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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Features of generative AI workloads on Azure | 20–25% | - Describe responsible AI practices for generative AI - Describe generative AI concepts - Describe use cases for generative AI - Describe capabilities of Azure OpenAI Service |
| Topic 2: Fundamental principles of machine learning on Azure | 15–20% | - Describe machine learning pipelines - Describe core concepts of machine learning - Describe automated machine learning - Describe capabilities of Azure Machine Learning |
| Topic 3: Features of computer vision workloads on Azure | 15–20% | - Describe capabilities of Azure Face - Describe capabilities of Azure Computer Vision - Identify types of computer vision solutions - Describe capabilities of Azure Custom Vision - Describe capabilities of Azure Form Recognizer |
| Topic 4: Artificial Intelligence workloads and considerations | 15–20% | - Identify types of AI workloads - Describe responsible AI principles - Describe considerations for developing AI solutions |
| Topic 5: Features of Natural Language Processing (NLP) workloads on Azure | 15–20% | - Identify types of NLP solutions - Describe capabilities of Azure Speech - Describe capabilities of Azure Language - Describe capabilities of Azure Translator |
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146. Frage
Which type of machine learning should you use to predict the number of gift cards that will be sold next month?
Antwort: A
Begründung:
Clustering, in machine learning, is a method of grouping data points into similar clusters. It is also called segmentation.
Over the years, many clustering algorithms have been developed. Almost all clustering algorithms use the features of individual items to find similar items. For example, you might apply clustering to find similar people by demographics. You might use clustering with text analysis to group sentences with similar topics or sentiment.
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/machine-learning-initialize- model-clustering
147. Frage
You build a machine learning model by using the automated machine learning user interface (UI).
You need to ensure that the model meets the Microsoft transparency principle for responsible AI. What should you do?
Antwort: D
Begründung:
Model Explain Ability.
Most businesses run on trust and being able to open the ML "black box" helps build transparency and trust. In heavily regulated industries like healthcare and banking, it is critical to comply with regulations and best practices. One key aspect of this is understanding the relationship between input variables (features) and model output. Knowing both the magnitude and direction of the impact each feature (feature importance) has on the predicted value helps better understand and explain the model. With model explain ability, we enable you to understand feature importance as part of automated ML runs.
Reference:
https://azure.microsoft.com/en-us/blog/new-automated-machine-learning-capabilities-in-azure-machine- learning-service/
148. Frage
Which type of natural language processing (NLP) entity is used to identify a phone number?
Antwort: C
149. Frage
You need to create a clustering model and evaluate the model by using Azure Machine Learning designer.
What should you do?
Antwort: B
Begründung:
According to the Microsoft Learn module "Explore fundamental principles of machine learning" and the AI-
900 Official Study Guide, when building and evaluating a model (such as a clustering model) in Azure Machine Learning designer, data must be divided into two subsets:
* Training dataset: Used to train the model so it can learn patterns and relationships in the data.
* Testing dataset: Used to evaluate the model's performance on unseen data, ensuring that it generalizes well and does not overfit.
In Azure ML Designer, this is typically done using the Split Data module, which separates the dataset into training and testing portions (for example, 70% training and 30% testing). After training, you connect the testing dataset to an Evaluate Model module to assess metrics such as accuracy, precision, or silhouette score (for clustering).
Other options are incorrect:
* A. Split into features and labels: Clustering is an unsupervised learning technique, so it doesn't use labeled data.
* B. Use training dataset for evaluation: This would cause overfitting, as the model is being tested on the same data it learned from.
* D. Use the original dataset for training and evaluation: Also causes overfitting, offering no measure of generalization.
150. Frage
You need to build an image tagging solution for social media that tags images of your friends automatically.
Which Azure Cognitive Services service should you use?
Antwort: D
Begründung:
The correct answer is B. Face because the Azure Face service, part of Azure Cognitive Services, provides capabilities for detecting, recognizing, and analyzing human faces in images. According to the Microsoft Azure AI Fundamentals (AI-900) official study guide and Microsoft Learn module "Explore computer vision", the Face service can automatically detect faces, determine attributes such as age, gender, emotion, and identify or verify individuals based on facial features.
In this scenario, the goal is to build an image tagging solution that automatically tags images of friends on social media. This requires the ability to recognize and match faces of known individuals, which is a key feature of the Face API. The API uses facial recognition technology to create face IDs for detected faces and can compare new faces against stored face data to identify known individuals. Microsoft Learn notes: "The Face service can detect, recognize, and identify people in images, enabling automated tagging or authentication scenarios." Other options explained:
* A. Computer Vision identifies objects, landmarks, and general content but does not specialize in identifying individual people.
* C. Text Analytics analyzes textual data for sentiment, key phrases, or entities, not image data.
* D. Form Recognizer extracts structured data from forms or receipts, unrelated to face detection.
Thus, for automatically tagging people in photos, Azure Face is the correct and most suitable service.
151. Frage
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