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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Features of Natural Language Processing (NLP) workloads on Azure | 15–20% | - Identify types of NLP solutions - Describe capabilities of Azure Language - Describe capabilities of Azure Translator - Describe capabilities of Azure Speech |
| Topic 2: Features of generative AI workloads on Azure | 20–25% | - Describe responsible AI practices for generative AI - Describe use cases for generative AI - Describe generative AI concepts - Describe capabilities of Azure OpenAI Service |
| Topic 3: Features of computer vision workloads on Azure | 15–20% | - Describe capabilities of Azure Custom Vision - Describe capabilities of Azure Form Recognizer - Describe capabilities of Azure Computer Vision - Describe capabilities of Azure Face - Identify types of computer vision solutions |
| Topic 4: Fundamental principles of machine learning on Azure | 15–20% | - Describe automated machine learning - Describe machine learning pipelines - Describe core concepts of machine learning - Describe capabilities of Azure Machine Learning |
| Topic 5: Artificial Intelligence workloads and considerations | 15–20% | - Identify types of AI workloads - Describe considerations for developing AI solutions - Describe responsible AI principles |
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NEW QUESTION # 51
What are three stages in a transformer model? Each correct answer presents a complete solution.
NOTE: Each correct answer is worth one point.
Answer: A,B,E
Explanation:
A transformer model is the foundational architecture behind many modern natural language processing systems such as GPT and BERT. It processes text data through multiple key stages. According to the Microsoft Azure AI Fundamentals (AI-900) curriculum and Microsoft Learn materials, the major stages of a transformer-based large language model are tokenization, embedding calculation, and next token prediction.
* Tokenization (C) - The first step converts raw text into smaller units called tokens (words, subwords, or characters). This process allows the model to handle text in a structured numerical form rather than as raw language.
* Embedding Calculation (B) - After tokenization, the tokens are mapped into high-dimensional numeric vectors, known as embeddings. These embeddings capture semantic relationships between words and phrases so that the model can understand context and meaning.
* Next Token Prediction (D) - This stage is the heart of transformer operation, where the model predicts the next likely token in a sequence based on prior tokens. Repeated next-token predictions enable text generation, summarization, or translation.
Options A (object detection) and E (anonymization) are incorrect because they relate to vision and privacy workflows, not language modeling.
NEW QUESTION # 52
To complete the sentence, select the appropriate option in the answer area.
Computer vision capabilities can be Deployed to....................
Integrate a facial recognition feature into an app.
Answer:
Explanation:
NEW QUESTION # 53
Match the types of AI workloads to the appropriate scenarios.
To answer, drag the appropriate workload type from the column on the left to its scenario on the right. Each workload type may be used once, more than once, or not at all.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Reference:
https://docs.microsoft.com/en-us/azure/architecture/data-guide/technology-choices/natural-language-processing
NEW QUESTION # 54
Which two languages can you use to write custom code for Azure Machine Learning designer? Each correct answer presents a complete solution.
NOTE; Each correct selection is worth one point.
Answer: A,D
NEW QUESTION # 55
You have an Azure Machine Learning model that uses clinical data to predict whether a patient has a disease.
You clean and transform the clinical data.
You need to ensure that the accuracy of the model can be proven.
What should you do next?
Answer: A
Explanation:
According to the Microsoft Azure AI Fundamentals (AI-900) official study guide and Microsoft Learn modules on machine learning concepts, ensuring that the accuracy of a predictive model can be proven requires data partitioning-specifically splitting the available data into training and testing datasets. This is a foundational concept in supervised machine learning.
When you split the data, typically about 70-80% of the dataset is used for training the model, while the remaining 20-30% is used for testing (or validation). The reason behind this approach is to ensure that the model's performance metrics-such as accuracy, precision, recall, and F1-score-are evaluated on data the model has never seen before. This prevents overfitting and allows you to demonstrate that the model generalizes well to new, unseen data.
In the AI-900 Microsoft Learn content under "Describe the machine learning process", it is explained that after cleaning and transforming the data, the next essential step is data splitting to "evaluate model performance objectively." By keeping training and testing data separate, you can prove the reliability and accuracy of the model's predictions, which is particularly crucial in sensitive domains like clinical or healthcare analytics, where decision transparency and validation are vital.
* Option A (Train the model by using the clinical data) is incorrect because you should not train and evaluate on the same data-it would lead to biased results.
* Option C (Train the model using automated ML) is incorrect because automated ML is a method for training and tuning, but it doesn't inherently prove accuracy.
* Option D (Validate the model by using the clinical data) is also incorrect if you use the same dataset for validation and training-it would not prove true accuracy.
Therefore, per Microsoft's official AI-900 study content, the verified correct answer is B. Split the clinical data into two datasets.
NEW QUESTION # 56
......
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