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| Section | Weight | Objectives |
|---|---|---|
| Features of natural language processing (NLP) workloads on Azure | 30-35% | - Text analytics and language understanding
|
| Describe AI workloads and considerations | 20-25% | - Fundamentals of artificial intelligence concepts
|
| Features of computer vision workloads on Azure | 15-20% | - Computer vision solutions
|
| Fundamentals of machine learning on Azure | 25-30% | - Core machine learning concepts
|
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NEW QUESTION # 260
To complete the sentence, select the appropriate option in the answer area.
Answer:
Explanation:
Explanation:
Features
NEW QUESTION # 261
Select the answer that correctly completes the sentence.
Answer:
Explanation:
Explanation:
The correct answer is "An embedding."
In the context of large language models (LLMs) such as GPT-3, GPT-3.5, or GPT-4, an embedding refers to a multi-dimensional numeric vector representation assigned to each word, token, or phrase. According to the Microsoft Azure AI Fundamentals (AI-900) study guide and Microsoft Learn documentation for Azure OpenAI embeddings, embeddings are used to represent textual or semantic meaning in a numerical form that a machine learning model can process mathematically.
Each embedding captures the semantic relationships between words. Words or tokens with similar meanings (for example, "car" and "automobile") are represented by vectors that are close together in the multi- dimensional space, while unrelated words (like "tree" and "laptop") are farther apart. This vector representation enables the model to understand context, similarity, and relationships between different pieces of text.
Embeddings are fundamental in tasks such as:
* Semantic search: Finding documents or sentences with similar meaning.
* Clustering: Grouping related concepts together.
* Recommendation systems: Suggesting similar content based on text meaning.
* Contextual understanding: Helping generative models produce coherent and context-aware text.
Option review:
* Attention: A mechanism used within transformers to focus on relevant parts of input sequences but not a representation of words.
* A completion: Refers to the generated text output from a model, not the internal representation.
* A transformer: The architecture that powers models like GPT, not the vector representation of tokens.
Therefore, the correct term for a multi-dimensional vector assigned to each word or token in a large language model (LLM) is An embedding, which represents how meaning is numerically encoded and compared within language models.
NEW QUESTION # 262
brectly completes the sentence.
Answer:
Explanation:
Explanation:
NEW QUESTION # 263
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 # 264
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:
According to the Microsoft Azure AI Fundamentals (AI-900) official study guide and the Microsoft Learn module "Identify features of the Computer Vision and Custom Vision services", the Custom Vision service is used to train, deploy, and improve custom image classification and object detection models using your own labeled data.
* Multilabel or Multiclass Selection - NOThe statement is false because the Multilabel or Multiclass choice applies only to image classification models, not object detection models. In image classification,
"Multiclass" means one label per image, while "Multilabel" means multiple labels per image. In contrast, object detection models identify and locate multiple objects in an image using bounding boxes; thus, this classification-type selection does not apply.
* Object Detection Locates Content in an Image - YESThis statement is true. The object detection functionality in Custom Vision is designed to both identify what objects appear in an image and determine their location through bounding box coordinates. For example, a model could detect and locate multiple products on a store shelf. Microsoft documentation describes object detection as
"identifying the presence and location of objects in an image."
* Predefined Domains - YESThis statement is true as well. When you create a new Custom Vision project, you must select a domain, which is a predefined optimization setting tailored to specific use cases such as retail, food, landmarks, or general images. These domains are designed to improve model accuracy by applying specialized transfer learning features based on the type of images you will analyze.
In summary:
* Classification type (Multilabel/Multiclass): No (only for classification models)
* Detect object location: Yes
* Choose predefined domain: Yes
NEW QUESTION # 265
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