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The AI-102 Certification Exam covers a wide range of topics related to Azure AI, including designing AI solutions that use cognitive services, machine learning, and other AI technologies. AI-102 exam also covers topics such as data preparation, natural language processing, and computer vision. Designing and Implementing a Microsoft Azure AI Solution certification exam is intended for professionals who are looking to build their skills in AI and machine learning, and who want to demonstrate their expertise in this area to potential employers.
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Microsoft AI-102 Exam is an advanced-level certification that requires a deep understanding of AI technologies and their applications. Candidates are expected to have a strong grasp of AI concepts, programming languages, and data science principles. Passing AI-102 Exam is a great way to demonstrate your expertise in designing and implementing AI solutions using Microsoft Azure, and it can help you advance your career in the field of AI and machine learning. Microsoft Designing and Implementing a Microsoft Azure AI Solution Sample Questions (Q143-Q148):NEW QUESTION # 143
Select the answer that correctly completes the sentence. Answer:
Explanation:
Explanation
NEW QUESTION # 144
You have a chatbot that was built by using the Microsoft Bot Framework. You need to debug the chatbot endpoint remotely.
Which two tools should you install on a local computer? Each correct answer presents part of the solution. (Choose two.) NOTE: Each correct selection is worth one point.
A. Bot Framework Composer
B. Bot Framework CLI
C. Fiddler
D. ngrok
E. Bot Framework Emulator
F. nginx
Answer: D,E
NEW QUESTION # 145
You have an Azure subscription that contains an Azure App Service app named App1.
You provision a multi-service Azure AI Services resource named CSAccount1.
You need to configure App1 to access CSAccount1. The solution must minimize administrative effort.
What should you use to configure App1?
A. the endpoint URI and subscription key
B. the endpoint URI and a shared access signature (SAS) token
C. a system-assigned managed identity and an X.509 certificate
D. the endpoint URI and an OAuth token
Answer: A
NEW QUESTION # 146
You need to implement a table projection to generate a physical expression of an Azure Cognitive Search index.
Which three properties should you specify in the skillset definition JSON configuration table node? Each correct answer presents part of the solution. (Choose three.) NOTE: Each correct selection is worth one point.
A. source
B. generatedKeyName
C. dataSource
D. dataSourceConnection
E. tableName
Answer: A,B,E
Explanation:
Explanation
Defining a table projection.
Each table requires three properties:
tableName: The name of the table in Azure Storage.
generatedKeyName: The column name for the key that uniquely identifies this row.
source: The node from the enrichment tree you are sourcing your enrichments from. This node is usually the output of a shaper, but could be the output of any of the skills.
Reference: https://docs.microsoft.com/en-us ... projection-overview
NEW QUESTION # 147
You have an Azure subscription that contains a Language service resource named ta1 and a virtual network named vnet1. You need to ensure that only resources in vnet1 can access ta1. What should you configure?
A. a Language service container for ta1
B. Azure Firewall for vnet1
C. the virtual network settings for ta 1
D. a network security group (NSG) for vnet1
Answer: C
Explanation:
* To restrict access so that only resources in vnet1 can access the Language service:
* You configure the virtual network settings on the Language service resource.
* This enables private endpoint integration with the VNet.
* NSGs and Azure Firewall apply to subnets/resources but do not configure the service resource itself.
* Containers can run Language service locally but are not needed here.
The answer: C
Reference: Restrict access to Cognitive Services by virtual network
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