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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Describe features of Natural Language Processing (NLP) workloads on Azure | 15-20% | - Identify common NLP tasks - Describe Azure capabilities for NLP - Identify Azure AI services for NLP |
| Topic 2: Describe features of computer vision workloads on Azure | 15-20% | - Describe Azure capabilities for computer vision - Identify common computer vision tasks - Identify Azure AI services for computer vision |
| Topic 3: Describe AI workloads and considerations | 15-20% | - Identify features of common AI workloads - Identify guiding principles for responsible AI |
| Topic 4: Describe fundamental principles of machine learning on Azure | 30-35% | - Describe core machine learning concepts - Describe Azure Machine Learning capabilities - Describe features of no-code automated ML - Identify common machine learning tasks |
| Topic 5: Describe features of Generative AI workloads on Azure | 15-20% | - Describe Azure OpenAI Service capabilities - Describe generative AI concepts - Identify responsible AI considerations for generative AI |
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NEW QUESTION # 329
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
Reference:
https://docs.microsoft.com/en-gb/azure/cognitive-services/text-analytics/overview
https://azure.microsoft.com/en-gb/services/cognitive-services/speech-services/ You can use the Speech service to transcribe a call to text - Yes we can use Speech to Text API to achieve this
https://docs.microsoft.com/en-us/learn/modules/recognize-synthesize-speech/1-introduction You can use a speech service to translate the audio of a call to a different language - Yes we can use Speech translation service to achieve this The Speech service includes the following application programming interfaces (APIs):
Speech-to-text - used to transcribe speech from an audio source to text format.
Text-to-speech - used to generate spoken audio from a text source.
Speech Translation - used to translate speech in one language to text or speech in another.
https://docs.microsoft.com/en-us/learn/modules/translate-text-with-translation-service/2-get-started-azure You can use text analytics service to extract key entities from a call transcript -Yes Text Analytics API helps to achieve this
https://docs.microsoft.com/en-us/learn/modules/analyze-text-with-text-analytics-service/2-get-started-azure
NEW QUESTION # 330
To complete the sentence, select the appropriate option in the answer area.
Answer:
Explanation:
Explanation:
Text Description automatically generated
Reference:
https://docs.microsoft.com/en-us/azure/cognitive-services/custom-vision-service/getting-started-build-a-classifie
NEW QUESTION # 331
You need to predict the income range of a given customer by using the following dataset.
Which two fields should you use as features? Each correct answer presents a complete solution.
NOTE: Each correct selection is worth one point.
Answer: B,C
Explanation:
Explanation
First Name, Last Name, Age and Education Level are features. Income range is a label (what you want to predict). First Name and Last Name are irrelevant in that they have no bearing on income. Age and Education level are the features you should use.
NEW QUESTION # 332
Select the answer that correctly completes the sentence.
Answer:
Explanation:
Explanation:
In Azure OpenAI Service, the temperature parameter directly controls the creativity and determinism of responses generated by models such as GPT-3.5. According to the Microsoft Learn documentation for Azure OpenAI models, temperature is a numeric value (typically between 0.0 and 2.0) that determines how
"random" or "deterministic" the output should be.
* A lower temperature value (for example, 0 or 0.2) makes the model's responses more deterministic, meaning the same prompt consistently produces nearly identical outputs.
* A higher temperature value (for example, 0.8 or 1.0) encourages creativity and variety, causing the model to generate different phrasing or interpretations each time it responds.
When a question specifies the need for more deterministic responses, Microsoft's guidance is to decrease the temperature parameter. This adjustment makes the model focus on the most probable tokens (words) rather than exploring less likely options, improving reliability and consistency-ideal for business or technical applications where consistent answers are essential.
The other parameters serve different purposes:
* Frequency penalty reduces repetition of the same phrases but does not control randomness.
* Max response (max tokens) limits the maximum length of the generated output.
* Stop sequence defines specific tokens that tell the model when to stop generating text.
Thus, the correct and Microsoft-verified completion is:
"You can modify the Temperature parameter to produce more deterministic responses from a chat solution that uses the Azure OpenAI GPT-3.5 model."
NEW QUESTION # 333
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/bot-service/bot-service-overview-introduction?view=azure-bot-service-4.0
NEW QUESTION # 334
......
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