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Microsoft AI-102 exam is designed to test the candidate's ability to design and implement AI solutions using Azure technologies. AI-102 exam covers a range of concepts, such as data pre-processing, model training and evaluation, deployment, and monitoring. Additionally, the exam assesses the candidate's ability to work with different Azure services and tools, including Azure Machine Learning, Cognitive Services, and Bot Services. Furthermore, the exam evaluates the candidate's ability to design and implement secure and scalable AI solutions that meet business requirements.
For candidates that are aiming to develop their skills in building, operating, and deploying AI solutions with the help of such services as Azure Applied AI services and Azure Cognitive Services, the best variant is to pass the Microsoft AI-102 Exam. This exam is all about designing and applying a Microsoft Azure AI Solution, and leads to getting the Microsoft Certified: Azure AI Engineer Associate certification.
Passing this exam implies that certified candidates are able to participate in all stages of AI solutions development from defining requirements to performance tuning and monitoring. These professionals cooperate with solution architects, as well as with data engineers and scientists, AI developers to show their vision and create comprehensive AI solutions.
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NEW QUESTION # 166
You have an Azure subscription that contains an Azure OpenAl resource. Multiple different models are deployed to the resource.
You are building a chatbot by using Chat playground in Azure Al Studio.
You need to ensure that the chatbot generates text in concise formal business language. The solution must meet the following requirements:
* Reduce the cost of running the language model.
* Maintain the size of the chatbot history window.
Which two settings should you configure? To answer, select the appropriate settings in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
Scenario Recap:
* You have an Azure OpenAI resource with multiple deployed models.
* You are using the Chat playground to build a chatbot.
* Requirements:
* Generate text in concise, formal business language.
* Reduce cost of running the model.
* Maintain the size of the chatbot history window (i.e., context length).
Step 1: Ensuring concise, formal business language In the Chat playground setup panel, you can configure System message instructions.
Example:
You are an AI assistant that responds in concise formal business language.
This controls tone, style, and response behavior.
So one setting is: System message / Instructions.
Step 2: Reducing cost while maintaining chat history window Costs in Azure OpenAI are tied to:
* The model used (larger models = more expensive).
* The context window size (token usage).
Since we must maintain the size of the chat history window, we cannot shrink context length.
Therefore, to reduce cost: select a smaller/cheaper deployed model (e.g., GPT-3.5 instead of GPT-4).
So the second setting is: Deployment (model selection).
* Configure Deployment # choose a smaller/cheaper model.
* Configure System message instructions # specify "concise formal business language."
# Final Answer:
Microsoft References:
* Azure OpenAI Chat playground
* Best practices for reducing token cost
NEW QUESTION # 167
You have a factory that produces cardboard packaging for food products. The factory has intermittent internet connectivity.
The packages are required to include four samples of each product.
You need to build a Custom Vision model that will identify defects in packaging and provide the location of the defects to an operator. The model must ensure that each package contains the four products.
Which project type and domain should you use? To answer, drag the appropriate options to the correct targets. Each option may be used once, more than once, or not at all. You may need to drag the split bar between panes or scroll to view content NOTE: Each correct selection is worth one point.
Answer:
Explanation:
NEW QUESTION # 168
You have a web app that uses Azure Cognitive Search.
When reviewing billing for the app, you discover much higher than expected charges. You suspect that the query key is compromised.
You need to prevent unauthorized access to the search endpoint and ensure that users only have read only access to the documents collection. The solution must minimize app downtime.
Which three actions should you perform in sequence? To answer, move the appropriate actions from the list of actions to the answer area and arrange them in the correct order.
Answer:
Explanation:
Explanation:
Graphical user interface, text Description automatically generated
Reference:
https://docs.microsoft.com/en-us/azure/search/search-security-api-keys
NEW QUESTION # 169
You have a Custom Vision resource named acvdev in a development environment.
You have a Custom Vision resource named acvprod in a production environment.
In acvdev, you build an object detection model named obj1 in a project named proj1.
You need to move obj1 to acvprod.
Which three actions should you perform in sequence? To answer, move the appropriate actions from the list of actions to the answer area and arrange them in the correct order.
Answer:
Explanation:
Explanation
Text Description automatically generated
Reference:
https://docs.microsoft.com/en-us/azure/cognitive-services/luis/luis-tutorial-pattern#what-did-this-tutorial-accomp
NEW QUESTION # 170
You are building an app that will analyze text by using the Azure Al Language service.
You need to configure the app to mask the telephone number and email details in a given document.
How should you complete the code? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
To mask (redact) phone numbers and email addresses in text using Azure AI Language, you should call the PII entity recognition operation and then print the redacted text that the service returns. In the SDKs, this corresponds to calling RecognizePiiEntities and reading the RedactedText from the result object. The PII feature automatically detects categories such as PhoneNumber and Email and returns a redacted version of the input (masked) in the redactedText/RedactedText field. This is exactly what the code snippet's "Masked Text" output is asking for.
Therefore:
Use RecognizePiiEntities to detect and redact PII in the document.
Output RedactedText to print the masked string.
Microsoft References
How-to: Detect and redact PII in text (overview and redaction behavior). Microsoft Learn Quickstart: Detect Personally Identifiable Information (shows PII detection and redacted output). Microsoft Learn NET reference: PiiEntityCollection.RedactedText property ("Gets the text of the input document with all PII redacted"). Microsoft Learn Python client overview: recognize_pii_entities operation for PII detection. Microsoft Learn
NEW QUESTION # 171
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