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:
Box 1: Reliability and safety
To build trust, it ' s critical that AI systems operate reliably, safely, and consistently under normal circumstances and in unexpected conditions. These systems should be able to operate as they were originally designed, respond safely to unanticipated conditions, and resist harmful manipulation.
Box 2: accountability
Box 3: Privacy and security
As AI becomes more prevalent, protecting privacy and securing important personal and business information is becoming more critical and complex. With AI, privacy and data security issues require especially close attention because access to data is essential for AI systems to make accurate and informed predictions and decisions about people. AI systems must comply with privacy laws that require transparency about the collection, use, and storage of data and mandate that consumers have appropriate controls to choose how their data is used
https://docs.microsoft.com/en-us/learn/modules/responsible-ai-principles/4-guiding-principles

The correct answers are A. Grounding data can be used to constrain the output of the chatbot and C. The chatbot might respond with inaccurate data.
According to the Microsoft Azure AI Fundamentals (AI-900) study material and Microsoft Learn modules on Azure OpenAI, a chatbot built with Azure OpenAI GPT-3.5 is a large language model (LLM) capable of generating natural language responses. However, these models operate based on statistical patterns learned from massive text datasets-they do not inherently guarantee factual accuracy. Hence, while GPT-based models can produce highly coherent text, they may sometimes generate inaccurate, outdated, or fabricated information (commonly referred to as "hallucinations"). This makes C correct.
Grounding data, as described in Microsoft's Responsible AI and Azure OpenAI grounding documentation, refers to integrating trusted external data sources-such as company documents, databases, or knowledge bases-into the prompt context. This helps the model stay aligned with factual or domain-specific content, effectively constraining its output to be relevant and verifiable. Therefore, A is also correct.
Options B and D are incorrect because GPT models do not always provide accurate information, and they are not approved for critical use cases such as medical diagnosis. Microsoft's Responsible AI principles explicitly prohibit unverified use in healthcare or other high-risk domains.
Thus, the verified answers are A and C.

According to the Microsoft Azure AI Fundamentals (AI-900) official study guide and the Microsoft Learn module "Identify features of speech capabilities in Azure Cognitive Services", the Azure Speech service provides functionality for converting text to spoken words (speech synthesis) and speech to text (speech recognition).
In this scenario, the app must read recipe instructions aloud to assist users with visual impairments. This task is achieved through speech synthesis, also known as text-to-speech (TTS). The Azure Speech service uses advanced neural network models to generate natural-sounding voices in many languages and accents, making it ideal for accessibility scenarios such as screen readers, virtual assistants, and educational tools.
Microsoft Learn defines Speech service as a unified offering that includes:
* Speech-to-text (speech recognition): Converts spoken words into text.
* Text-to-speech (speech synthesis): Converts written text into natural-sounding audio output.
* Speech translation: Translates spoken language into another language in real time.
* Speaker recognition: Identifies or verifies a person based on their voice.
The other options do not fit the requirements:
* A. Text Analytics - Performs text-based natural language analysis such as sentiment, key phrase extraction, and entity recognition, but it cannot produce audio output.
* B. Translator Text - Translates text between languages but does not generate speech output.
* D. Language Understanding (LUIS) - Interprets user intent from text or speech for conversational bots but does not read text aloud.
Therefore, based on the AI-900 curriculum and Microsoft Learn documentation, the correct service for converting recipe text to spoken audio is the Azure Speech service.
# Final answer: C. Speech
Reference:Microsoft Learn - "What is the Speech service?" (Azure Cognitive Services > Speech)

In a chat solution using the Azure OpenAI GPT-3.5 model, the temperature parameter controls the creativity and variability of generated responses. According to the Microsoft Learn documentation for Azure OpenAI Service, temperature is a float value typically between 0 and 2, determining how deterministic or random the model's output is. A lower temperature (e.g., 0-0.3) makes responses more focused and deterministic, while a higher temperature (e.g., 0.8-1.2) produces more verbose, creative, and diverse responses.
When you want the chat model to generate more detailed or expressive output, increasing the temperature encourages the model to explore a broader range of possible continuations, leading to longer and more varied text. This parameter directly affects how "verbose" or elaborate the model's responses can be, which is why it is the correct answer.
The other options are not appropriate for this scenario:
* A. Presence penalty reduces repetition by discouraging reuse of the same phrases but does not control verbosity.
* C. Stop sequence defines tokens where generation should stop, limiting rather than extending response length.
* D. Max response (max tokens) controls the maximum length of the response but does not inherently make answers more verbose or expressive.
Thus, to encourage more elaborate and detailed output from the Azure OpenAI GPT-3.5 model, the correct configuration parameter to adjust is Temperature (B).