Microsoft Azure AI Fundamentals (AI-900日本語版) - AI-900日本語 FREE EXAM DUMPS QUESTIONS & ANSWERS

Explanation:
Statements
Yes
No
A webchat bot can interact with users visiting a website.
Yes
Automatically generating captions for pre-recorded videos is an example of natural language processing.
No
A smart device in the home that responds to questions such as "What will the weather be like today?" is an example of natural language processing.
Yes
According to the Microsoft Azure AI Fundamentals (AI-900) official study materials and Microsoft Learn modules on AI workloads, each of these statements maps to a distinct area of artificial intelligence - namely Conversational AI, Speech AI, and Natural Language Processing (NLP).
* "A webchat bot can interact with users visiting a website." - YesThis is true. A webchat bot represents an example of Conversational AI. It leverages natural language understanding (NLU) to interpret user input and generate appropriate responses. These bots can be created using Azure services such as Azure AI Bot Service and Language Understanding (LUIS). They enable automated interactions with users through text-based communication on websites, applications, or messaging platforms.
* "Automatically generating captions for pre-recorded videos is an example of natural language processing." - NoThis is false. Generating captions from audio involves speech recognition, not NLP.
Specifically, it uses speech-to-text technology to transcribe spoken words into written text. This function is typically performed by Azure's Speech service, which is part of the Speech AI workload, not the language-processing workload.
* "A smart device in the home that responds to questions such as 'What will the weather be like today?' is an example of natural language processing." - YesThis is true. Smart assistants like Alexa or Cortana use NLP to interpret spoken queries, extract meaning, and generate appropriate responses. NLP allows these devices to understand human language, retrieve relevant information, and respond conversationally.

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Explanation:

This question assesses knowledge of the Azure Cognitive Services Speech and Text Analytics capabilities, as described in the Microsoft Azure AI Fundamentals (AI-900) official study guide and Microsoft Learn modules "Explore natural language processing" and "Explore speech capabilities." These services are part of Azure Cognitive Services, which provide prebuilt AI capabilities for speech, language, and text understanding.
* You can use the Speech service to transcribe a call to text # YesThe Speech-to-Text feature in the Azure Speech service automatically converts spoken words into written text. Microsoft Learn explains:
"The Speech-to-Text capability enables applications to transcribe spoken audio to text in real time or from recorded files." This makes it ideal for call transcription, voice assistants, and meeting captioning.
* You can use the Text Analytics service to extract key entities from a call transcript # YesOnce a call has been transcribed into text, the Text Analytics service (part of Azure Cognitive Services for Language) can process that text to extract key entities, key phrases, and sentiment. For example, it can identify names, organizations, locations, and product mentions. Microsoft Learn notes: "Text Analytics can extract key phrases and named entities from text to derive insights and structure from unstructured data."
* You can use the Speech service to translate the audio of a call to a different language # YesThe Azure Speech service also includes Speech Translation, which can translate spoken language in real time. It converts audio input from one language into translated text or speech output in another language.
Microsoft Learn describes this as: "Speech Translation combines speech recognition and translation to translate spoken audio to another language."

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Explanation:

According to the Microsoft Azure AI Fundamentals (AI-900) official study guide and the Microsoft Learn module "Identify features of common AI workloads", OCR (Optical Character Recognition) is a Computer Vision technology that detects and extracts printed or handwritten text from images and scanned documents.
OCR allows organizations and individuals to convert physical or image-based text into machine-readable, editable, and searchable digital text.
In the context of this question, a historian working with old newspaper articles or archival documents would use OCR to digitize printed content. For instance, the historian can scan or photograph old newspaper pages, and then use an OCR tool-such as Azure Computer Vision's OCR API-to automatically recognize and extract the textual content from those images. This process enables the historian to store, edit, and analyze the content digitally without manually typing everything.
OCR works by using deep learning algorithms trained on thousands of text samples. The system analyzes patterns, shapes, and spatial relationships of characters to identify text accurately, even from low-quality or aged paper documents. Once extracted, the digital text can be indexed, translated, or processed further using Natural Language Processing (NLP) tools for content analysis.
Now, addressing the other options:
* Facial analysis is used to detect emotions, age, or gender from human faces-irrelevant to text digitization.
* Image classification identifies entire images by categories (e.g., cat, car, flower).
* Object detection identifies and locates multiple objects within an image but doesn't extract text.
Therefore, per the AI-900 learning objectives under the Computer Vision workload, the correct and verified completion is:

Explanation:

This question evaluates understanding of fundamental machine learning concepts as covered in the Microsoft Azure AI Fundamentals (AI-900) official study guide and Microsoft Learn module "Explore the machine learning process." These statements relate to data labeling, model evaluation practices, and performance metrics-three essential parts of building and assessing a machine learning model.
* Labelling is the process of tagging training data with known values # YesAccording to Microsoft Learn,
"Labeling is the process of tagging data with the correct output value so the model can learn relationships between inputs and outputs." This is essential for supervised learning, where models require historical data with known outcomes. For example, if training a model to recognize fruit images, each image is labeled as "apple," "banana," or "orange." Hence, this statement is true.
* You should evaluate a model by using the same data used to train the model # NoThe AI-900 guide stresses that using the same data for both training and evaluation can cause overfitting, where the model performs well on training data but poorly on unseen data. Instead, the dataset is split into training and testing (or validation) subsets. Evaluation must use test data that the model has never seen before to ensure an unbiased measure of performance. Therefore, this statement is false.
* Accuracy is always the primary metric used to measure a model's performance # NoMicrosoft Learn emphasizes that accuracy is only one metric and not always the best choice. Depending on the problem type, other metrics such as precision, recall, F1-score, or AUC (Area Under the Curve) may be more appropriate-especially in cases with imbalanced datasets. For example, in fraud detection, recall may be more important than accuracy. Thus, this statement is false.

Explanation:

According to the Microsoft Azure AI Fundamentals (AI-900) official study guide and the Microsoft Learn module "Explore fundamental principles of machine learning", feature engineering is the process used to generate additional features or transform existing data into forms that improve model performance. Features are individual measurable properties or characteristics used as input for machine learning algorithms. The goal of feature engineering is to create new informative variables that better represent the underlying patterns in the data.
Feature engineering may include tasks such as:
* Combining or transforming raw data columns (e.g., creating a "total purchase amount" from price × quantity).
* Extracting time-based components (e.g., year, month, day, hour) from datetime values.
* Encoding categorical variables (e.g., one-hot encoding or label encoding).
* Scaling or normalizing numerical features.
* Creating polynomial or interaction terms to capture complex relationships.
Microsoft's AI-900 learning material emphasizes that the process of preparing data for machine learning involves data cleaning, feature engineering, and feature selection. While feature selection is about choosing the most relevant features from the existing dataset, feature engineering focuses on creating or generating new features to enhance model accuracy and generalization.
The other options do not fit this definition:
* Feature selection is about removing redundant or irrelevant features, not generating new ones.
* Model evaluation involves assessing the model's performance using metrics like accuracy or F1 score.
* Model training is the phase where the algorithm learns patterns from the data, not when features are created.
Therefore, based on the AI-900 official concepts and Microsoft's documentation, the correct answer is Feature engineering, as it is the process specifically used to generate additional features that improve machine learning model performance and predictive capability.

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Explanation:
Statement
Yes / No
Providing an explanation of the outcome of a credit loan application is an example of the Microsoft transparency principle for responsible AI.
Yes
A triage bot that prioritizes insurance claims based on injuries is an example of the Microsoft reliability and safety principle for responsible AI.
Yes
An AI solution that is offered at different prices for different sales territories is an example of the Microsoft inclusiveness principle for responsible AI.
No
This question is based on the Responsible AI principles defined by Microsoft, which are part of the AI-900 Microsoft Azure AI Fundamentals curriculum. Microsoft's Responsible AI framework consists of six key principles: Fairness, Reliability and Safety, Privacy and Security, Inclusiveness, Transparency, and Accountability. Each principle ensures that AI systems are developed and used in a way that benefits people and society responsibly.
* Transparency Principle - YesProviding an explanation for a loan decision aligns with the Transparency principle. Microsoft defines transparency as helping users and stakeholders understand how AI systems make decisions. For example, when a credit scoring AI model approves or denies a loan, explaining the factors that influenced that outcome (such as credit history or income level) ensures that customers understand the reasoning process. This builds trust and supports responsible deployment.
* Reliability and Safety Principle - YesA triage bot that prioritizes insurance claims based on injury severity relates directly to Reliability and Safety. This principle ensures AI systems operate consistently, perform accurately, and produce dependable outcomes. In the case of the triage bot, it must reliably assess the input data (injury descriptions) and rank claims appropriately to avoid harm or misjudgment, aligning with Microsoft's emphasis on designing AI systems that are safe and robust.
* Inclusiveness Principle - NoAn AI solution priced differently across sales territories is not related to Inclusiveness. Inclusiveness focuses on ensuring accessibility and eliminating bias or exclusion for all users-especially those with disabilities or underrepresented groups. Pricing strategy is a business decision, not an inclusiveness issue. Therefore, this statement is No.
In summary, based on the AI-900 Responsible AI principles, the correct selections are: