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| Section | Weight | Objectives |
|---|---|---|
| Implement text and speech analysis solutions | 10–15% | - Implement speech capabilities
|
| Implement generative AI and agentic solutions | 30–35% | - Design and implement intelligent agents
|
| Plan and manage Azure AI solutions | 25–30% | - Manage AI solution development lifecycle
|
| Implement computer vision solutions | 10–15% | - Implement image analysis and processing
|
| Implement information extraction and knowledge mining | 10–15% | - Extract structured data from documents
|
>> Free AI-103 Exam Questions <<
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NEW QUESTION # 52
In Azure AI Studio, you use Completions playground with the GPT-35 Turbo model.
You have a prompt that contains the following code.
function F(n)
{
var f = [0, 1];
for (var i = 2; i < n; i++) f[i] = f[i-1] + f[i-2];
return f;
}
You need the model to create an explanation of the code. The solution must minimize costs.
What should you do?
Answer: A
Explanation:
By adding a comment like // what does function F do? to the prompt, you are instructing the GPT model to provide an explanation of the function's behavior. This approach directly asks the model to explain the code, which will help generate an explanation without requiring changes to the model or additional resource-intensive configurations.
NEW QUESTION # 53
Hotspot Question
You plan to create a Microsoft Foundry project named Project1 that will contain an agent and use an Azure key vault named KV1.
You need to configure a connection from Project1 to KV1.
How should you complete the Bicep code? To answer, select the appropriate options in the answer area?
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
Box 1: AzureKeyVault
To configure a connection from a Microsoft Foundry project to an Azure Key Vault, you must use the AzureKeyVault connection category.
Box 2: AccountManagedIdentity
To connect a Microsoft Foundry project to an Azure Key Vault, you must use AccountManagedIdentity as the authType.
Microsoft Foundry secures its outbound connections to core Azure storage and security infrastructure using Microsoft Entra ID role-based access control (RBAC). Using AccountManagedIdentity completely eliminates the need to handle or rotate explicit credentials or api keys within your configuration.
Reference:
https://learn.microsoft.com/en-us/azure/foundry/how-to/set-up-key-vault-connection
NEW QUESTION # 54
You have a customer support agent built by using the Microsoft Foundry Agent Service. The agent calls an Azure OpenAI model deployment.
During load testing, calls intermittently fail and return an HTTP 429 rate limit exceeded error.
You need to handle throttling to reduce call failures and improve reliability under load. The solution must remain within the service and model limits.
What should you do?
Answer: D
Explanation:
To handle HTTP 429 throttling and improve load testing reliability for your Microsoft Foundry Agent Service, implement exponential backoff with jitter on the client side, deploy a load- balancing gateway across multiple Azure OpenAI regions, or upgrade your deployment to Provisioned Throughput Units (PTU).
*-> 1. Implement Client-Side Retry LogicConfigure your agent's HTTP client or SDK to handle
429 errors gracefully rather than failing immediately.
Exponential Backoff: Increase the wait time between subsequent retry attempts exponentially .
Jitter: Add a small amount of random delay (jitter) to the backoff time to prevent a "thundering herd" effect where all throttled requests retry simultaneously.
Header Inspection: Programmatically read the Retry-After or x-ratelimit-reset values from the HTTP 429 response headers to pause execution for the exact duration requested by Azure.
2. Set Up Multi-Region Load Balancing
3. Switch to Provisioned Throughput (PTU)
4. Optimize Token Consumption
Reference:
https://learn.microsoft.com/en-us/answers/questions/1518859/help-with-resolving-ptu-m-service-429-error
NEW QUESTION # 55
You have a Microsoft Foundry project that contains an agent. The agent uses Azure AI Search for Retrieval Augmented Generation (RAG). You plan to ingest and index PDF product manuals. You need to build a solution that supports semantic similarity matching. The solution must ensure that the agent retrieves relevant data when user questions use different wording than the product manuals.
Answer: B
Explanation:
Use vector search because it performs similarity matching based on semantic meaning rather than requiring literal keyword overlap. During ingestion, the product manuals should be divided into appropriately sized chunks. An embedding model converts each chunk into a numerical vector, which Azure AI Search stores in a vector field. At query time, the user's question is also converted into an embedding, and the service retrieves chunks whose vectors are closest to the query vector. Consequently, wording such as "reset the device to factory settings" can match manual content such as "restore the unit's original configuration," despite minimal lexical overlap. Azure AI Search explicitly defines vector fields as enabling similarity searches over mathematically represented content and supports this retrieval pattern for RAG applications.
Semantic ranking improves the ordering of an existing result set but is not the indexing mechanism that stores embeddings and performs nearest-neighbor similarity matching. Suggesters provide search-as-you-type functionality, while analyzers control tokenization and lexical processing for text fields.
This aligns with the AI-103 Study Guide objectives for implementing RAG, configuring Azure AI Search grounding, generating embeddings, and selecting vector retrieval techniques for agentic solutions.
NEW QUESTION # 56
You have a Microsoft Foundry project named Project1 that contains the following:
- An OpenAPI tool that calls an external API
- A project connection named Connection1 that stores the API key of the external API When an agent calls the OpenAPI tool, the API returns a 401 unauthorized error, and traces show that the API key header is NOT being sent.
You need to ensure that the OpenAPI tool automatically includes the API key from Connection1 on all requests.
What should you do?
Answer: A
Explanation:
To make sure that the OpenAPI tool automatically includes the API key from the connection on all requests, you should explicitly define the security schemes and security requirements in your OpenAPI specification file.
Incorrect:
[Not D]
Even if you successfully link the tool to the custom connection in the portal UI, the Microsoft Foundry agent service will intentionally redact or withhold the API key if it cannot find a corresponding matching structure in the tool's code definition.
Reference:
https://learn.microsoft.com/en-us/azure/foundry/agents/how-to/tools/openapi
NEW QUESTION # 57
......
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