Microsoft Azure AI Fundamentals (AI-900 Korean Version) : AI-900 Korean valid dump torrent

Correct Answer:

Explanation:

Box1: Sentiment analysis
Sentiment Analysis is the process of determining whether a piece of writing is positive, negative or neutral.
Box 2: Broad entity extraction
Broad entity extraction: Identify important concepts in text, including key Key phrase extraction/ Broad entity extraction: Identify important concepts in text, including key phrases and named entities such as people, places, and organizations.
Box 3: Entity Recognition
Named Entity Recognition: Identify and categorize entities in your text as people, places, organizations, date
/time, quantities, percentages, currencies, and more. Well-known entities are also recognized and linked to more information on the web.
Reference:
https://docs.microsoft.com/en-us/azure/architecture/data-guide/technology-choices/natural-language-processing
https://azure.microsoft.com/en-us/services/cognitive-services/text-analytics

Correct Answer:

Explanation:

The Azure Text Analytics service, a component of Azure Cognitive Services, provides natural language processing (NLP) capabilities to analyze and understand text-based data. According to the Microsoft Azure AI Fundamentals (AI-900) official study guide and Microsoft Learn module "Identify features and uses for Natural Language Processing (NLP)", the Text Analytics service supports multiple text understanding tasks, such as language detection, key phrase extraction, sentiment analysis, and entity recognition.
* Language Identification - Yes:Text Analytics can automatically detect the language in which text is written. This feature analyzes linguistic patterns and assigns a language code (for example, "en" for English, "es" for Spanish). It is one of the primary features described in Microsoft Learn as part of the service's Language Detection API.
* Detect Handwritten Signatures - No:Detecting handwritten signatures is not a text-based NLP task.
Instead, it belongs to the computer vision domain, specifically Optical Character Recognition (OCR).
The Text Analytics service only processes digital text, not handwritten or image-based data. To detect handwriting or signatures, you would use the Computer Vision OCR API, not Text Analytics.
* Entity Recognition - Yes:The Text Analytics service can identify named entities-such as people, locations, organizations, dates, and quantities-within documents. This is known as Named Entity Recognition (NER), which helps extract structured information from unstructured text.

Correct Answer:

Explanation:

The Azure AI Language Service includes several natural language processing features, such as question answering, language understanding, entity recognition, sentiment analysis, and more. Each feature serves a distinct purpose, and understanding their differences is key to selecting the correct AI workload.
* "You can use Azure AI Language Service ' s question answering to query an Azure SQL database." - NOThe question answering feature is designed to retrieve answers from text-based knowledge sources (for example, FAQs, documents, or website content). It cannot directly query a database such as Azure SQL. Querying databases requires Azure Cognitive Search, Azure OpenAI, or custom integration using application logic, not the question answering model.
* "You should use Azure AI Language Service ' s question answering when you want a knowledge base to provide the same answer to different users who submit similar questions." - YESThis is the primary use case of question answering. It allows developers to build a knowledge base (KB) of predefined question-answer pairs or extract answers from documents. When users submit semantically similar questions (e.g., "What are your office hours?" or "When are you open?"), the service returns the same consistent answer.
* "Azure AI Language Service ' s question answering can determine the intent of a user utterance." - NODetermining user intent is part of the Language Understanding (LUIS) capability, not question answering. LUIS models map natural language inputs to intents and entities, typically used in bots or applications that execute tasks (like booking a meeting or checking weather).
Hence, correct answers are: No, Yes, No - aligning with the AI-900 official study guide and Microsoft Learn module "Identify Azure AI Language capabilities."

Correct Answer:

Explanation:

"When evaluating the performance of a model, the confusion matrix displays the predicted and actual positives and negatives by using a grid of 0 and 1 values." According to the Microsoft Azure AI Fundamentals (AI-900) official study materials and Microsoft Learn module "Identify features of common machine learning types", a confusion matrix is a tool used to evaluate the performance of classification models. It visually summarizes how many predictions were correctly or incorrectly classified by comparing the predicted labels to the actual (true) labels.
A confusion matrix is a table, typically 2×2 for binary classification, with the following components:
* True Positives (TP): The model correctly predicted the positive class.
* True Negatives (TN): The model correctly predicted the negative class.
* False Positives (FP): The model incorrectly predicted the positive class.
* False Negatives (FN): The model incorrectly predicted the negative class.
The confusion matrix allows data scientists and analysts to derive important performance metrics such as accuracy, precision, recall, and F1-score, which together provide a more complete understanding of how well a model performs beyond a single number.
In Microsoft Learn's AI-900 curriculum, the confusion matrix is highlighted as a key visualization tool that
"compares actual values to predicted values to evaluate classification performance." The grid format (using 0s and 1s for predicted classes) helps identify where misclassifications occur.
By contrast:
* AUC metric (Area Under Curve) and ROC curve evaluate model discrimination ability.
* Threshold defines decision cutoffs but doesn't display classifications.
Therefore, based on the official Microsoft AI-900 study guide and Microsoft Learn resources, the correct answer is Confusion Matrix, as it provides a grid view comparing actual versus predicted values in classification models.

Correct Answer:

Explanation:

In the context of Microsoft Azure AI Fundamentals (AI-900) and general machine learning principles, regression refers to a type of supervised learning used to predict continuous numerical values based on historical data. The goal of regression is to model the relationship between input variables (features) and a continuous output variable (target).
In this scenario, the task is to predict how many vehicles will travel across a bridge on a given day. The number of vehicles is a numerical value that can vary continuously depending on factors such as time of day, weather, weekday/weekend, or traffic trends. Because the output is numeric and not categorical, this problem type clearly fits into regression analysis.
Microsoft's official learning content for AI-900, under "Identify features of regression and classification machine learning models," specifies that regression models are used to predict values such as sales forecasts, demand estimation, temperature prediction, or traffic volume-all of which share the same underlying objective: predicting a quantity.
To clarify other options:
* Classification is used when predicting categories or discrete classes, such as determining whether an email is spam or not spam, or if an image contains a cat or a dog.
* Clustering is an unsupervised learning technique used to group similar data points without predefined labels (for example, grouping customers by purchasing behavior).
Since predicting the number of vehicles results in a continuous numerical output, it aligns precisely with the regression workload type described in the Microsoft AI-900 study materials.