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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Describe fundamental principles of machine learning on Azure | 25-30% | - Azure Machine Learning basics
|
| Topic 2: Describe features of natural language processing (NLP) workloads on Azure | 15-20% | - Text analysis
|
| Topic 3: Describe Artificial Intelligence workloads and considerations | 20-25% | - Identify features of common AI workloads
|
| Topic 4: Describe features of generative AI workloads on Azure | 10-15% | - Azure OpenAI and generative services
|
| Topic 5: Describe features of computer vision workloads on Azure | 15-20% | - Image analysis
|
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NEW QUESTION # 98
Select the answer that correctly completes the sentence.
Answer:
Explanation:
Explanation:
Ongoing monitoring, feedback, and evaluation are goals of the Reliability & Safety principle.
The Microsoft responsible AI principle of Reliability and safety focuses on ensuring that AI systems operate consistently, safely, and as intended across different conditions. This includes testing, monitoring, collecting feedback, and evaluating system performance over time.
Why the other options are incorrect:
Human oversight and control aligns more closely with accountability.
Data governance and management aligns more closely with privacy and security.
The minimization of stereotyping, demeaning, and erasing outputs aligns more closely with fairness and inclusiveness.
NEW QUESTION # 99
You are building an AI system.
Which task should you include to help the service meet the Microsoft transparency principle for responsible AI?
Answer: B
NEW QUESTION # 100
You deploy the Azure OpenAI service to generate images.
You need to ensure that the service provides the highest level of protection against harmful content.
What should you do?
Answer: B
NEW QUESTION # 101
You are developing a simple application that uses the Microsoft Foundry SDK to send chat prompts to a model.
Which three elements should you provide in the code to run the application? Each correct answer presents part of the solution.
NOTE: Each correct selection is worth one point.
Answer: A,C,E
Explanation:
To build a simple application that sends chat prompts to a model using the Microsoft Foundry SDK, you will initialize a project client using your endpoint and credentials, then request chat completions using your model deployment name.
Microsoft Entra ID Credentials: Handled via DefaultAzureCredential(). This eliminates the need to hardcode sensitive API keys. Before running this app, ensure you are authenticated in your terminal by running az login via the Azure CLI. Your account must have the Cognitive Services User or Azure AI Developer role assigned.
Endpoint URL: Passed directly into the AIProjectClient initializer. This maps your code to the specific Azure AI Foundry project hub managing your computational resources.
Model Deployment Name: Referenced inside the final .create() method payload. This instructs the inference routing service exactly which deployed LLM (e.g., GPT-4o, DeepSeek, or Claude) should process your incoming chat transcript.
Reference:
https://learn.microsoft.com/en-us/azure/foundry/openai/how-to/chatgpt
NEW QUESTION # 102
Hotspot Question
Select the answer that correctly completes the sentence.
Answer:
Explanation:
Explanation:
Box: IProjectClient
The AIProjectClient class (from the azure-ai-projects package) is the primary entry point used to access project deployments, agents, and indexes when building applications with the Microsoft Foundry SDK.
Reference:
https://pypi.org/project/azure-ai-projects/
NEW QUESTION # 103
......
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