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| Certification Vendor: | Microsoft |
|---|---|
| Exam Name: | Microsoft Azure AI Fundamentals |
| Exam Number: | AI-900 |
| Exam Price: | USD 99.00 |
| Related Certifications: | Microsoft Certified: Azure AI Fundamentals |
| Real Exam Qty: | 40-60 |
| Exam Format: | Drag and drop, Sequence ordering, True/False, Multiple-choice, Single-choice |
| Exam Duration: | 45-60 |
| Available Languages: | Japanese, Chinese (Simplified), Korean, German, French, Spanish, Portuguese (Brazil), English, Indonesian |
| Certificate Validity Period: | No expiration (certification does not expire) |
| Passing Score: | 700/1000 |
| Sample Questions: | Microsoft AI-900 Sample Questions |
| Exam Way: | Online proctored exam or in-person testing center (Pearson VUE) |
| Pre Condition: | No formal prerequisites required. Basic knowledge of cloud computing concepts is recommended but not mandatory. |
| Official Syllabus URL: | https://learn.microsoft.com/en-us/certifications/exams/ai-900/ |
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NEW QUESTION # 178
A medical research project uses a large anonymized dataset of brain scan images that are categorized into predefined brain haemorrhage types.
You need to use machine learning to support early detection of the different brain haemorrhage types in the images before the images are reviewed by a person.
This is an example of which type of machine learning?
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 supervised machine learning technique used when the output variable represents discrete categories or classes. In this case, the brain scan images are already labeled into predefined haemorrhage types, such as "subarachnoid," "epidural," or
"intraventricular." The model's goal is to learn patterns from labeled examples and then predict the correct class for new, unseen images.
The use of categorized brain scan images clearly indicates a supervised learning setup because both the input (image data) and output (haemorrhage type) are known during training. This aligns with Microsoft's definition: classification problems "predict which category or class an item belongs to," often using algorithms such as logistic regression, decision trees, neural networks, or convolutional neural networks (CNNs) for image-based data.
In contrast:
* A. Clustering is an unsupervised learning approach that groups data into clusters based on similarity when no predefined labels exist.
* B. Regression predicts continuous numeric values (e.g., predicting age or temperature), not categories.
Because this project aims to automatically classify medical images into known diagnostic categories, it is a textbook example of classification.
Reference:Microsoft Learn - Identify common types of machine learning models: Regression, Classification, and Clustering (AI-900 Learning Path)
NEW QUESTION # 179
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:
Reference:
https://docs.microsoft.com/en-us/azure/architecture/reference-architectures/ai/conversational-bot
https://docs.microsoft.com/en-us/azure/bot-service/bot-builder-webchat-overview?view=azure-bot-service-4.0
NEW QUESTION # 180
For which two workloads can you use computer vision? Each correct answer presents a complete solution.
NOTE: Each correct selection is worth one point.
Answer: A,B
Explanation:
The correct answers are B. assigning the color pixels in an image to object names and C. describing the contents of an image.
According to the Microsoft Azure AI Fundamentals (AI-900) official study guide and Microsoft Learn module "Describe features of computer vision workloads on Azure," computer vision is a branch of AI that enables systems to analyze, interpret, and understand visual data from images and videos. It allows machines to identify objects, people, text, and even describe scenes automatically.
* Option B: Assigning color pixels in an image to object names represents image classification or object detection, which are key computer vision workloads. In these tasks, AI analyzes pixel patterns to determine which pixels correspond to specific objects (for example, classifying pixels as "car," "tree," or "road").
* Option C: Describing the contents of an image corresponds to image captioning, another computer vision workload. It involves using AI models trained to generate natural language descriptions of what is visible in an image, such as "A group of people sitting at a dining table." Azure's Computer Vision service provides this functionality through its "Describe Image" API.
Incorrect options:
* A. Creating photorealistic images involves generative AI and 3D modeling, not traditional computer vision.
* D. Detecting inconsistencies and anomalies in a data stream relates to anomaly detection, not computer vision.
* E. Creating visual representations of numerical data involves data visualization, not AI-driven image analysis.
NEW QUESTION # 181
You are evaluating whether to use a basic workspace or an enterprise workspace in Azure Machine Learning.
What are two tasks that require an enterprise workspace? Each correct answer presents a complete solution.
NOTE: Each correct selection is worth one point.
Answer: C,D
Explanation:
The correct answers are A. Use a graphical user interface (GUI) to run automated machine learning experiments and C. Use a graphical user interface (GUI) to define and run machine learning experiments from Azure Machine Learning designer.
According to the Microsoft Azure AI Fundamentals (AI-900) official documentation and Microsoft Learn module "Create and manage Azure Machine Learning workspaces", there are two workspace tiers: Basic and Enterprise. The Enterprise workspace provides advanced capabilities for automation, visualization, and collaboration that are not available in the Basic tier.
Specifically:
* Automated machine learning (AutoML) using a GUI is only available in the Enterprise tier. AutoML automatically selects algorithms and tunes hyperparameters through the Azure Machine Learning studio interface.
* Azure Machine Learning designer, which allows users to visually drag and drop datasets and modules to create machine learning pipelines, also requires the Enterprise workspace.
In contrast:
* B. Create a compute instance and D. Create a dataset from a CSV file are fundamental actions supported in both Basic and Enterprise workspaces. These do not require the advanced licensing features of the Enterprise edition.
Therefore, tasks involving the graphical, no-code tools-Automated ML (AutoML) and the Designer- require the Enterprise workspace, aligning with AI-900's learning objectives.
Reference:Microsoft Learn - Create and manage Azure Machine Learning workspaces and Automate model training with Azure Machine Learning
NEW QUESTION # 182
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:
In Microsoft Azure AI Language Service, both Named Entity Recognition (NER) and Key Phrase Extraction are core features for text analytics. They serve distinct purposes in analyzing and structuring unstructured text data.
* Named Entity Recognition (NER):NER is used to identify and categorize specific entities within text, such as people, organizations, locations, dates, times, and quantities. According to Microsoft Learn's
"Analyze text with Azure AI Language" module, NER scans text to extract these entities along with their types. Therefore, the statement "Named entity recognition can be used to retrieve dates and times in a text string" is True (Yes).
* Key Phrase Extraction:This feature identifies the most important phrases or main topics in a block of text. It is useful for summarization or highlighting central ideas without classifying them into specific categories. Therefore, the statement "Key phrase extraction can be used to retrieve important phrases in a text string" is also True (Yes).
* City Name Retrieval:While key phrase extraction highlights major phrases, it does not extract specific entities like cities or dates. Extracting such details requires Named Entity Recognition, which is designed to find named entities such as city names, people, or organizations. Hence, the statement "Key phrase extraction can be used to retrieve all the city names in a text string" is False (No).
NEW QUESTION # 183
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