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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Describe features of Generative AI workloads on Azure | 15-20% | - Identify responsible AI considerations for generative AI - Describe Azure OpenAI Service capabilities - Describe generative AI concepts |
| Topic 2: Describe AI workloads and considerations | 15-20% | - Identify features of common AI workloads - Identify guiding principles for responsible AI |
| Topic 3: Describe fundamental principles of machine learning on Azure | 30-35% | - Identify common machine learning tasks - Describe core machine learning concepts - Describe Azure Machine Learning capabilities - Describe features of no-code automated ML |
| Topic 4: Describe features of computer vision workloads on Azure | 15-20% | - Identify common computer vision tasks - Identify Azure AI services for computer vision - Describe Azure capabilities for computer vision |
| Topic 5: 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 |
>> AI-900 Most Reliable Questions <<
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NEW QUESTION # 204
You are evaluating whether to use a basic workspace or an enterprise workspace in Azure Machine Learning.
What are two tasks that require an enterprise workspace? Each correct answer presents a complete solution.
NOTE: Each correct selection is worth one point.
Answer: B,D
Explanation:
Explanation
Note: Enterprise workspaces are no longer available as of September 2020. The basic workspace now has all the functionality of the enterprise workspace.
Reference:
https://www.azure.cn/en-us/pricing/details/machine-learning/
https://docs.microsoft.com/en-us/azure/machine-learning/concept-workspace
NEW QUESTION # 205
Match the machine learning models to the appropriate deceptions.
To answer, drag the appropriate model from the column on the left to its description on the right Each model may be used once, more than once, or not at all.
NOTE: Each correct match is worth one point.
Answer:
Explanation:
NEW QUESTION # 206
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 # 207
You have a natural language processing (NIP) model that was created by using data obtained without permission.
Which Microsoft principle for responsible Al does this breach?
Answer: A
Explanation:
According to the Microsoft Azure AI Fundamentals (AI-900) official study materials and Microsoft's Responsible AI Principles, one of the core principles is "Privacy and Security." This principle ensures that AI systems protect personal and sensitive information, maintaining compliance with privacy laws, data protection regulations, and ethical data-handling practices.
If a Natural Language Processing (NLP) model is created using data obtained without permission, it directly violates this principle. Data collected without proper consent breaches user privacy and potentially violates regulations such as GDPR (General Data Protection Regulation) or other global privacy frameworks.
The Privacy and Security principle emphasizes the following:
* AI systems must ensure data collection and usage transparency.
* Data must be lawfully acquired and used with consent.
* Systems should protect data against unauthorized access or misuse.
In contrast:
* Inclusiveness promotes accessibility and fairness for all users.
* Transparency focuses on explaining how AI systems make decisions.
* Reliability and safety ensure systems function as intended and minimize harm.
Therefore, using unapproved data clearly breaches Privacy and Security, as it involves unethical data sourcing and endangers user trust.
NEW QUESTION # 208
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
Azure bot service can be integrated with the powerful AI capabilities with Azure Cognitive Services.
Box 2: Yes
Azure bot service engages with customers in a conversational manner.
Box 3: No
The QnA Maker service creates knowledge base, not question and answers sets.
Note: You can use the QnA Maker service and a knowledge base to add question-and-answer support to your bot. When you create your knowledge base, you seed it with questions and answers.
Reference:
https://docs.microsoft.com/en-us/azure/bot-service/bot-builder-tutorial-add-qna
NEW QUESTION # 209
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