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| Section | Objectives |
|---|---|
| Plan and manage Azure AI solutions | - Select appropriate Azure AI services - Monitor and optimize AI solutions - Plan security and compliance requirements |
| Implement and monitor AI workloads | - Monitor performance and troubleshoot issues - Deploy AI models and services |
| Implement Azure AI solutions | - Implement computer vision solutions - Implement knowledge mining with Azure AI Search - Implement generative AI solutions using Azure OpenAI - Implement natural language processing solutions |
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NEW QUESTION # 78
A customer support bot built with Azure AI Foundry Agent Service must ground its answers strictly in an internal knowledge base and avoid hallucinating unsupported facts.
What should you implement?
Answer: C
Explanation:
Connect the agent to an Azure AI Search knowledge source and explicitly constrain its instructions to answer from retrieved evidence. Microsoft Foundry Agent Service can use Azure AI Search to retrieve proprietary organizational content and ground model responses in that content. Retrieved documents provide the factual context from which the model generates its answer, and Azure AI Search can return source references for attribution.
Microsoft ' s RAG guidance also specifically recommends using clear system instructions that require the model to stay within retrieved content when hallucination risk must be reduced. If the available evidence does not support an answer, the instructions should direct the agent to state that the information is unavailable rather than inventing facts. Retrieval grounding substantially reduces unsupported generation, although production systems should still evaluate outputs because grounding alone does not mathematically guarantee zero hallucinations.
Increasing temperature would generally increase output variability and is contrary to the objective. A larger context window without retrieval does not give the model access to the organization ' s internal knowledge base. Disabling content filtering weakens safety controls and has no role in factual grounding.
Study Guide references: Microsoft Foundry Agent Service # Azure AI Search tool; RAG grounding; knowledge sources; source attribution; grounding instructions.
NEW QUESTION # 79
You are developing a Java application to be deployed in Azure. The application stores sensitive data in Azure Cosmos DB.
You need to configure Always Encrypted to encrypt the sensitive data inside the application.
What should you do first?
Answer: A
Explanation:
Detailed Explanation: For Cosmos DB Always Encrypted, the key hierarchy starts with a customer-managed key held in Azure Key Vault. The data encryption key used by the client is protected by that customer- managed key. Microsoft's setup sequence therefore begins by preparing Key Vault and the CMK before creating DEKs or an encryption policy. Creating a managed identity, a container policy, or a DEK can be necessary later, but none precedes establishing the CMK that protects the client-side encryption key material.
Study Guide Alignment: AI data-management workloads: Cosmos DB, PostgreSQL, caching, vector storage, vector retrieval, consistency, and connection optimization.
Official Microsoft Learn References: AI-200 Study Guide | Always Encrypted for Azure Cosmos DB
NEW QUESTION # 80
You are implementing the expiration requirement for AI-generated summaries in the Redis cache.
You need to purge the AI summaries five minutes after creation, regardless of how often they are accessed.
What should you do?
Answer: B
Explanation:
TTL (Time-To-Live) with a fixed duration of five minutes is the exact Redis feature you need to purge AI-generated summaries regardless of access frequency.
Redis TTL and Expiration Features
Core Expiration Feature
EXPIRE command: Sets a timeout in seconds on a key. Use EXPIRE key 300 to remove the summary after 300 seconds (five minutes).
PX command: Sets a timeout in milliseconds. Use PX key 300000 for precise millisecond control.
EXPIREAT / PEXPIREAT: Sets an absolute Unix timestamp for expiration instead of a relative duration.
Reference:
https://dev.to/truongpx396/30-common-redis-interview-questions-1ai
NEW QUESTION # 81
You are deploying a model in Azure AI Foundry and must ensure the endpoint can handle unpredictable bursts of traffic while keeping cost low during idle periods.
Which deployment type should you choose?
Answer: D
Explanation:
Choose a Standard pay-as-you-go deployment . Microsoft Foundry documentation identifies Standard deployments as appropriate for low-to-medium volume workloads with high burstiness . Standard uses pay-per-token billing , so charges are based on actual inference consumption rather than continuously reserved model capacity. This makes it well suited to workloads whose traffic varies significantly and may have long idle periods.
By contrast, Provisioned Throughput Units (PTUs) reserve model-processing capacity and are designed for sustained, predictable high-throughput workloads where consistent latency is important. Microsoft states that PTUs are billed based on deployed capacity even when that capacity is not actively serving requests.
Consequently, PTU deployments can be economically inefficient when the endpoint is frequently idle or traffic is highly variable.
A Batch deployment is optimized for asynchronous, non-latency-sensitive processing and is not appropriate for an interactive endpoint receiving unpredictable live traffic. Dedicated managed compute with fixed capacity introduces continuously allocated infrastructure and therefore does not align with the requirement to minimize idle-period cost.
Microsoft also recommends Standard or Global Standard for variable, bursty traffic , while provisioned types are recommended for consistent high-volume workloads.
Study Guide references: Microsoft Foundry Models # Standard deployments; pay-per-token billing; bursty workloads; provisioned throughput; batch processing.
NEW QUESTION # 82
A company is implementing a publish-subscribe (Pub/Sub) messaging component by using Azure Service Bus. You are developing the first subscription application.
In the Azure portal you see that messages are being sent to the subscription for each topic. You create and initialize a subscription client object by supplying the correct details, but the subscription application is still not consuming the messages.
You need to ensure that the subscription client processes all messages.
Which code segment should you use?
Answer: C
Explanation:
Detailed Explanation: Creating a subscription client establishes the client object but does not start application-level message processing. The message handler must be registered so received subscription messages are delivered to the application callback. Adding a default TrueFilter is only necessary when subscription rules require adjustment and does not start the receiver; the default subscription rule already accepts messages in a normal subscription. Closing the client would stop processing rather than enable it.
Study Guide Alignment: Azure service integration: Service Bus, Event Grid, Azure Functions triggers
/bindings, and event-driven processing.
Official Microsoft Learn References: AI-200 Study Guide | Service Bus message transfers, locks, and settlement
NEW QUESTION # 83
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