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| Certification Vendor: | Microsoft |
|---|---|
| Exam Name: | Microsoft Azure AI Fundamentals |
| Exam Number: | AI-900 |
| Certificate Validity Period: | Does not expire (Fundamentals certification) |
| Real Exam Qty: | 40-60 |
| Available Languages: | Spanish, Japanese, French, Korean, Chinese (Simplified), German, English, Portuguese (Brazil) |
| Exam Format: | Drag and drop, Multiple choice, Case studies, Multiple response |
| Exam Duration: | 85 minutes |
| Related Certifications: | Microsoft Azure Data Fundamentals Microsoft Azure AI Engineer Associate Microsoft Azure Fundamentals |
| Exam Price: | USD 99 (varies by region) |
| Passing Score: | 700/1000 |
| Recommended Training: | Microsoft Learn AI-900 Learning Path Azure AI Fundamentals Practice Modules |
| Exam Registration: | Pearson VUE Exam Registration Official Microsoft Certification Page |
| Sample Questions: | Microsoft AI-900 Sample Questions |
| Exam Way: | Online proctored exam or in-person test center |
| Pre Condition: | No formal prerequisites required; basic understanding of cloud and AI concepts recommended |
| Official Syllabus URL: | https://learn.microsoft.com/en-us/credentials/certifications/azure-ai-fundamentals/ |
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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.
The AI-900 exam covers a wide range of topics related to AI and ML, including the basics of data science, machine learning, natural language processing, computer vision, and more. Candidates will be tested on their understanding of various Azure AI services, such as Azure Cognitive Services, Azure Machine Learning, and Azure Bot Service, and their ability to implement these services to develop intelligent solutions.
NEW QUESTION # 94
You need to create a customer support solution to help customers access information. The solution must support email, phone, and live chat channels. Which type of Al solution should you use?
Answer: D
Explanation:
According to the Microsoft Azure AI Fundamentals (AI-900) official study materials and Microsoft Learn module "Describe features of common AI workloads", a chatbot (also known as a conversational AI agent) is a solution designed to interact with users through natural language conversation across multiple channels such as email, phone, webchat, and messaging apps.
Chatbots use Natural Language Processing (NLP) to interpret what users are saying, identify their intent, and provide relevant responses. In Azure, this functionality is implemented using the Azure Bot Service integrated with the Azure Cognitive Service for Language (Question Answering and Language Understanding). The study guide emphasizes that chatbots are used in customer service, information retrieval, and support automation to reduce the workload on human agents and improve response times.
The requirement in this question - supporting email, phone, and live chat channels - aligns exactly with the definition of a conversational AI chatbot, which can operate across multiple communication platforms.
Microsoft Learn clearly identifies that chatbots can be deployed to assist customers in retrieving information, answering FAQs, and escalating complex issues when necessary.
The other options are incorrect because:
* A. NLP is the underlying technology used by the chatbot but not the solution itself.
* B. Computer vision involves analyzing images or videos, which is unrelated to this scenario.
* C. Machine learning is a broader AI field and not a specific customer support solution type.
NEW QUESTION # 95
To complete the sentence, select the appropriate option in the answer area.
Answer:
Explanation:
Explanation:
According to the Microsoft Azure AI Fundamentals (AI-900) official study guide and Microsoft Learn module "Identify features of regression machine learning", regression is a type of supervised machine learning used when the target variable (the value you want to predict) is a continuous numeric value.
In this scenario, the task is to predict how many hours of overtime a delivery person will work based on the number of orders received. Both the input (number of orders) and the output (hours of overtime) are numeric variables. Since the goal is to estimate a quantitative value rather than categorize or group data, this is a classic example of a regression problem.
Regression models analyze the relationship between variables to make numerical predictions. For example, the model might learn that each additional 20 orders increases overtime by about two hours. Common algorithms used for regression include linear regression, decision tree regression, and boosted regression models. These models produce outputs such as "expected overtime = 5.6 hours," which are continuous numeric results.
To contrast with the other options:
* Classification is used for predicting categories or labels, such as "overtime required" vs. "no overtime," or "high-risk" vs. "low-risk." It deals with discrete outputs rather than continuous numbers.
* Clustering is an unsupervised learning approach used to group similar data points based on shared characteristics, such as grouping delivery staff by performance patterns or customer types.
As emphasized in Microsoft's Responsible AI and Machine Learning Fundamentals learning paths, regression models are ideal for numeric forecasting problems such as predicting sales, revenue, demand, or working hours.
Therefore, the correct answer is: Regression.
Reference:Microsoft Learn - Identify the types of machine learning models: Regression, Classification, and Clustering (AI-900 Learning Path)
NEW QUESTION # 96
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:
Box 1: No
Box 2: Yes
Box 3: Yes
Anomaly detection encompasses many important tasks in machine learning:
Identifying transactions that are potentially fraudulent.
Learning patterns that indicate that a network intrusion has occurred.
Finding abnormal clusters of patients.
Checking values entered into a system.
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/anomaly-detection
NEW QUESTION # 97
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:
NEW QUESTION # 98
Select the answer that correctly completes the sentence.
Answer:
Explanation:
NEW QUESTION # 99
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