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Microsoft AI-900 Certification Exam is an entry-level exam that is designed to test an individual's knowledge of AI concepts and their ability to apply them in the Microsoft Azure cloud platform. It is an important credential for individuals who are looking to establish themselves as experts in the field of AI and can be used to demonstrate their knowledge and understanding of AI concepts and their applications.
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Microsoft AI-900 Exam covers a wide range of topics related to AI and machine learning, including machine learning workloads, computer vision workloads, natural language processing workloads, and conversational AI workloads. Candidates will be tested on their understanding of the key concepts and terminology related to these topics, as well as their ability to use the Azure AI services to build intelligent solutions.
NEW QUESTION # 27
Match the Azure Al 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 be used once, more than once, or not at all.
NOTE: Each correct match is worth one point.
Answer:
Explanation:
Explanation:
The correct mapping is based on how each Azure Cognitive Service functions within the Microsoft AI ecosystem, as detailed in the Microsoft Azure AI Fundamentals (AI-900) study guide and Microsoft Learn Cognitive Services documentation.
* Convert spoken requests into text # Azure AI SpeechThe Azure AI Speech service provides speech-to- text (STT) capabilities, which enable an application to recognize spoken language and convert it into written text. This functionality is foundational in voice-enabled applications like digital assistants or transcription services. When a user speaks, this service captures the audio signal and produces an accurate textual representation that can then be processed by other AI services.
* Identify the intent of a user's requests # Azure AI LanguageThe Azure AI Language service (which includes Conversational Language Understanding, formerly LUIS) is designed to extract meaning from text. It identifies intents-the goals or actions a user wants to perform-and entities, which are key details within that request. For example, in the command "Book a flight to Paris," the intent is "book a flight," and the entity is "Paris."
* Apply intent to entities and utterances # Azure AI LanguageAgain, the Language service performs this deeper contextual analysis. It not only identifies what the user wants (intent) but also applies it to utterances (specific user expressions) and entities (data elements extracted from text). This helps conversational AI systems take meaningful actions, such as fulfilling user requests.
In summary, Azure AI Speech handles audio-to-text conversion, while Azure AI Language performs natural language understanding, mapping intents and entities-a workflow essential in intelligent conversational applications.
NEW QUESTION # 28
What is a use case for classification?
Answer: C
Explanation:
According to the Microsoft Azure AI Fundamentals (AI-900) official study guide and Microsoft Learn module "Identify features of classification machine learning", classification is a type of supervised machine learning used when the goal is to predict a categorical outcome. That means the output variable represents discrete labels such as Yes/No, True/False, or Category A/B/C.
In this example, the model is predicting whether a person uses a bicycle (Yes or No) - a binary categorical outcome. The input (distance from home to work) is numeric, but the prediction is a class or category, which makes it a classification problem.
To compare:
* A and D (predicting how many cups of coffee or race minutes) involve numeric predictions, which are regression tasks.
* B (grouping images by similar colors) involves clustering, an unsupervised learning method used to find natural groupings in data.
Thus, the use case that fits classification is predicting whether someone uses a bicycle, since the answer is categorical.
Reference:Microsoft Learn - Identify the types of machine learning models: Classification, Regression, and Clustering (AI-900 Learning Path)
NEW QUESTION # 29
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:
Explanation
Graphical user interface, text, application Description automatically generated
Box 1: Image classification
Image classification is a supervised learning problem: define a set of target classes (objects to identify in images), and train a model to recognize them using labeled example photos.
Box 2: Object detection
Object detection is a computer vision problem. While closely related to image classification, object detection performs image classification at a more granular scale. Object detection both locates and categorizes entities within images.
Box 3: Semantic Segmentation
Semantic segmentation achieves fine-grained inference by making dense predictions inferring labels for every pixel, so that each pixel is labeled with the class of its enclosing object ore region.
Reference:
https://developers.google.com/machine-learning/practica/image-classification
https://docs.microsoft.com/en-us/dotnet/machine-learning/tutorials/object-detection-model-builder
https://nanonets.com/blog/how-to-do-semantic-segmentation-using-deep-learning/
NEW QUESTION # 30
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:
According to the Microsoft Azure AI Fundamentals (AI-900) official study guide and Microsoft Learn module "Identify features of Computer Vision workloads on Azure", the Custom Vision service is a part of Azure Cognitive Services that allows users to build, train, and deploy custom image classification and object detection models. It is primarily designed for still-image analysis, not video processing.
* "The Custom Vision service can be used to detect objects in an image." - Yes.This is correct. The Custom Vision service supports two major model types: classification (categorizing entire images) and object detection (identifying and locating multiple objects within a single image). In object detection mode, the model outputs both the object's category and its position in the image using bounding boxes.
This capability is emphasized in the AI-900 curriculum as an example of applying computer vision to real-world scenarios, such as identifying products on shelves or detecting equipment parts in manufacturing.
* "The Custom Vision service requires that you provide your own data to train the model." - Yes.This statement is also true. Unlike prebuilt computer vision models, Custom Vision is a trainable model that requires users to upload their own labeled images to create a domain-specific AI model. The model's accuracy depends on the quality and quantity of this user-provided data. The AI-900 study materials explain that Custom Vision is used when prebuilt models do not meet specific needs, enabling businesses to train models tailored to unique image sets.
* "The Custom Vision service can be used to analyze video files." - No.This is incorrect. Custom Vision is limited to image-based analysis. To analyze video content (detecting objects or motion in moving frames), Azure provides Video Indexer, which is a separate service designed for extracting insights from video files, including speech, objects, faces, and emotions.
NEW QUESTION # 31
Match the types of computer vision 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/face/
https://docs.microsoft.com/en-us/azure/cognitive-services/computer-vision/concept-object-detection
NEW QUESTION # 32
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