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Reference: https://docs.microsoft.com/en-us/learn/certifications/exams/ai-900
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Microsoft Azure AI Fundamentals Exam Certification Details:
| Duration | 60 mins |
| Sample Questions | Microsoft Azure AI Fundamentals Sample Questions |
| Exam Code | AI-900 |
| Schedule Exam | Pearson VUE |
| Books / Training | Course AI-900T00: Microsoft Azure AI Fundamentals |
| Exam Name | Microsoft Certified - Azure AI Fundamentals |
| Exam Price | $99 (USD) |
| Passing Score | 700 / 1000 |
| Number of Questions | 40-60 |
Microsoft AI-900 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Describe features of computer vision workloads on Azure | 15-20% | - Describe Azure capabilities for computer vision - Identify Azure AI services for computer vision - Identify common computer vision tasks |
| Describe fundamental principles of machine learning on Azure | 30-35% | - Identify common machine learning tasks - Describe core machine learning concepts - Describe Azure Machine Learning capabilities - Describe features of no-code automated ML |
| Describe features of Natural Language Processing (NLP) workloads on Azure | 15-20% | - Describe Azure capabilities for NLP - Identify common NLP tasks - Identify Azure AI services for NLP |
| Describe AI workloads and considerations | 15-20% | - Identify features of common AI workloads - Identify guiding principles for responsible AI |
| Describe features of Generative AI workloads on Azure | 15-20% | - Describe generative AI concepts - Describe Azure OpenAI Service capabilities - Identify responsible AI considerations for generative AI |
Microsoft Azure AI Fundamentals Sample Questions:
You are building a Language Understanding model for an e-commerce business.
You need to ensure that the model detects when utterances are outside the intended scope of the model.
What should you do?
- A. Create a new model
- B. Test the model by using new utterances
- C. Add utterances to the None intent
- D. Create a prebuilt task entity
Correct Answer: C 🗳️
Explanation: Only visible for TestValid members. You can sign-up / login (it's free).
You are processing photos of runners in a race.
You need to read the numbers on the runners ' shirts to identify the runners in the photos. Which type of computer vision should you use?
- A. optical character recognition (OCR)
- B. object detection
- C. image classification
- D. facial recognition
Correct Answer: A 🗳️
Explanation: Only visible for TestValid members. You can sign-up / login (it's free).
Select the answer that correctly completes the sentence.
Correct Answer:
Explanation:
"When evaluating the performance of a model, the confusion matrix displays the predicted and actual positives and negatives by using a grid of 0 and 1 values." According to the Microsoft Azure AI Fundamentals (AI-900) official study materials and Microsoft Learn module "Identify features of common machine learning types", a confusion matrix is a tool used to evaluate the performance of classification models. It visually summarizes how many predictions were correctly or incorrectly classified by comparing the predicted labels to the actual (true) labels.
A confusion matrix is a table, typically 2×2 for binary classification, with the following components:
* True Positives (TP): The model correctly predicted the positive class.
* True Negatives (TN): The model correctly predicted the negative class.
* False Positives (FP): The model incorrectly predicted the positive class.
* False Negatives (FN): The model incorrectly predicted the negative class.
The confusion matrix allows data scientists and analysts to derive important performance metrics such as accuracy, precision, recall, and F1-score, which together provide a more complete understanding of how well a model performs beyond a single number.
In Microsoft Learn's AI-900 curriculum, the confusion matrix is highlighted as a key visualization tool that
"compares actual values to predicted values to evaluate classification performance." The grid format (using 0s and 1s for predicted classes) helps identify where misclassifications occur.
By contrast:
* AUC metric (Area Under Curve) and ROC curve evaluate model discrimination ability.
* Threshold defines decision cutoffs but doesn't display classifications.
Therefore, based on the official Microsoft AI-900 study guide and Microsoft Learn resources, the correct answer is Confusion Matrix, as it provides a grid view comparing actual versus predicted values in classification models.
Select the answer that correctly completes the sentence.
Correct Answer:
Explanation:
In Microsoft's Responsible AI framework, the Reliability and Safety principle ensures that AI systems perform consistently, safely, and as intended across diverse conditions - even when faced with incomplete, unusual, or unexpected data. Correctly handling unusual or missing values in a dataset directly demonstrates this principle, as it helps prevent faulty predictions, biased results, or unsafe system behaviors.
According to the Microsoft Learn Responsible AI module (from the AI-900 and AI-102 study paths), a reliable AI model should maintain its performance when encountering data anomalies. This includes validating inputs, managing missing or extreme values, and testing models to ensure they behave as expected in real-world scenarios. Such practices make AI systems robust and trustworthy, which aligns exactly with the Reliability and Safety principle.
The other Responsible AI principles address different concerns:
* Inclusiveness: Ensures AI empowers and serves all users equitably.
* Privacy and Security: Focuses on safeguarding personal data and preventing unauthorized access.
* Transparency: Ensures that AI decisions are understandable and explainable to users.
While all principles are essential, managing data integrity and system stability-including how a model responds to missing or anomalous values-is primarily a matter of reliability and safety. It ensures the AI behaves predictably and minimizes risks of errors or unintended harm.
Therefore, the correct completion of the sentence is:
"Correctly handling unusual or missing values is an example of the application of the Reliability and Safety principle for Responsible AI."
You use drones to identify where weeds grow between rows of crops to send an Instruction for the removal of the weeds. This is an example of which type of computer vision?
- A. optical character recognition (OCR)
- B. object detection
- C. scene segmentation
Correct Answer: B 🗳️
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