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Average Salary of AI-900: Microsoft Azure AI Fundamentals Exam Certified Professionals
The average salary of an AI-900: Microsoft Azure AI Fundamentals Exam Outlook and Modules Exam Certified Expert in:
- Europe - 105,000 EURO
- England - 55,900 POUNDS
- United States - 155,000 USD
- India - 11,42,117 INR
NEW QUESTION # 166
You have 100 instructional videos that do NOT contain any audio. Each instructional video has a script. You need to generate a narration audio file for each video based on the script. Which type of workload should you use?
- A. speech recognition
- B. speech synthesis
- C. translation
- D. language modeling
Answer: B
Explanation:
Speech synthesis, also known as text-to-speech (TTS), is the AI workload that converts written text into spoken words. In this case, the task is to generate narration audio from provided scripts for silent instructional videos.
Speech recognition performs the opposite function - it converts speech into text. Language modeling is for text understanding and prediction (e.g., GPT). Translation converts text between languages, not from text to audio.
Therefore, the most appropriate workload, according to Microsoft's AI-900 study material under the "Speech AI capabilities" section, is speech synthesis, which enables natural voice narration generation.
NEW QUESTION # 167
To complete the sentence, select the appropriate option in the answer area.
Answer:
Explanation:
Reference:
https://azure.microsoft.com/en-in/blog/microsoft-conversational-ai-tools-enable-developers-to-build-connect-and-manage-intelligent-bots
NEW QUESTION # 168
Select the answer that correctly completes the sentence.
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NEW QUESTION # 169
Select the answer that correctly completes the sentence.
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NEW QUESTION # 170
For each of the following statements, select Yes if the statement is true. Otherwise, select No. NOTE: Each correct selection is worth one point.
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NEW QUESTION # 171
brectly completes the sentence.
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NEW QUESTION # 172
For each of the following statements, select Yes if the statement is true. Otherwise, select No. NOTE: Each correct selection is worth one point.
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NEW QUESTION # 173
For each of the following statements, select Yes if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.
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NEW QUESTION # 174
You need to identify street names based on street signs in photographs.
Which type of computer vision should you use?
- A. object detection
- B. optical character recognition (OCR)
- C. image classification
- D. facial recognition
Answer: B
Explanation:
According to the Microsoft Azure AI Fundamentals (AI-900) official study guide and Microsoft Learn module "Describe features of computer vision workloads on Azure", Optical Character Recognition (OCR) is a core computer vision workload that enables AI systems to detect and extract text from images or scanned documents.
In this scenario, the goal is to identify street names from street signs in photographs. Since the text is embedded within images, OCR is the correct technology to use. OCR works by analyzing the visual patterns of letters, numbers, and symbols, then converting them into machine-readable text. Azure's Computer Vision API and Azure AI Vision Service provide OCR capabilities that can extract printed or handwritten text from pictures, documents, and even real-time camera feeds.
Let's analyze the other options:
* A. Object detection: Identifies and locates objects (like cars, people, or street signs) but not the text written on them.
* C. Image classification: Classifies an entire image into categories (e.g., "street scene" or "traffic sign") but doesn't extract text content.
* D. Facial recognition: Identifies or verifies people by analyzing facial features, unrelated to text extraction.
Therefore, identifying street names on street signs is a text extraction problem, making Optical Character Recognition (OCR) the most accurate and verified answer per Microsoft Learn content.
NEW QUESTION # 175
What are two tasks that can be performed by using the Computer Vision service? Each correct answer presents a complete solution.
NOTE: Each correct selection is worth one point.
- A. Recognize handwritten text.
- B. Translate the text in an image between languages.
- C. Train a custom image classification model.
- D. Detect faces in an image.
Answer: A,D
Explanation:
Section: Describe features of computer vision workloads on Azure
Explanation:
B: Azure's Computer Vision service provides developers with access to advanced algorithms that process images and return information based on the visual features you're interested in. For example, Computer Vision can determine whether an image contains adult content, find specific brands or objects, or find human faces.
C: Computer Vision includes Optical Character Recognition (OCR) capabilities. You can use the new Read API to extract printed and handwritten text from images and documents.
Reference:
https://docs.microsoft.com/en-us/azure/cognitive-services/computer-vision/home
NEW QUESTION # 176
To complete the sentence, select the appropriate option in the answer area.
Answer:
Explanation:
Explanation:
In the Microsoft Azure AI Fundamentals (AI-900) and Azure Machine Learning (AML) learning paths, deploying a real-time inference pipeline refers to making a trained machine learning model available as a web service that can process incoming data and return predictions instantly. To achieve this, the model must be deployed to an infrastructure capable of handling continuous, low-latency requests with high reliability and scalability.
Microsoft's official guidance from Azure Machine Learning documentation specifies that:
* For testing or development, you can deploy to Azure Container Instances (ACI) because it provides a lightweight, temporary environment suitable for small-scale or non-production workloads.
* For production-grade, real-time inference, the deployment should be made to Azure Kubernetes Service (AKS).
AKS provides enterprise-level scalability, load balancing, and high availability, which are critical for serving real-time predictions to multiple consumers simultaneously. It manages containerized applications using Kubernetes orchestration, allowing the model to scale automatically based on traffic demands.
Azure Machine Learning Compute is mainly used for model training and batch inference pipelines, not real- time endpoints. A local web service is typically used only for debugging or offline testing on a developer machine and cannot be shared for external consumption.
Therefore, when deploying a real-time inference pipeline as a service for others to consume, the correct and Microsoft-verified option is Azure Kubernetes Service (AKS). This environment ensures production readiness, secure endpoint management, and scalability for live AI applications, fully aligning with best practices outlined in the Azure Machine Learning designer documentation and AI-900 exam objectives.
https://docs.microsoft.com/en-us/azure/machine-learning/concept-designer#deploy
NEW QUESTION # 177
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 "Describe features of conversational AI workloads," the correct answer is Power Virtual Agents. This service is part of the Microsoft Power Platform and is specifically designed to enable users to create intelligent chatbots without writing any code.
Power Virtual Agents (PVA) provides a no-code/low-code environment where business users and developers can collaboratively design conversational experiences. It integrates built-in natural language processing (NLP) models to understand user intent and respond intelligently to text or speech inputs. The platform's interface allows chatbot creators to design dialogues visually, connect to back-end data via Power Automate, and publish bots on websites, Teams, or other communication channels.
This approach is highlighted in Microsoft Learn as an ideal solution for organizations that want to deploy conversational bots quickly without requiring specialized AI or programming expertise. PVA automatically leverages Microsoft's language understanding models, allowing it to interpret user input and map it to predefined topics or actions.
Let's analyze the other options:
* Azure Health Bot: A specialized solution for the healthcare industry that provides prebuilt medical compliance and healthcare content. It is not a general-purpose, no-code chatbot builder.
* Microsoft Bot Framework: A developer-focused SDK for building highly customized bots through code, offering maximum flexibility but not no-code functionality.
Therefore, the most appropriate choice that "can be used to build no-code apps that use built-in natural language processing models" is Power Virtual Agents - the official Microsoft no-code chatbot solution for conversational AI workloads.
NEW QUESTION # 178
Select the answer that correctly completes the sentence.
Answer:
Explanation:
Explanation:
The correct answer is Azure AI Language, which includes the Question Answering capability (previously known as QnA Maker). According to the Microsoft Azure AI Fundamentals (AI-900) study guide and Microsoft Learn documentation, the Azure AI Language service can be used to create a knowledge base from frequently asked questions (FAQ) and other structured or semi-structured text sources.
This service allows developers to build intelligent applications that can understand and respond to user questions in natural language by referencing prebuilt or custom knowledge bases. The Question Answering feature extracts pairs of questions and answers from documents, websites, or manually entered data and uses them to construct a searchable knowledge base. This knowledge base can then be integrated with Azure Bot Service or other conversational platforms to create interactive, self-service chatbots.
Here's how it works:
* Developers upload FAQ documents, URLs, or structured content.
* Azure AI Language processes the content and identifies logical question-answer pairs.
* The model stores these pairs in a knowledge base that can be queried by user input.
* When users ask questions, the model finds the best matching answer using natural language understanding techniques.
In contrast:
* Azure AI Document Intelligence (Form Recognizer) is used to extract structured data from forms and documents, not to create FAQ knowledge bases.
* Azure AI Bot Service is for managing and deploying conversational bots but does not generate knowledge bases.
* Microsoft Bot Framework SDK provides tools for building conversational logic but still requires a knowledge source like Question Answering from Azure AI Language.
Therefore, the service that can create a knowledge base from FAQ content is Azure AI Language.
NEW QUESTION # 179
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:
According to the Microsoft Azure AI Fundamentals (AI-900) official study guide and the Microsoft Learn module "Identify features of common machine learning types", there are three main types of machine learning: supervised learning, unsupervised learning, and reinforcement learning. Within supervised learning, two common approaches are regression and classification, while clustering is a primary example of unsupervised learning.
* "You train a regression model by using unlabeled data." - No.Regression models are trained with labeled data, meaning the input data includes both features (independent variables) and target labels (dependent variables) representing continuous numerical values. Examples include predicting house prices or sales forecasts. Unlabeled data (data without target output values) cannot be used to train regression models; such data is used in unsupervised learning tasks like clustering.
* "The classification technique is used to predict sequential numerical data over time." - No.
Classification is used for categorical predictions, where outputs belong to discrete classes, such as spam
/not spam or disease present/absent. Predicting sequential numerical data over time refers to time series forecasting, which is typically a regression or forecasting problem, not classification. The AI-900 syllabus clearly separates classification (categorical prediction) from regression (continuous value prediction) and time series (temporal pattern analysis).
* "Grouping items by their common characteristics is an example of clustering." - Yes.This statement is correct. Clustering is an unsupervised learning technique used to group similar data points based on their features. The AI-900 study materials describe clustering as the process of "discovering natural groupings in data without predefined labels." Common examples include customer segmentation or document grouping.
Therefore, based on Microsoft's AI-900 training objectives and definitions:
* Regression # supervised learning using labeled continuous data (No)
* Classification # categorical prediction, not sequential numeric forecasting (No)
* Clustering # grouping by similarity (Yes)
NEW QUESTION # 180
Match the types of natural languages processing workloads to the appropriate scenarios.
To answer, drag the appropriate workload type from the column on the left to its scenario on the right. Each workload type may be used once, more than once, or not at all.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Reference:
https://azure.microsoft.com/en-us/services/cognitive-services/text-analytics
NEW QUESTION # 181
Select the answer that correctly completes the sentence.
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NEW QUESTION # 182
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:
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Explanation:
The Azure OpenAI DALL-E model is a generative image model designed to create original images from textual descriptions (prompts). According to the Microsoft Learn documentation and the AI-900 study guide, DALL-E's primary function is text-to-image generation-it converts creative or descriptive text input into visually relevant imagery.
* "Generate captions for uploaded images" # NoDALL-E cannot create image captions. Captioning an image (describing what's in an uploaded image) is a vision analysis task, not an image generation task.
That functionality belongs to Azure AI Vision, which can analyze and describe images, detect objects, and generate captions automatically.
* "Reliably generate technically accurate diagrams" # NoWhile DALL-E can create visually appealing artwork or conceptual sketches, it is not designed for producing precise or technically correct diagrams, such as engineering schematics or architectural blueprints. The model's generative process emphasizes creativity and visual diversity rather than factual or geometric accuracy. Thus, it cannot be relied upon for professional technical outputs.
* "Generate decorative images to enhance learning materials" # YesThis is one of DALL-E's strongest use cases. It can generate decorative, conceptual, or illustrative images to enhance presentations, educational materials, and marketing content. It enables educators and designers to quickly produce unique visuals aligned with specific themes or topics, enhancing engagement and creativity.
NEW QUESTION # 183
To complete the sentence, select the appropriate option in the answer area.
Answer:
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NEW QUESTION # 184
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:
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According to the Microsoft Azure AI Fundamentals (AI-900) official study guide and the Microsoft Learn module "Identify features of the Computer Vision and Custom Vision services", the Custom Vision service is used to train, deploy, and improve custom image classification and object detection models using your own labeled data.
* Multilabel or Multiclass Selection - NOThe statement is false because the Multilabel or Multiclass choice applies only to image classification models, not object detection models. In image classification,
"Multiclass" means one label per image, while "Multilabel" means multiple labels per image. In contrast, object detection models identify and locate multiple objects in an image using bounding boxes; thus, this classification-type selection does not apply.
* Object Detection Locates Content in an Image - YESThis statement is true. The object detection functionality in Custom Vision is designed to both identify what objects appear in an image and determine their location through bounding box coordinates. For example, a model could detect and locate multiple products on a store shelf. Microsoft documentation describes object detection as
"identifying the presence and location of objects in an image."
* Predefined Domains - YESThis statement is true as well. When you create a new Custom Vision project, you must select a domain, which is a predefined optimization setting tailored to specific use cases such as retail, food, landmarks, or general images. These domains are designed to improve model accuracy by applying specialized transfer learning features based on the type of images you will analyze.
In summary:
* Classification type (Multilabel/Multiclass): No (only for classification models)
* Detect object location: Yes
* Choose predefined domain: Yes
NEW QUESTION # 185
Select the answer that correctly completes the sentence.
Answer:
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NEW QUESTION # 186
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Microsoft AI-900 exam is designed to evaluate an individual's understanding of AI and ML concepts, such as machine learning types, data labeling, and the creation of ML models using Azure services. AI-900 exam also covers the Azure AI services and their capabilities, including Cognitive Services, Azure Machine Learning, and Bot Services. The AI-900 exam is suitable for individuals who have a basic understanding of cloud computing and programming concepts.
Latest AI-900 Exam Dumps Microsoft Exam: https://passitsure.itcertmagic.com/Microsoft/real-AI-900-exam-prep-dumps.html