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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Features of natural language processing (NLP) workloads on Azure | 30-35% | - Text analytics and language understanding
|
| Topic 2: Describe AI workloads and considerations | 20-25% | - Fundamentals of artificial intelligence concepts
|
| Topic 3: Fundamentals of machine learning on Azure | 25-30% | - Core machine learning concepts
|
| Topic 4: Features of computer vision workloads on Azure | 15-20% | - Computer vision solutions
|
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NEW QUESTION # 115
Which scenario is an example of a webchat bot?
Answer: D
Explanation:
According to the Microsoft Azure AI Fundamentals (AI-900) official study materials, a webchat bot is defined as a conversational AI application that interacts with users through a web-based chat interface. It simulates human conversation using text (and sometimes voice) to answer questions, assist with transactions, or provide information automatically. Microsoft Learn's "Describe features of common AI workloads" module highlights conversational AI as a major AI workload, where bots and virtual agents are used to provide automated, intelligent responses in real time through web, mobile, or messaging platforms.
In this scenario, the chatbot on the festival website provides immediate answers about scheduled events and ticket purchases. This aligns exactly with how a webchat bot operates - interacting with users through a website, handling repetitive inquiries, and providing consistent information without human intervention. This type of solution is commonly built using Azure Bot Service integrated with Azure Cognitive Services for Language, which allows the bot to understand user intent and respond naturally.
Let's examine the other options to reinforce why D is correct:
* A describes a text analytics or sentiment analysis scenario, not a conversational bot, because it classifies text sentiment but doesn't "chat" with a user.
* B is an example of machine translation using the Translator service, not a chatbot.
* C is an email classification or natural language processing task, not a webchat interaction.
The AI-900 exam objectives clearly distinguish conversational AI from other cognitive services such as translation or sentiment analysis. Conversational AI focuses on dialogue and interaction through natural language conversation channels like websites or messaging apps.
Therefore, the verified and officially aligned answer is D. From a website interface, answer common questions about scheduled events and ticket purchases for a music festival.
NEW QUESTION # 116
When training a model, why should you randomly split the rows into separate subsets?
Answer: C
Explanation:
The goal is to produce a trained (fitted) model that generalizes well to new, unknown data. The fitted model is evaluated using "new" examples from the held-out datasets (validation and test datasets) to estimate the model's accuracy in classifying new data.
https://en.wikipedia.org/wiki/Training,_validation,_and_test_sets#:~:text=Training%20dataset,- A%20training%20dataset&text=The%20goal%20is%20to%20produce,accuracy%20in%20classifying%20new%
NEW QUESTION # 117
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: Yes
Content Moderator is part of Microsoft Cognitive Services allowing businesses to use machine assisted moderation of text, images, and videos that augment human review.
The text moderation capability now includes a new machine-learning based text classification feature which uses a trained model to identify possible abusive, derogatory or discriminatory language such as slang, abbreviated words, offensive, and intentionally misspelled words for review.
Box 2: No
Azure's Computer Vision service gives you 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.
Box 3: Yes
Natural language processing (NLP) is used for tasks such as sentiment analysis, topic detection, language detection, key phrase extraction, and document categorization.
Sentiment Analysis is the process of determining whether a piece of writing is positive, negative or neutral.
Reference:
https://azure.microsoft.com/es-es/blog/machine-assisted-text-classification-on-content-moderator-public-preview
https://docs.microsoft.com/en-us/azure/architecture/data-guide/technology-choices/natural-language-processing
NEW QUESTION # 118
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?
Answer: B
Explanation:
Object detection is similar to tagging, but the API returns the bounding box coordinates for each tag applied.
For example, if an image contains a dog, cat and person, the Detect operation will list those objects together with their coordinates in the image.
Reference:
https://docs.microsoft.com/en-us/ai-builder/object-detection-overview
https://docs.microsoft.com/en-us/azure/cognitive-services/computer-vision/overview-ocr
https://docs.microsoft.com/en-us/azure/azure-video-analyzer/video-analyzer-for-media-docs/video-indexer-overv
NEW QUESTION # 119
Select the answer that correctly completes the sentence.
Answer:
Explanation:
Explanation:
According to the Microsoft Azure AI Fundamentals (AI-900) official study guide and Microsoft Learn module "Identify features of common machine learning types", regression is a supervised machine learning technique used to predict continuous numerical values based on one or more input features. In this scenario, the task is to predict a vehicle's miles per gallon (MPG)-a continuous numeric value-based on several measurable factors such as weight, engine power, and other specifications.
Regression models learn the mathematical relationship between input variables (independent features) and a numeric target variable (dependent outcome). Common regression algorithms include linear regression, decision tree regression, and support vector regression. In the example, the model would analyze historical data of vehicles and learn patterns that map characteristics (like engine size, horsepower, and weight) to fuel efficiency. Once trained, it can predict the MPG for a new vehicle configuration.
The other options describe different problem types:
* Classification predicts discrete categories (for example, whether a car is "fuel efficient" or "not fuel efficient"), not continuous values.
* Clustering is an unsupervised learning method that groups data points based on similarities without predefined labels, not predictive modeling.
* Anomaly detection identifies data points that significantly deviate from normal patterns, such as detecting engine sensor failures or fraudulent transactions.
Since predicting MPG involves estimating a numeric value within a continuous range, regression is the most appropriate model type.
In summary, per AI-900 training content, regression models are used when the output variable is numeric, classification for categorical outputs, and clustering for pattern discovery. Therefore, predicting miles per gallon based on vehicle features is a textbook example of a regression problem in Azure Machine Learning.
NEW QUESTION # 120
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