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Microsoft AI-102 Exam Overview:

Certification Vendor:Microsoft
Exam Name:Designing and Implementing a Microsoft Azure AI Solution
Exam Number:AI-102
Related Certifications:Microsoft Certified: Azure AI Fundamentals (AI-900)
Exam Duration:100-120
Passing Score:700 (out of 1000)
Real Exam Qty:40-60 (approximate range)
Available Languages:Simplified Chinese, Japanese, Korean, French, German, Portuguese (Brazil), English, Spanish (Latin America)
Certificate Validity Period:1 year (renewable annually via Microsoft certification renewal assessment)
Exam Price:USD 165 (varies by country/region)
Exam Format:Build/review solution scenarios, Case studies, Drag and drop, Multiple choice, Multiple response
Recommended Training:Azure AI Engineer Associate Study Resources
Microsoft Learn AI-102 Learning Path
Exam Registration:Microsoft Certification Exam Registration (Pearson VUE)
Microsoft Learn AI-102 Certification Page
Sample Questions:Microsoft AI-102 Sample Questions
Exam Way:Online proctored or onsite test center (Pearson VUE)
Pre Condition:No formal prerequisites required, but familiarity with Azure fundamentals and AI/ML concepts is recommended
Official Syllabus URL:https://learn.microsoft.com/en-us/credentials/certifications/azure-ai-engineer/

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Prerequisites

Before enrolling in the process of taking the AI-102 Exams, candidates should be skilled in implementing Python and C#, using APIs and SDKs based on REST to create natural language processing solutions, computer vision solutions, and knowledge mining and communicative AI solutions based on Azure. In addition, such specialists should be knowledgeable of the elements that create the Azure AI portfolio as well as the data storage options. To add more, they should be able to implement AI principles appropriately.

Microsoft Designing and Implementing a Microsoft Azure AI Solution Sample Questions (Q11-Q16):

NEW QUESTION # 11
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:

* Normalization involves eliminating relationships between database tables.
* No
* Normalization does not eliminate relationships. In fact, it often creates relationships between tables by splitting data into smaller, related tables.
* Normalizing a database reduces data redundancy.
* Yes
* The primary goal of normalization is to minimize duplicate data and ensure that each fact is stored only once.
* Normalization improves data integrity.
* Yes
* By reducing redundancy and ensuring data is stored in a structured manner, normalization enforces consistency and integrity across the database.
* No
* Yes
* Yes
Correct Responses:
* Database normalization basics
* Database design and normalization
Microsoft References


NEW QUESTION # 12
You are building a retail chatbot that will use a QnA Maker service.
You upload an internal support document to train the model. The document contains the following question:
"What is your warranty period?"
Users report that the chatbot returns the default QnA Maker answer when they ask the following question:
"How long is the warranty coverage?"
The chatbot returns the correct answer when the users ask the following question: 'What is your warranty period?" Both questions should return the same answer.
You need to increase the accuracy of the chatbot responses.
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. (Choose three.)

Answer:

Explanation:

Explanation:

This scenario is about improving the accuracy of QnA Maker responses when user phrasing is different but semantically the same.
* Uploaded document contains: "What is your warranty period?"
* Users ask: "How long is the warranty coverage?"
* QnA Maker fails because it does not automatically equate these two phrasings.
* Both must map to the same answer.
* Add alternative phrasing to the question and answer (QnA) pair.
* In QnA Maker, you can add synonyms or paraphrases for a question.
* Example: Add "How long is the warranty coverage?" as an alternative phrasing to the existing QnA pair.
* This ensures the system treats both phrasings as the same question.
* Retrain the model.
* After modifying the knowledge base with new alternatives, you must retrain the model.
* This step incorporates the updates into the QnA Maker service.
* Republish the model.
* To make the changes live and accessible by the chatbot endpoint, the retrained model must be republished.
* Without republishing, the chatbot will still use the old knowledge base.
* Add a new QnA pair # Not needed, because we want both questions to map to the same existing answer, not duplicate answers.
* Add additional questions to the document # That's not required when QnA Maker already supports adding alternative phrasing within the portal or JSON editor.
* Add alternative phrasing to the question and answer (QnA) pair.
* Retrain the model.
* Republish the model.
* QnA Maker - Add alternative phrasing
* Train and publish a QnA Maker knowledge base


NEW QUESTION # 13
You have an Azure loT hub that receives series data from machinery. You need to build an app that will perform the following actions:
* Perform anomaly detection across multiple correlated sensors
* Identify the root cause of process stops.
* Send incident alerts
The solution must minimize development time. Which Azure service should you use?

Answer: C


NEW QUESTION # 14
You have an Azure subscription that contains an Azure Al service resource named CSAccount1 and a virtual network named VNet1 CSAaccount1 is connected to VNet1 You need to ensure that only specific resources can access CSAccount1. The solution must meet the following requirements:
* Prevent external access to CSAccount1
* Minimize administrative effort
Which two actions should you perform? Each correct answer presents part of the solution. NOTE: Each correct answer is worth one point.

Answer: B,E

Explanation:
You need to restrict access to an Azure AI services (Cognitive Services) account so that only specific resources on a virtual network can reach it and block public (external) access with minimum effort.
The simplest approach is to use Virtual Network service endpoints for Microsoft.CognitiveServices:
Enable the service endpoint on the VNet/subnet (VNet1) for Microsoft.CognitiveServices This routes traffic from that subnet to the Cognitive Services account over the Azure backbone and allows the account to identify traffic coming from that subnet.
Modify the Cognitive Services account's network settings (CSAccount1)
Set Public network access: Disabled (or allow access from Selected networks) and add the VNet/subnet enabled with the service endpoint to the allowed list.
This blocks all external access while permitting access from only the specified VNet resources.
Other options are not appropriate:
A). Modify VNet settings (generic) doesn't target the required service endpoint action.
C). IAM controls user/role authorization, not network access.
D). Create a virtual subnet isn't necessary if the VNet/subnet already exists; using service endpoints avoids creating/maintaining private endpoints.
Therefore, the correct pair is B and E.
Microsoft Azure AI Solution References
Secure Cognitive Services with virtual network service endpoints - explains enabling Microsoft.
CognitiveServices service endpoints on a subnet and restricting the account to selected networks.
https://learn.microsoft.com/azure/ai-services/cognitive-services-virtual-networks Network access restrictions for Cognitive Services - details configuring "Public network access" and allowing access from selected VNets/subnets.
https://learn.microsoft.com/azure/ai-services/cognitive-services-network-security Overview of securing Azure AI services - covers options (service endpoints vs. private endpoints) and minimal configuration steps.
https://learn.microsoft.com/azure/ai-services/security-networking


NEW QUESTION # 15
You have an app named App1 that uses a custom Azure Al Document Intelligence model to recognize contract documents. You need to ensure that the model supports an additional contract format. The solution must minimize development effort. What should you do?

Answer: D

Explanation:
* A. Lower the confidence score threshold of App1
* Would just accept more low-confidence predictions, but won't add support for a new format. #
* B. Lower the accuracy threshold of App1
* Similar reasoning: affects prediction acceptance, not model capabilities. #
* C. Add the additional contract format to the existing training set and retrain the model
* Correct approach. You extend the training data to include the new format, retrain, and reuse the same model. #
* D. Create a new training set and add the additional contract format
* This means starting from scratch, which requires more effort. Not minimal. #
* E. Create and train a new custom model
* Same as D, higher effort than necessary. #
The answer:
C). Add the additional contract format to the existing training set. Retrain the model.
Microsoft Reference:
* Azure AI Document Intelligence - Custom models


NEW QUESTION # 16
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