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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Describe features of computer vision workloads on Azure | 15-20% | - Azure AI Vision services
|
| Topic 2: Describe fundamental principles of machine learning on Azure | 25-30% | - Azure Machine Learning basics
|
| Topic 3: Describe features of natural language processing (NLP) workloads on Azure | 15-20% | - Text analysis
|
| Topic 4: Describe Artificial Intelligence workloads and considerations | 20-25% | - Responsible AI principles
|
| Topic 5: Describe features of generative AI workloads on Azure | 10-15% | - Azure OpenAI and generative services
|
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NEW QUESTION # 34
Hotspot Question
Select the answer that correctly completes the sentence.
Answer:
Explanation:
Explanation:
Box: Natural Language Understanding (NLU)
A virtual assistant that converts spoken requests into written text relies primarily on Automatic Speech Recognition (ASR) or Speech-to-Text first, while Natural Language Understanding (NLU) is used subsequently to extract meaning, intent, and entities from that generated text.
How the Workload Splits
Speech-to-Text (ASR):
Turns audio waves into raw text words.
Handles accents, background noise, and phonetic matching.
Natural Language Understanding (NLU):
Analyzes the text produced by the ASR engine.
Figures out what the user actually wants (intent).
Finds key details like dates, names, or items (entities).
Reference:
https://www.assemblyai.com/blog/how-speech-to-text-ai-works-and-what-to-consider
NEW QUESTION # 35
What should you use to compare benchmark metrics of different AI models?
Answer: A
Explanation:
To compare benchmark metrics across Azure AI models, navigate to the Azure AI Foundry portal, explore the model catalog, and use the benchmarking tools to assess model performance. You can then compare these metrics to choose the most suitable model for your needs.
Reference:
https://learn.microsoft.com/en-us/azure/ai-foundry/how-to/benchmark-model-in-catalog
NEW QUESTION # 36
You plan to create an AI application that will use Azure OpenAI Service. The solution requires that a specific amount of throughput be allocated. Which type of deployment should you use?
Answer: D
Explanation:
When a specific amount of throughput is needed with Azure OpenAI Service, you should use Provisioned Throughput Units (PTUs) for your deployment. PTUs allow you to reserve a fixed amount of processing capacity for your models, ensuring consistent performance and avoiding potential throttling or rate limits.
In contrast, the "Standard" deployment option in Azure OpenAI Service uses a "pay-as-you-go" model, where you are billed for the amount of usage. While this can be suitable for applications with fluctuating usage patterns, it does not provide the same level of throughput control as PTUs.
In summary: When you need to allocate a specific amount of throughput with Azure OpenAI Service, choose the "Provisioned" deployment type, which allows you to use PTUs.
Reference:
https://github.com/MicrosoftDocs/azure-ai-docs/blob/main/articles/ai-services/openai/concepts/provisioned-throughput.md
NEW QUESTION # 37
You have a website that includes customer reviews.
You need to store the reviews in English and present the reviews to users in their respective language based on each user ' s geographical location.
Which type of natural language processing workload should you use?
Answer: B
NEW QUESTION # 38
Hotspot Question
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
Yes, generative AI models like large language models operate precisely by predicting the next token in a sequence using statistical patterns learned during training How Next-Token Prediction Works Tokenization: Input text splits into small chunks called tokens, which can be whole words, parts of words, or characters.
Context Processing: The model reads the existing sequence of tokens to evaluate the surrounding context.
Probability Distribution: It calculates statistical likelihoods for every possible next token in its vocabulary.
Iterative Loop: A single token gets chosen and added to the text, then the model repeats the entire process to generate the next one.
Box 2: Yes
Yes, a system prompt is used to influence the behavior, tone, and boundaries of a generative AI model. It acts as a foundational, high-level instruction set provided before user inputs are processed.
Box 3: No
Increasing the temperature makes generative AI responses more random, creative, and varied, while lowering the temperature makes them more deterministic, predictable, and consistent.
Low Temperature (near 0): The model picks the most likely words every time. This creates consistent and factual answers.
High Temperature (closer to 1 or higher): The model takes more risks and picks less common words. This creates unique and surprising answers, but can cause mistakes or off-topic text.
Reference:
https://www.ucf.edu/artificial-intelligence/how-do-generative-ai-tools-like-chatgpt-work/
NEW QUESTION # 39
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