are confused about the right preparation material for AI-900 Exam Dumps and they couldn't find real AI-900 exam questions so that they can pass Microsoft AI-900 certification exam in a short time with good grades.
All in all, Microsoft provides two ways to help candidates prepare for the Microsoft AI-900 Exam. These include the free online learning option and the paid, instructor-led training. Let’s discuss the two most popular courses offered by each of these options by the vendor:
This one-day course is part of the instructor-led learning platform and is, therefore, a paid option. As the name suggests, it is meant to introduce the candidates to fundamental AI concepts. The training, in particular, emphasizes the skills you need to become well versed in AI workloads and understand the Azure services that support such solutions. Usually, the course curriculum is viewed as comprehensive blended learning, featuring a combination of online materials and instructor-led training to ensure candidates have a good grasp of the exam topic areas. Even more, the hands-on exercises that are featured here are meant to help learners understand the test domains in real-world scenarios. Familiarity with the internet and computer technology is not a prerequisite but a great combination that will go a long way toward helping you register excellent results in this course and the ultimate exam.
The online gratis class, “Get Started with Artificial Intelligence” features one module and is intended to run for a maximum of 34 minutes. In general, this course is designed to help you get started with Microsoft services that empower new AI solutions. There are no prerequisites associated with this training. However, it features one module with a total of 10 units including the introduction and an understanding of machine learning, anomaly detection, computer vision, natural language processing, conversational AI, and responsible AI just to mention a few. Visit the official certification page to find out more information about this learning path that also includes classes on ML, NLP, and conversational AI.
NEW QUESTION # 97
Match the types of machine learning to the appropriate scenarios.
To answer, drag the appropriate machine learning type from the column on the left to its scenario on the right. Each machine learning type may be used once, more than once, or not at all.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
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:
Location of a damaged product # Yes
Multiple instances of the same product # Yes
Multiple types of damaged products # Yes
All three statements are Yes, because they correctly describe the capabilities of object detection, one of the major workloads in computer vision, as defined in the Microsoft Azure AI Fundamentals (AI-900) study guide and Microsoft Learn module: "Describe features of computer vision workloads on Azure." Object detection is an advanced computer vision technique that allows AI systems not only to classify objects within an image but also to locate them by drawing bounding boxes around each detected object. This differentiates it from simple image classification, which only identifies what objects exist in an image without specifying their locations.
* Identifying the location of a damaged product - YesAccording to Microsoft Learn, object detection can return the coordinates or bounding boxes for recognized objects. Therefore, if the model is trained to detect damaged products, it can pinpoint exactly where those defects appear within an image.
* Identifying multiple instances of a damaged product - YesObject detection models can detect multiple objects of the same class in one image. For instance, if an image contains several damaged products, each will be identified and marked individually. This feature supports tasks such as automated quality inspection in manufacturing, where several defective units may appear simultaneously.
* Identifying multiple types of damaged products - YesObject detection can also distinguish different classes of objects. When trained on multiple labels (e.g., cracked, scratched, or broken items), the model can detect and classify each type of damage in one image.
In Microsoft's AI-900 framework, object detection is presented as a critical part of computer vision workloads capable of locating and classifying multiple objects and categories within visual content.
NEW QUESTION # 99
To complete the sentence, select the appropriate option in the answer area.
Answer:
Explanation:
Explanation
In the most basic sense, regression refers to prediction of a numeric target.
Linear regression attempts to establish a linear relationship between one or more independent variables and a numeric outcome, or dependent variable.
You use this module to define a linear regression method, and then train a model using a labeled dataset. The trained model can then be used to make predictions.
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/algorithm-module-reference/linear-regression
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/machine-learning-initialize-m Regression is a form of machine learning that is used to predict a numeric label based on an item's features.
https://docs.microsoft.com/en-us/learn/modules/create-regression-model-azure-machine-learning-designer/introd
NEW QUESTION # 100
You need to generate images based on user prompts. Which Azure OpenAI model should you use?
Answer: B
Explanation:
According to the Microsoft Azure OpenAI Service documentation and AI-900 official study materials, the DALL-E model is specifically designed to generate and edit images from natural language prompts. When a user provides a descriptive text input such as "a futuristic city skyline at sunset", DALL-E interprets the textual prompt and produces an image that visually represents the content described. This functionality is known as text-to-image generation and is one of the creative AI capabilities supported by Azure OpenAI.
DALL-E belongs to the family of generative models that can create new visual content, expand existing images, or apply transformations to images based on textual instructions. Within Azure OpenAI, the DALL-E API enables developers to integrate image creation directly into applications-useful for design assistance, marketing content generation, or visualization tools. The model learns from vast datasets of text-image pairs and is optimized to ensure alignment, diversity, and accuracy in the produced visuals.
By contrast, the other options serve different purposes:
* A. GPT-4 is a large language model for text-based generation, reasoning, and conversation, not for creating images.
* C. GPT-3 is an earlier text generation model, primarily used for language tasks like summarization, classification, and question answering.
* D. Whisper is an automatic speech recognition (ASR) model used to convert spoken language into written text; it has no image-generation capability.
Therefore, when the requirement is to generate images based on user prompts, the only Azure OpenAI model that fulfills this purpose is DALL-E. This aligns directly with the AI-900 learning objective covering Azure OpenAI generative capabilities for text, code, and image creation.
NEW QUESTION # 101
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:
Statements
Yes
No
A bot that responds to queries by internal users is an example of a conversational AI workload.
# Yes
An application that displays images relating to an entered search term is an example of a conversational AI workload.
# No
A web form used to submit a request to reset a password is an example of a conversational AI workload.
# No
According to the Microsoft Azure AI Fundamentals (AI-900) official study materials, conversational AI workloads are those that enable interaction between humans and AI systems through natural language conversation, either by text or speech. These workloads are typically implemented using Azure Bot Service, Azure Cognitive Services for Language, and Azure OpenAI Service. The key characteristic of a conversational AI workload is the presence of dialogue-the AI interprets user intent and provides a meaningful, contextual response in a conversation-like manner.
* "A bot that responds to queries by internal users is an example of a conversational AI workload." # YESThis fits the definition perfectly. A chatbot that helps employees (internal users) by answering questions about policies, IT issues, or HR procedures is a typical example of conversational AI. It uses natural language understanding to interpret questions and provide automated responses. Microsoft Learn explicitly identifies chatbots as conversational AI solutions designed for both internal and external interactions.
* "An application that displays images relating to an entered search term is an example of a conversational AI workload." # NOThis is not conversational AI because there is no dialogue or language understanding involved. It is an example of information retrieval or computer vision if it uses image recognition, but not conversation.
* "A web form used to submit a request to reset a password is an example of a conversational AI workload." # NOA password reset form is a simple UI-driven process that doesn't require AI or conversational logic. It performs a fixed function based on user input but does not understand or respond to natural language.
Therefore, based on the AI-900 study guide, only the first statement is an example of a conversational AI workload, while the second and third statements are not.
NEW QUESTION # 102
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