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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Features of Natural Language Processing (NLP) workloads on Azure | 15–20% | - Describe capabilities of Azure Translator - Identify types of NLP solutions - Describe capabilities of Azure Language - Describe capabilities of Azure Speech |
| Topic 2: Fundamental principles of machine learning on Azure | 15–20% | - Describe core concepts of machine learning - Describe automated machine learning - Describe capabilities of Azure Machine Learning - Describe machine learning pipelines |
| 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: 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 generative AI workloads on Azure | 20–25% | - Describe use cases for generative AI - Describe generative AI concepts - Describe capabilities of Azure OpenAI Service - Describe responsible AI practices for generative AI |
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NEW QUESTION # 81
To complete the sentence, select the appropriate option in the answer area.
Answer:
Explanation:
Explanation:
Graphical user interface, table Description automatically generated
Reference:
https://docs.microsoft.com/en-us/azure/cloud-adoption-framework/innovate/best-practices/trusted-ai
NEW QUESTION # 82
What is a use case for classification?
Answer: D
Explanation:
According to the Microsoft Azure AI Fundamentals (AI-900) official study guide and Microsoft Learn module "Identify features of classification machine learning", classification is a type of supervised machine learning used when the goal is to predict a categorical outcome. That means the output variable represents discrete labels such as Yes/No, True/False, or Category A/B/C.
In this example, the model is predicting whether a person uses a bicycle (Yes or No) - a binary categorical outcome. The input (distance from home to work) is numeric, but the prediction is a class or category, which makes it a classification problem.
To compare:
* A and D (predicting how many cups of coffee or race minutes) involve numeric predictions, which are regression tasks.
* B (grouping images by similar colors) involves clustering, an unsupervised learning method used to find natural groupings in data.
Thus, the use case that fits classification is predicting whether someone uses a bicycle, since the answer is categorical.
Reference:Microsoft Learn - Identify the types of machine learning models: Classification, Regression, and Clustering (AI-900 Learning Path)
NEW QUESTION # 83
You are developing a conversational AI solution that will communicate with users through multiple channels including email, Microsoft Teams, and webchat.
Which service should you use?
Answer: B
Explanation:
Reference:
https://docs.microsoft.com/en-us/azure/bot-service/bot-service-overview-introduction?view=azure-bot-service-4.
NEW QUESTION # 84
You plan to deploy an Azure Machine Learning model by using the Machine Learning designer Which four actions should you perform in sequence? To answer, move the appropriate actions from the list of actions to the answer area and arrange them in the correct order.
Answer:
Explanation:
Explanation:
According to the Microsoft Azure AI Fundamentals (AI-900) official study guide and the Microsoft Learn module "Identify features of common machine learning types", the standard workflow for creating and deploying a machine learning model - especially within Azure Machine Learning Designer - follows a structured sequence of steps to ensure that the model is trained effectively and evaluated correctly.
Here's the detailed breakdown of the correct order:
* Import and prepare a dataset:This is always the first step in the machine learning lifecycle. The dataset is imported into Azure Machine Learning and cleaned or preprocessed. Preparation might include handling missing values, normalizing data, removing outliers, and encoding categorical variables. This ensures the dataset is ready for modeling.
* Split the data randomly into training data and validation data:The dataset is then divided into two parts
- the training set and the validation (or testing) set. Typically, around 70-80% of the data is used for training and 20-30% for validation. This step ensures that the model can be evaluated on unseen data later, preventing overfitting.
* Train the model:During this stage, the machine learning algorithm learns patterns from the training data. Azure Machine Learning Designer provides multiple algorithms (classification, regression, clustering, etc.) that can be applied using "Train Model" components.
* Evaluate the model against the validation dataset:Finally, the trained model's performance is tested using the validation dataset. Evaluation metrics such as accuracy, precision, recall, or RMSE (depending on the model type) are calculated to assess how well the model generalizes to new data.
The incorrect option - "Evaluate the model against the original dataset" - is not used in proper ML workflows, because evaluating on the same data used for training would give misleadingly high accuracy due to overfitting.
NEW QUESTION # 85
You send an image to a Computer Vision API and receive back the annotated image shown in the exhibit.
Which type of computer vision was used?
Answer: A
Explanation:
Section: Describe features of computer vision workloads on Azure
Explanation:
Object detection is similar to tagging, but the API returns the bounding box coordinates (in pixels) for each object found. For example, if an image contains a dog, cat and person, the Detect operation will list those objects together with their coordinates in the image. You can use this functionality to process the relationships between the objects in an image. It also lets you determine whether there are multiple instances of the same tag in an image.
The Detect API applies tags based on the objects or living things identified in the image. There is currently no formal relationship between the tagging taxonomy and the object detection taxonomy. At a conceptual level, the Detect API only finds objects and living things, while the Tag API can also include contextual terms like
"indoor", which can't be localized with bounding boxes.
Reference:
https://docs.microsoft.com/en-us/azure/cognitive-services/computer-vision/concept-object-detection
NEW QUESTION # 86
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