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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Features of natural language processing (NLP) workloads on Azure | 30-35% | - Text analytics and language understanding
|
| Topic 2: Fundamentals of machine learning on Azure | 25-30% | - Core machine learning concepts
|
| Topic 3: Features of computer vision workloads on Azure | 15-20% | - Computer vision solutions
|
| Topic 4: Describe AI workloads and considerations | 20-25% | - Fundamentals of artificial intelligence concepts
|
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NEW QUESTION # 332
You have the following dataset.
You plan to use the dataset to train a model that will predict the house price categories of houses.
What are Household Income and House Price Category? To answer, select the appropriate option in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
Box 1: A feature
Box 2: A label
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/studio/interpret-model-results
NEW QUESTION # 333
Which term is used to describe uploading your own data to customize an Azure OpenAI model?
Answer: B
Explanation:
In Azure OpenAI Service, fine-tuning refers to the process of uploading your own labeled dataset to customize or adapt a pretrained model (such as GPT-3.5 or Curie) for a specific use case. According to the Microsoft Learn documentation and AI-900 official study guide, fine-tuning allows organizations to improve a model's performance on domain-specific tasks or to align responses with brand tone and context.
Fine-tuning differs from simple prompting because it requires providing structured training data (usually in JSONL format) that contains pairs of input prompts and ideal completions. The model uses this data to adjust its internal weights, thereby "learning" your organization's language patterns, terminology, or industry context.
Option review:
* A. Completion: Refers to the text generated by a model in response to a prompt. It's the output, not the customization process.
* B. Grounding: Integrates external, up-to-date data sources (like search results or databases) during inference but doesn't alter the model's parameters.
* C. Fine-tuning: # Correct - this is the process of uploading and training with your own data.
* D. Prompt engineering: Involves designing effective prompts but does not change the underlying model.
Thus, fine-tuning is the term used for customizing an Azure OpenAI model using your own uploaded data.
NEW QUESTION # 334
Match the Azure OpenAI large language model (LLM) process to the appropriate task.
To answer, drag the appropriate process from the column on the left to its task on the right. Each process may be used once, more than once, or not at all.
NOTE: Each correct match is worth one point.
Answer:
Explanation:
Explanation:
NEW QUESTION # 335
For each of the following statements, select Yes if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation
Box 1: Yes
In machine learning, if you have labeled data, that means your data is marked up, or annotated, to show the target, which is the answer you want your machine learning model to predict.
In general, data labeling can refer to tasks that include data tagging, annotation, classification, moderation, transcription, or processing.
Box 2: No
Box 3: No
Accuracy is simply the proportion of correctly classified instances. It is usually the first metric you look at when evaluating a classifier. However, when the test data is unbalanced (where most of the instances belong to one of the classes), or you are more interested in the performance on either one of the classes, accuracy doesn't really capture the effectiveness of a classifier.
Reference:
https://www.cloudfactory.com/data-labeling-guide
https://docs.microsoft.com/en-us/azure/machine-learning/studio/evaluate-model-performance
NEW QUESTION # 336
Your company manufactures widgets.
You have 1.000 digital photos of the widgets.
You need to identify the location of the widgets within the photos.
What should you use?
Answer: A
Explanation:
According to the Microsoft Azure AI Fundamentals (AI-900) Official Study Guide and the Microsoft Learn module "Explore computer vision in Microsoft Azure," object detection is a computer vision technique used to locate and identify objects within an image. It not only determines what objects are present but also where they appear in the image by returning bounding box coordinates around each detected item.
In this scenario, the goal is to identify the location of widgets within digital photos. This requires both recognition (knowing that the object is a widget) and localization (determining its position). The Custom Vision service in Azure allows you to train a model specifically for your own images, making it ideal for recognizing company-specific products such as widgets. By selecting the Object Detection domain in Custom Vision, you can label regions of interest in your training images. The model then learns to detect and locate those objects in new photos.
Let's examine the other options:
* A. Computer Vision Spatial Analysis: Used for people tracking, movement detection, and occupancy analytics in video streams - not for locating products in still images.
* C. Custom Vision classification: This model categorizes an image as a whole (e.g., "contains a widget" or "does not contain a widget") but does not locate objects within the image.
* D. Computer Vision Image Analysis: Provides general image tagging, description, and OCR capabilities but does not pinpoint object locations.
NEW QUESTION # 337
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