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AI-900認證考試是任何希望在AI和ML領域建立職業生涯的人的優秀起點。它適用於對雲計算有基本理解並希望擴展AI概念及其實際應用知識的個人。通過這個考試是向潛在雇主和同事展示您在AI和ML方面專業知識的絕佳方式。此外，它還作為驗證在特定的AI和ML領域中具備專業知識的高級認證的先決條件。
Microsoft AI-900 認證考試是希望在人工智慧和機器學習方面建立職業生涯的個人的理想起點。這個認證考試旨在測試你對人工智慧和機器學習概念的基礎知識以及它們如何應用於Azure平台的了解程度。通過AI-900考試，你將更好地了解如何使用人工智慧和機器學習來解決現實世界的問題，並能夠使用Azure人工智慧服務和工具構建智能應用程式。
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微軟AI-900 (微軟Azure AI基礎) 考試是為了測試考生在人工智慧和機器學習領域的基礎知識。該考試適用於希望在微軟Azure環境中驗證AI和應用的基礎知識的個人。該考試適用於希望獲得有關AI和機器學習的知識和技能的技術和非技術專業人員。
問題 #164
Providing contextual information to improve the responses quality of a generative Al solution is an example of which prompt engineering technique?
答案：C
解題說明：
In Microsoft Azure OpenAI Service and the AI-900/AI-102 study materials, grounding data is the correct term used to describe the process of providing contextual or external information to improve the accuracy, relevance, and quality of responses generated by a generative AI model such as GPT-3.5 or GPT-4.
Grounding is a prompt engineering technique where the AI model is supplemented with relevant background data, such as company documents, knowledge bases, or user context, that helps the model generate factually correct and context-aware responses. Microsoft Learn defines grounding as a way to connect the model's general knowledge to specific, real-world information. For example, if you ask a GPT-3.5 model about your organization's HR policies, the base model will not know them unless that policy information is provided (grounded) in the prompt. By embedding this contextual data, the AI becomes "grounded" in the facts it needs to respond reliably.
This technique differs from other prompt engineering concepts:
* A. Providing examples (few-shot prompting) shows the model sample inputs and outputs to guide formatting or style, not factual context.
* B. Fine-tuning involves retraining the model with labeled data to permanently adjust its behavior - it's not a prompt-based technique.
* D. System messages define the model's role, tone, or style (for example, "You are a helpful assistant") but do not add factual context.
Therefore, when you provide contextual information (like product details, policy documents, or reference text) within a prompt to enhance the quality and factual reliability of the model's responses, you are applying the grounding data technique.
問題 #165
Select the answer that correctly completes the sentence.
答案：
解題說明：
Explanation:
According to the Microsoft Azure AI Fundamentals (AI-900) Official Study Guide and the Microsoft Learn module "Explore computer vision in Microsoft Azure," computer vision is a field of artificial intelligence that enables computers to interpret and understand visual information from the world - such as images or videos.
In this scenario, the task is to count the number of animals in an area based on a video feed. This requires the system to:
* Detect the presence of animals in each frame of the video (object detection).
* Track and count them across multiple frames as they move.
These are classic computer vision tasks, as they involve analyzing visual inputs (video or image data) and identifying objects (in this case, animals). Azure provides services such as Azure Computer Vision, Custom Vision, and Video Indexer, which can perform object detection, counting, and activity recognition using AI models trained on visual datasets.
Why the other options are incorrect:
* Forecasting: Involves predicting future values based on historical data (e.g., predicting sales or weather), not analyzing video feeds.
* Knowledge mining: Focuses on extracting insights from large text-based document repositories, not images or videos.
* Anomaly detection: Identifies unusual patterns in numeric or time-series data, not visual objects.
Therefore, identifying and counting animals in video footage falls under computer vision, since it uses AI to visually detect, classify, and quantify objects in real-time or recorded feeds.
問題 #166
Select the answer that correctly completes the sentence.
答案：
解題說明：
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.
問題 #167
brectly completes the sentence.
答案：
解題說明：
Explanation:
問題 #168
For each of the following statements, select Yes if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.
答案：
解題說明：
Explanation:
The Translator service, part of Microsoft Azure Cognitive Services, is designed specifically for text translation between multiple languages. It is a cloud-based neural machine translation service that supports more than 100 languages. According to Microsoft Learn's module "Translate text with the Translator service", this service provides two main capabilities: text translation and automatic language detection.
* "You can use the Translator service to translate text between languages." # YesThis statement is true.
The primary purpose of the Translator service is to translate text accurately and efficiently between supported languages, such as English to Spanish or French to Japanese. It maintains contextual meaning using neural machine translation models.
* "You can use the Translator service to detect the language of a given text." # YesThis statement is also true. The Translator service includes automatic language detection, which determines the source language before translation. For instance, if a user submits text in an unknown language, the service can identify it automatically before performing translation.
* "You can use the Translator service to transcribe audible speech into text." # NoThis statement is false.
Transcribing speech (audio) into text is a function of the Azure Speech service, specifically the Speech- to-Text API, not the Translator service.
Therefore, the Translator service is used for text translation and language detection, while speech transcription belongs to the Speech service.
問題 #169
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