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| Section | Weight | Objectives |
|---|---|---|
| Features of computer vision workloads on Azure | 15–20% | - Identify types of computer vision solutions - Describe capabilities of Azure Face - Describe capabilities of Azure Custom Vision - Describe capabilities of Azure Form Recognizer - Describe capabilities of Azure Computer Vision |
| Features of Natural Language Processing (NLP) workloads on Azure | 15–20% | - Describe capabilities of Azure Speech - Identify types of NLP solutions - Describe capabilities of Azure Translator - Describe capabilities of Azure Language |
| Features of generative AI workloads on Azure | 20–25% | - Describe responsible AI practices for generative AI - Describe generative AI concepts - Describe capabilities of Azure OpenAI Service - Describe use cases for generative AI |
| Fundamental principles of machine learning on Azure | 15–20% | - Describe automated machine learning - Describe capabilities of Azure Machine Learning - Describe machine learning pipelines - Describe core concepts of machine learning |
| Artificial Intelligence workloads and considerations | 15–20% | - Describe responsible AI principles - Describe considerations for developing AI solutions - Identify types of AI workloads |
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209. Frage
You have a chatbot that answers technical questions by using the Azure OpenAI GPT-3.5 large language model (LLM). Which two statements accurately describe the chatbot? Each correct answer presents a complete solution.
NOTE: Each correct answer is worth one point.
Antwort: A,B
Begründung:
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 Hence, while GPT-based models can produce highly coherent text, they may sometimes generate inaccurate, outdated, or fabricated information (commonly referred to as "hallucinations").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.
210. Frage
Match the principles of responsible AI to appropriate requirements.
To answer, drag the appropriate principles from the column on the left to its requirement on the right. Each principle may be used once, more than once, or not at all. You may need to drag the split bar between panes or scroll to view content.
NOTE: Each correct selection is worth one point.
Antwort:
Begründung:
Reference:
https://docs.microsoft.com/en-us/azure/cloud-adoption-framework/innovate/best-practices/trusted-ai
https://docs.microsoft.com/en-us/learn/modules/responsible-ai-principles/4-guiding-principles
211. Frage
For each of the following statements, select Yes if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.
Antwort:
Begründung:
Explanation:
The correct answers are based on the Microsoft Azure AI Fundamentals (AI-900) Official Study Guide and the Microsoft Learn module "Explore fundamental principles of machine learning." In supervised machine learning, data is typically divided into three main subsets:
* Training set - used to train the model, i.e., to teach the algorithm the patterns and relationships between input features and output labels.
* Validation set - used to evaluate the model during training to tune hyperparameters and prevent overfitting.
* Test set - used after training to assess the final model's performance on unseen data.
Let's analyze each statement in light of these definitions:
* "A validation set includes the set of input examples that will be used to train a model." # NoThis is incorrect because the training set, not the validation set, contains the input examples used for model training. The validation set is separate from the training data to ensure unbiased evaluation.
* "A validation set can be used to determine how well a model predicts labels." # YesThis is correct. The validation set helps assess how effectively the model generalizes during training. It measures performance and helps tune model parameters for optimal results.
* "A validation set can be used to verify that all the training data was used to train the model." # NoThis is false. The validation set is not used to verify the completeness of training data usage. It exists independently to evaluate the model's performance during training cycles.
According to Microsoft Learn, using a validation set helps ensure that a model generalizes well and avoids overfitting to the training data. It plays a crucial role in refining and optimizing models before final testing.
212. Frage
To complete the sentence, select the appropriate option in the answer area.
Antwort:
Begründung:
Explanation:
Reference:
https://azure.microsoft.com/en-gb/services/cognitive-services/speech-to-text/#features Speech recognition means Speech to Text. In the above example as a person speaks the words are converted into text of the same language. Hence Speech to Text also called Speech recognition is the right answer.
Speech recognition - the ability to detect and interpret spoken input.
Speech synthesis - the ability to generate spoken output.
https://docs.microsoft.com/en-us/learn/modules/recognize-synthesize-speech/1-introduction
213. Frage
To complete the sentence, select the appropriate option in the answer area.
Antwort:
Begründung:
Explanation:
Regression is a machine learning task that is used to predict the value of the label from a set of related features.
Reference:
https://docs.microsoft.com/en-us/dotnet/machine-learning/resources/tasks
214. Frage
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