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| Certification Vendor: | Microsoft |
|---|---|
| Exam Name: | Microsoft Azure AI Fundamentals |
| Exam Number: | AI-900 |
| Related Certifications: | Microsoft Certified: Azure AI Fundamentals |
| Real Exam Qty: | 40-60 |
| Exam Format: | Single-choice, Multiple-choice, True/False, Drag and drop, Sequence ordering |
| Exam Duration: | 45-60 |
| Certificate Validity Period: | No expiration (certification does not expire) |
| Passing Score: | 700/1000 |
| Exam Price: | USD 99.00 |
| Available Languages: | Korean, Japanese, Portuguese (Brazil), Chinese (Simplified), French, Spanish, German, English, Indonesian |
| 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/ |
>> Exam AI-900 Registration <<
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Passing the Microsoft AI-900 Certification Exam is a valuable credential for those seeking to demonstrate their expertise in AI and the Azure platform. It can help individuals stand out from the competition in the job market and open up new opportunities for career advancement. Additionally, it provides a foundation for those who wish to pursue further certifications in AI or Azure.
NEW QUESTION # 273
To complete the sentence, select the appropriate option in the answer area.
Answer:
Explanation:
Reference:
https://docs.microsoft.com/en-us/dotnet/machine-learning/resources/tasks
NEW QUESTION # 274
To complete the sentence, select the appropriate option in the answer area.
Answer:
Explanation:
Explanation
Diagram, table Description automatically generated
Reference:
https://docs.microsoft.com/en-us/azure/cloud-adoption-framework/innovate/best-practices/trusted-ai
NEW QUESTION # 275
Select the answer that correctly completes the sentence.
Answer:
Explanation:
Explanation:
According to the Microsoft Azure AI Fundamentals (AI-900) Official Study Guide and the Microsoft Learn module "Explore computer vision in Microsoft Azure," computer vision is a field of artificial intelligence that enables computers to interpret and understand visual information from the world - such as images or videos.
In this scenario, the task is to count the number of animals in an area based on a video feed. This requires the system to:
* Detect the presence of animals in each frame of the video (object detection).
* Track and count them across multiple frames as they move.
These are classic computer vision tasks, as they involve analyzing visual inputs (video or image data) and identifying objects (in this case, animals). Azure provides services such as Azure Computer Vision, Custom Vision, and Video Indexer, which can perform object detection, counting, and activity recognition using AI models trained on visual datasets.
Why the other options are incorrect:
* Forecasting: Involves predicting future values based on historical data (e.g., predicting sales or weather), not analyzing video feeds.
* Knowledge mining: Focuses on extracting insights from large text-based document repositories, not images or videos.
* Anomaly detection: Identifies unusual patterns in numeric or time-series data, not visual objects.
Therefore, identifying and counting animals in video footage falls under computer vision, since it uses AI to visually detect, classify, and quantify objects in real-time or recorded feeds.
NEW QUESTION # 276
Select the answer that correctly completes the sentence.
Answer:
Explanation:
Explanation:
According to the Microsoft Azure AI Fundamentals (AI-900) official study guide and the Microsoft Learn module "Identify features of common machine learning types", a regression model is a type of supervised machine learning model that is used to predict continuous numeric values based on one or more input variables (features).
In supervised learning, models are trained using labeled data, where each input record has a known target value (label). For regression specifically, the label represents a numeric quantity - such as price, age, temperature, or sales figures. The model learns to approximate a function that maps input variables to numeric outputs.
For example:
* Predicting house prices based on size, location, and number of rooms.
* Predicting monthly sales revenue from marketing spend and seasonality.
* Forecasting temperature based on historical weather data.
In Azure Machine Learning Designer and AutoML, when building a regression model, the label column must therefore be of numeric data type (integer or float). If the label were categorical (for example, "yes/no" or
"approved/denied"), the appropriate model type would be classification, not regression.
To contrast with other options:
* Boolean - used in binary classification problems (true/false outcomes).
* Datetime - used for time series forecasting, not standard regression labels.
* Text - used as input features for NLP models, not as regression targets.
Hence, when configuring a regression task in Azure, ensuring the target variable (label) is numeric is a fundamental requirement. The model's performance metrics-such as Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and R²-also rely on numeric computations.
NEW QUESTION # 277
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:
NEW QUESTION # 278
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