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Microsoft Azure AI Fundamentals Sample Questions (Q212-Q217):
NEW QUESTION # 212
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?
- A. image classification
- B. object detection
- C. semantic segmentation
- D. optical character recognition (OCR)
Answer: B
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 # 213
Select the answer that correctly completes the sentence.
Answer:
Explanation:
Explanation:
In Azure Machine Learning Designer, the Dataset output visualization feature is specifically used to explore and understand the distribution of values in potential feature columns before model training begins. This capability is critical for data exploration and preprocessing, two essential stages of the machine learning pipeline described in the Microsoft Azure AI Fundamentals (AI-900) and Azure Machine Learning learning paths.
When a dataset is imported into Azure Machine Learning Designer, users can right-click on the dataset output port and select "Visualize". This launches the dataset visualization pane, which provides detailed statistical summaries for each column, including:
* Data type (numeric, categorical, string, Boolean)
* Minimum, maximum, mean, and standard deviation values for numeric columns
* Frequency counts and distinct values for categorical columns
* Missing value counts
This visual inspection helps determine which columns should be used as features, which might need normalization or encoding, and which contain missing or irrelevant data. It is a vital step in ensuring the dataset is clean and ready for model training.
Let's examine why other options are incorrect:
* Normalize Data module is used to scale numeric data, not to visualize distributions.
* Select Columns in Dataset module is used to include or exclude columns, not to analyze them.
* Evaluation results visualization feature is used after model training to interpret performance metrics like accuracy or recall, not data distributions.
Therefore, based on official Microsoft documentation and AI-900 study materials, to explore the distribution of values in potential feature columns, you use the Dataset output visualization feature in Azure Machine Learning Designer.
NEW QUESTION # 214
You have a database that contains a list of employees and their photos.
You are tagging new photos of the employees.
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:
Reference:
https://docs.microsoft.com/en-us/azure/cognitive-services/face/overview
https://docs.microsoft.com/en-us/azure/cognitive-services/face/concepts/face-detection
NEW QUESTION # 215
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:
NEW QUESTION # 216
You need to generate images based on user prompts. Which Azure OpenAI model should you use?
- A. DALL-E
- B. GPT-3
- C. GPT-4
- D. Whisper
Answer: A
Explanation:
According to the Microsoft Azure OpenAI Service documentation and AI-900 official study materials, the DALL-E model is specifically designed to generate and edit images from natural language prompts. When a user provides a descriptive text input such as "a futuristic city skyline at sunset", DALL-E interprets the textual prompt and produces an image that visually represents the content described. This functionality is known as text-to-image generation and is one of the creative AI capabilities supported by Azure OpenAI.
DALL-E belongs to the family of generative models that can create new visual content, expand existing images, or apply transformations to images based on textual instructions. Within Azure OpenAI, the DALL-E API enables developers to integrate image creation directly into applications-useful for design assistance, marketing content generation, or visualization tools. The model learns from vast datasets of text-image pairs and is optimized to ensure alignment, diversity, and accuracy in the produced visuals.
By contrast, the other options serve different purposes:
* A. GPT-4 is a large language model for text-based generation, reasoning, and conversation, not for creating images.
* C. GPT-3 is an earlier text generation model, primarily used for language tasks like summarization, classification, and question answering.
* D. Whisper is an automatic speech recognition (ASR) model used to convert spoken language into written text; it has no image-generation capability.
Therefore, when the requirement is to generate images based on user prompts, the only Azure OpenAI model that fulfills this purpose is DALL-E. This aligns directly with the AI-900 learning objective covering Azure OpenAI generative capabilities for text, code, and image creation.
NEW QUESTION # 217
......
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