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Important facts you need to know about AI-900: Microsoft Azure AI Fundamentals Exam
Preparing for the exam will help you screen out questions that are irrelevant for this certification. Ingestion of the data is the most important role in AI. Ready to use the data as soon as possible. Anomaly detection refers to all situations where something out of the ordinary is happening. Accountability models are used for the accuracy rate. Transparency refers to using the data for this service. Interoperability is used on the platform. Microsoft AI-900 exam dumps in order to get the best scores with the Microsoft Azure AI Fundamentals Exam. Tech terms used in AZ-900:Microsoft Azure AI Fundamentals Exam. Serviceidentify is used in the process of AI. Files are used to store the data. The AI application is the product of the AI. Learning is used for this purpose to provide better accuracy rate.
Concepts of the AI are explained in the Microsoft AI-900 exam. Image classification is used as the labeling. Recommendation engine is used as the indexing. Intelligent chatbots are used as the chatbot. Community engagement is the chatbot. Custom bot is used as the chatbot. Extracts are used for this purpose. Brainpool is the tool used to perform the extraction. Word embedding is used as the vector training. Dimensionality reduction is used for this purpose. Intelligent chatbot are used as the chatbot.
Microsoft Azure AI Fundamentals Exam Certification Details:
| Number of Questions | 40-60 |
| Sample Questions | Microsoft Azure AI Fundamentals Sample Questions |
| Schedule Exam | Pearson VUE |
| Exam Name | Microsoft Certified - Azure AI Fundamentals |
| Exam Price | $99 (USD) |
| Passing Score | 700 / 1000 |
| Books / Training | Course AI-900T00: Microsoft Azure AI Fundamentals |
| Exam Code | AI-900 |
| Duration | 60 mins |
Reference: https://docs.microsoft.com/en-us/learn/certifications/exams/ai-900
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Microsoft AI-900 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Artificial Intelligence workloads and considerations | 15–20% | - Identify types of AI workloads - Describe considerations for developing AI solutions - Describe responsible AI principles |
| Features of computer vision workloads on Azure | 15–20% | - Describe capabilities of Azure Face - Identify types of computer vision solutions - Describe capabilities of Azure Form Recognizer - Describe capabilities of Azure Computer Vision - Describe capabilities of Azure Custom Vision |
| Features of Natural Language Processing (NLP) workloads on Azure | 15–20% | - Identify types of NLP solutions - Describe capabilities of Azure Translator - Describe capabilities of Azure Speech - Describe capabilities of Azure Language |
| Features of generative AI workloads on Azure | 20–25% | - Describe generative AI concepts - Describe capabilities of Azure OpenAI Service - Describe responsible AI practices for generative AI - Describe use cases for generative AI |
| Fundamental principles of machine learning on Azure | 15–20% | - Describe capabilities of Azure Machine Learning - Describe machine learning pipelines - Describe automated machine learning - Describe core concepts of machine learning |
Microsoft Azure AI Fundamentals Sample Questions:
Select the answer that correctly completes the sentence.
Correct Answer:
Explanation:
The correct completion of the sentence "_____ is an example of speech recognition." is A voice-activated security key system.
According to the Microsoft Azure AI Fundamentals (AI-900) official study guide and Microsoft Learn module "Describe features of common AI workloads", speech recognition refers to the ability of a system or application to convert spoken language into text or actionable commands. It allows computers to interpret and respond to human speech inputs, bridging human-computer interaction through natural language.
Microsoft Learn clearly explains that speech recognition is used in applications such as voice assistants, dictation software, and voice-activated security systems, where the spoken input from a user is captured, analyzed, and translated into commands or text. For example, when a user says "Unlock door" or "Open session," the speech recognition system interprets that sound input, converts it into text or a command, and then performs the appropriate action. This is a direct implementation of speech-to-text processing combined with command execution logic.
Let's analyze the other options:
* Creating an audio commentary for a video recording is related to speech synthesis (text-to-speech), not recognition.
* Creating captions for a video recording involves speech-to-text transcription, which is a subset of speech recognition, but the question emphasizes a system that responds to voice commands, making the first option more accurate.
* Identifying key phrases in a video transcript involves natural language processing (NLP) techniques rather than speech recognition.
Therefore, the voice-activated security key system best represents the use of speech recognition technology because it interprets spoken commands and takes a corresponding action based on recognized speech patterns.
This aligns directly with the AI-900 learning objectives where speech recognition is defined as a process that enables applications to interpret and respond to human voice input.
You have a solution that analyzes social media posts to extract the mentions of city names and the city names discussed most frequently.
Which type of natural language processing (NLP) workload does the solution use?
- A. entity recognition
- B. key phrase extraction
- C. speech recognition
- D. sentiment analysis
Correct Answer: A 🗳️
To complete the sentence, select the appropriate option in the answer area.
Correct Answer:
Explanation:
Classification
According to the Microsoft Azure AI Fundamentals (AI-900) official study guide and the Microsoft Learn module "Describe features of common AI workloads", classification is a supervised machine learning technique used when the goal is to predict which category or class an item belongs to. In supervised learning, the model is trained with labeled data-data that already contains known outcomes. The system learns patterns and relationships between input features and their corresponding labels so it can predict future classifications accurately.
In the scenario provided - "A banking system that predicts whether a loan will be repaid" - the model's output is a binary decision, meaning there are two possible outcomes:
* The loan will be repaid (positive class)
* The loan will not be repaid (negative class)
This kind of problem involves predicting a discrete value (a label or category), not a continuous numeric output. Therefore, it perfectly fits the classification type of machine learning.
The AI-900 learning materials describe classification as being used in many real-world examples, including:
* Determining whether an email is spam or not spam.
* Predicting whether a customer will churn (leave) or stay.
* Detecting fraudulent transactions.
* Assessing medical test results as positive or negative.
By contrast:
* Regression predicts continuous numeric values, such as predicting house prices, temperatures, or sales revenue. It would not apply here because repayment prediction is not a numeric value but a categorical decision.
* Clustering is an unsupervised learning method that groups similar data points without predefined categories, such as segmenting customers by purchasing behavior.
Thus, based on Microsoft's Responsible AI and AI-900 study guide concepts, a banking system that predicts whether a loan will be repaid uses the Classification type of machine learning.
You are authoring a Language Understanding (LUIS) application to support a music festival.
You want users to be able to ask questions about scheduled shows, such as: "Which act is playing on the main stage?" The question "Which act is playing on the main stage?" is an example of which type of element?
- A. an intent
- B. an entity
- C. an utterance
- D. a domain
Correct Answer: C 🗳️
Explanation: Only visible for DumpTorrent members. You can sign-up / login (it's free).
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.
- A. Create a dataset from a comma-separated value (CSV) file.
- B. Use a graphical user interface (GUI) to define and run machine learning experiments from Azure Machine Learning designer.
- C. Use a graphical user interface (GUI) to run automated machine learning experiments.
- D. Create a compute instance to use as a workstation.
Correct Answer: B,C 🗳️
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