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| Certification Vendor: | Microsoft |
|---|---|
| Exam Name: | Microsoft Azure AI Fundamentals |
| Exam Number: | AI-900 |
| Related Certifications: | Microsoft Certified: Azure AI Fundamentals |
| Exam Format: | True/False, Drag and drop, Multiple-choice, Sequence ordering, Single-choice |
| Passing Score: | 700/1000 |
| Exam Price: | USD 99.00 |
| Certificate Validity Period: | No expiration (certification does not expire) |
| Real Exam Qty: | 40-60 |
| Exam Duration: | 45-60 |
| Available Languages: | German, English, Japanese, Portuguese (Brazil), French, Korean, Spanish, Indonesian, Chinese (Simplified) |
| 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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Microsoft AI-900 Exam is an excellent certification for individuals who want to showcase their foundational knowledge of AI and machine learning in the Microsoft Azure environment. Obtaining this certification can help professionals advance their careers and gain recognition for their expertise in AI and machine learning.
NEW QUESTION # 196
A company employs a team of customer service agents to provide telephone and email support to customers.
The company develops a webchat bot to provide automated answers to common customer queries.
Which business benefit should the company expect as a result of creating the webchat bot solution?
Answer: A
Explanation:
Full Detailed Explanation with exact Extract from your Official Study guide and Trained Data at least 250 to
300 words in Explanation:
The correct answer is B. a reduced workload for the customer service agents.
According to the Microsoft Azure AI Fundamentals (AI-900) official study guide and Microsoft Learn module "Describe features of common AI workloads", conversational AI solutions such as chatbots are primarily designed to automate repetitive and routine customer interactions. The key business value emphasized in these materials is operational efficiency-chatbots allow organizations to respond to a high volume of customer queries without relying solely on human agents. This results in reduced workload, lower operational costs, and faster response times.
Microsoft's AI-900 learning objectives highlight that AI can be applied to automate tasks that previously required human interaction. In the context of customer support, a webchat bot powered by Azure AI services (such as Azure Bot Service or Azure Cognitive Services for Language) can handle frequently asked questions like order status, password resets, or basic troubleshooting. This allows human agents to focus their time and skills on more complex issues that require empathy, reasoning, or decision-making-tasks that AI cannot yet handle as effectively.
Additionally, the AI-900 course materials explain that one of the measurable business benefits of deploying AI-driven chatbots is improved efficiency and scalability. Chatbots can handle thousands of simultaneous interactions, something that human teams cannot easily do. As a result, the organization experiences reduced operational pressure on support staff, improved customer satisfaction due to quicker responses, and optimized resource utilization.
Options A and C are incorrect because chatbots do not directly influence sales growth or product reliability.
While increased customer satisfaction might indirectly support sales, it is not the primary or guaranteed outcome of implementing a chatbot. Similarly, product reliability is tied to engineering quality, not customer service automation.
Therefore, based on the official AI-900 study materials and Microsoft Learn concepts, the best and verified answer is B. a reduced workload for the customer service agents.
NEW QUESTION # 197
You plan to deploy an Azure Machine Learning model as a service that will be used by client applications.
Which three processes should you perform in sequence before you deploy the model? To answer, move the appropriate processes from the list of processes to the answer area and arrange them in the correct order.
Answer:
Explanation:
Explanation:
The correct order of processes before deploying a model as a service is:
(1) Data preparation # (2) Model training # (3) Model evaluation.
According to the Microsoft Azure AI Fundamentals (AI-900) official study guide and Microsoft Learn module "Explore the machine learning process", machine learning follows a structured lifecycle that involves several sequential stages. Before a model can be deployed, the data must be properly prepared, the model must be trained, and then its performance must be evaluated to ensure accuracy and reliability.
* Data Preparation:The first stage involves collecting, cleaning, and transforming raw data into a usable format. Azure Machine Learning provides tools like Data Wrangler, Data Labeling, and Data Transformation pipelines to ensure the dataset is accurate and consistent. As per Microsoft Learn, "data preparation is essential to remove noise, handle missing values, and split the dataset into training and testing sets." This step ensures the model learns from quality input.
* Model Training:In this step, algorithms are applied to the prepared training data to create a predictive model. The system learns patterns and relationships from the data. Azure Machine Learning allows model training using AutoML, custom code, or designer pipelines. The training process produces a model that can make predictions, but it still needs to be tested before deployment.
* Model Evaluation:Once trained, the model's performance is tested against unseen (test) data.
Evaluation metrics like accuracy, precision, recall, and F1-score are analyzed to verify if the model meets business and performance requirements. Microsoft Learn defines this stage as "assessing the model's performance to determine its readiness for deployment." After these three processes, the model can then be deployed as a web service using Azure Machine Learning endpoints. Model retraining happens later when new data becomes available, and data encryption is a security process, not part of model development steps.
NEW QUESTION # 198
To complete the sentence, select the appropriate option in the answer area.
Answer:
Explanation:
Explanation
To perform real-time inferencing, you must deploy a pipeline as a real-time endpoint.
Real-time endpoints must be deployed to an Azure Kubernetes Service cluster.
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/concept-designer#deploy
NEW QUESTION # 199
You need to create a model that labels a collection of your personal digital photographs.
Which Azure Al service should you use?
Answer: A
Explanation:
According to the Microsoft Azure AI Fundamentals (AI-900) official study materials and Microsoft Learn module "Describe features of Computer Vision workloads on Azure", the Azure AI Custom Vision service allows users to build, train, and deploy custom image classification or object detection models. It is specifically designed for scenarios where you need a model tailored to your unique image dataset - in this case, personal digital photographs.
Custom Vision lets you upload and label your own images (for example, "family," "friends," "vacations," or
"pets") and then train a model that learns to recognize those categories. The system automatically extracts relevant features from the training images and creates a model capable of classifying new images into the predefined labels. You can iteratively refine your model by adding more images or re-training to improve accuracy.
The other options do not fit this requirement:
* A. Azure AI Language deals with text-based tasks such as language understanding, sentiment analysis, and key phrase extraction - not image processing.
* B. Azure AI Computer Vision provides prebuilt image analysis models (e.g., object detection, tag generation, scene description), but it cannot learn custom categories unique to your dataset. It's great for general image recognition but not for specialized labeling tasks.
* C. Azure AI Document Intelligence (Form Recognizer) is used to extract information from structured or semi-structured documents such as forms, invoices, or receipts - not photographs.
Therefore, when you need to label or categorize personal photos with custom-defined tags, the right service is Azure AI Custom Vision, because it allows you to build a model trained specifically on your own collection of images.
NEW QUESTION # 200
You have a database that contains a list of employees and their photos.
You are tagging new photos of the employees.
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:
Reference:
https://docs.microsoft.com/en-us/azure/cognitive-services/face/overview
https://docs.microsoft.com/en-us/azure/cognitive-services/face/concepts/face-detection
NEW QUESTION # 201
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